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Journal articles on the topic 'Intruder detection systems'
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Shevantikar, Pradnesh, Avinash Udata, and Abhishek Korachagao. "Laser-Enabled Intruder Detection System." Journal of Electrical Engineering and Electronics Design 2, no. 1 (2024): 1–4. http://dx.doi.org/10.48001/joeeed.2024.211-4.
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Laser-enabled intruder detection systems (LIDs) are a type of intrusion detection system (IDS) that utilizes laser beams to detect the presence of intruders in a protected area. LIDs can be used to protect a wide variety of assets, including homes, businesses, and government facilities. They are particularly effective in areas where traditional security measures, such as motion sensors and cameras, are not feasible or effective. LIDs work by creating a virtual barrier around a protected area. When an intruder breaks this barrier, the laser beam is interrupted, and an alarm is triggered. LIDs can be customized to detect a wide variety of intruders, including humans, animals, and vehicles. They can also be configured to detect intruders at a variety of distances, from a few feet to several hundred feet. LIDs are a relatively new technology, but they have already been proven to be an effective way to deter and detect intruders. They are becoming increasingly popular as a security measure for both residential and commercial properties.
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Shim, Jaeseok, and Yujin Lim. "WSN-Based Height Estimation of Moving Object in Surveillance Systems." Mobile Information Systems 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/2127593.
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In the WSN- (wireless sensor network-) based surveillance system to detect undesired intrusion, all detected objects are not intruders. In order to reduce false alarms, human detection mechanism needs to determine if the detected object is a human. For human detection, physical characteristics of human are usually used. In this paper, we use the physical height to differentiate an intruder from detected objects. Using the measured information from sensors, we estimate the height of the detected object. Based on the height, if the detected object is decided as an intruder, an alarm is given to a control center. The experimental results indicate that our mechanism correctly and fast estimates the height of the object without complex computation.
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AM, Bharath, V. Dhananjaya, Bharath, and S. Nithyanandhan. "IoT Based Thermal Survillance and Security System." IJISE 1, no. 2 (2019): 93–98. https://doi.org/10.5281/zenodo.2794156.
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Security is the need in day to day life. Use of intruder detection systems were increasing year by year. IR surveillance method for the detection of illegal intruder in low light condition is more easy unlike optical camera systems. This paper presents the design and implementation of intruder detection using IR sensor based system for application in the field of security and surveillance. Presenting methods are based on use of IR thermal detection system for the intruder detection, recognition and alerting. The use of such systems has special significance in the context of security in the domain of timely detection of abnormal movements inside the buildings. IR thermal imaging has higher advantages compared to optical camera surveillance systems because thermal imaging is effected by to weather conditions and can be used any time in a day. Presenting system are mainly used for illegal intruder detection inside buildings as well as infrared thermal imagers mounted on autonomous vehicles and unmanned aerial vehicles (UAV) and Banking Security Systems for intruder detection. Thermal sensors are used to detect the hot body by IR radiation detection. Thermal sensors are combined with microwave motion detection sensors to detect abnormal movement of hot bodies. Motion sensors also helps in prevention of false alarm due to external environmental condition. After detection of intruder alert the user using IoT systems.
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Jinisha, J. Josephin, and S. Jerine. "Intrusion Detection Mechanism for Empowered Intruders Using IDEI." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 186–93. http://dx.doi.org/10.17762/ijritcc.v11i8s.7189.
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In the past, intrusion detection has been extensively investigated as a means of ensuring the security of wireless sensor networks. Anti-recon technology has made it possible for an attacker to get knowledge about the detecting nodes and plot a route around them in order to evade detection. An "empowered intruder" is one who poses new threats to current intrusion detection technologies. Furthermore, the intended impact of detection may not be obtained in certain subareas owing to gaps in coverage caused by the initial deployment of detection nodes at random. A vehicle collaboration sensing network model is proposed to solve these difficulties, in which mobile sensing cars and static sensor nodes work together to identify intrusions by empowered intruders. An algorithm for mobile sensing vehicles, called Intrusion Detection Mechanism for Empowered Intruders(IDEI), and a sleep-scheduling technique for static nodes form the basis of our proposal. Sophisticated intruders will be tracked by mobile sensors, which will fill in the gaps in coverage, while static nodes follow a sleep schedule and will be woken when the intruder is discovered close. Our solution is compared to current techniques like Kinetic Theory Based Mobile Sensor Network (KMsn)and Mean Time to Attacks (MTTA) in terms of intrusion detection performance, energy usage, and sensor node movement distance. IDEI's parameter sensitivity is also examined via comprehensive simulations. It is clear from the theoretical analysis and simulation findings that our idea is more efficient and available.
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Rzucidło, Paweł, Grzegorz Jaromi, Tomasz Kapuściński, Damian Kordos, Tomasz Rogalski, and Piotr Szczerba. "In-Flight Tests of Intruder Detection Vision System." Sensors 21, no. 21 (2021): 7360. http://dx.doi.org/10.3390/s21217360.
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In the near future, the integration of manned and unmanned aerial vehicles into the common airspace will proceed. The changes taking place mean that the safety of light aircraft, ultralight aircraft and unmanned air vehicles (UAV) will become an increasing problem. The IDAAS project (Intruder Detection And collision Avoidance System) meets the new challenges as it aims to produce technically advanced detection and collision avoidance systems for light and unmanned aerial vehicles. The work discusses selected elements of research and practical tests of the intruder detection vision system, which is part the of IDAAS project. At the outset, the current formal requirements related to the necessity of installing anticollision systems on aircraft are presented. The concept of the IDAAS system and the structure of algorithms related to image processing are also discussed. The main part of the work presents the methodology developed for the needs of dedicated flight tests, its implementation and the results obtained. The initial tests of the IDAAS system carried out on an ultralight aircraft generally indicate the possibility of the effective detection of intruders in the airspace with the use of vision methods, although they also indicated the existence of conditions in which this detection may prove difficult or even impossible.
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Oladunjoye John Abiodun and Okwori Anthony Okpe. "Smart Home Security using Arduino-based Internet of Things (IoTs) Intrusion Detection System." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 857–64. http://dx.doi.org/10.30574/wjarr.2024.22.3.2000.
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Home intrusion detection systems (IDS) have become increasingly important in our modern community due to their high performance in adequately securing the home environment. Nowadays, there is an incessant increase in the number of theft cases hence a need for an intrusion detection system to detect any intruder around our homes. The aim of This paper is to develop a Smart Home Security using an Arduino-based Internet of Things (IoT) Intrusion Detection System. This security system is built using Arduino Uno, ultrasonic sensor and GSM module for efficient monitoring of intruders and sending of SMS alerts to the homeowners at a point of intrusion which is made possible through the Internet of Things network. The Internet of Things is simply a type of network used to connect anything with the Internet based on stipulated protocols through information sensing equipment to conduct information exchange and communications in order to achieve smart recognitions, positioning, tracing, monitoring, and or administration. The proposed system uses an Arduino microcontroller programed to coordinate the activities of the various components connected together for efficient communication using C++ programming language through the Arduino Integrated Development Environment (IDE). The system is simple and highly efficient as it is capable of detecting any intruder within the monitoring environment and triggering an SMS alert once an intruder is found.
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Oladunjoye, John Abiodun, and Anthony Okpe Okwori. "Smart Home Security using Arduino-based Internet of Things (IoTs) Intrusion Detection System." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 857–64. https://doi.org/10.5281/zenodo.14738955.
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Home intrusion detection systems (IDS) have become increasingly important in our modern community due to their high performance in adequately securing the home environment. Nowadays, there is an incessant increase in the number of theft cases hence a need for an intrusion detection system to detect any intruder around our homes. The aim of This paper is to develop a Smart Home Security using an Arduino-based Internet of Things (IoT) Intrusion Detection System. This security system is built using Arduino Uno, ultrasonic sensor and GSM module for efficient monitoring of intruders and sending of SMS alerts to the homeowners at a point of intrusion which is made possible through the Internet of Things network. The Internet of Things is simply a type of network used to connect anything with the Internet based on stipulated protocols through information sensing equipment to conduct information exchange and communications in order to achieve smart recognitions, positioning, tracing, monitoring, and or administration. The proposed system uses an Arduino microcontroller programed to coordinate the activities of the various components connected together for efficient communication using C++ programming language through the Arduino Integrated Development Environment (IDE). The system is simple and highly efficient as it is capable of detecting any intruder within the monitoring environment and triggering an SMS alert once an intruder is found.
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Peňaška, Michal, and Milan Kutaj. "EXPERIMENTAL TESTING OF PASSIVE INFRARED DETECTORS AND EXAMINING THE PROBABILITY OF INTRUDER DETECTION." CBU International Conference Proceedings 6 (September 25, 2018): 1139–43. http://dx.doi.org/10.12955/cbup.v6.1306.
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The article describes intruder alarm systems, experimental testing of selected components of these systems, and examining the probability of intruder detection in a protected area. Component parameters are specified in the producer technical documentation (datasheets, service manuals) and they have to comply with European technical standards. Specifically, the article focuses on passive infrared motion detectors, which are among the most used components of intruder alarm systems. Experiments were carried out under the conditions specified in the European technical standards and also beyond these conditions. The experiments were performed in controlled measuring and testing laboratories. The results of the experiments can be used as a basis for improving the European technical standards, a proposal for their modification, and also for the evaluation of intruder alarm systems.
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Rzucidło, Paweł, Tomasz Rogalski, Grzegorz Jaromi, Damian Kordos, Piotr Szczerba, and Andrzej Paw. "Simulation studies of a vision intruder detection system." Aircraft Engineering and Aerospace Technology 92, no. 4 (2020): 621–31. http://dx.doi.org/10.1108/aeat-04-2019-0065.
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Purpose The purpose of this paper is to describe simulation research carried out for the needs of multi-sensor anti-collision system for light aircraft and unmanned aerial vehicles. Design/methodology/approach This paper presents an analysis related to the practical possibilities of detecting intruders in the air space with the use of optoelectronic sensors. The theoretical part determines the influence of the angle of view, distance from the intruder and the resolution of the camera on the ability to detect objects with different linear dimensions. It has been assumed that the detection will be effective for objects represented by at least four pixels (arranged in a line) on the sensor matrix. In the main part devoted to simulation studies, the theoretical data was compared to the obtained intruders’ images. The verified simulation environment was then applied to the image processing algorithms developed for the anti-collision system. Findings A simulation environment was obtained enabling reliable tests of the anti-collision system using optoelectronic sensors. Practical implications The integration of unmanned aircraft operations in civil airspace is a serious problem on a global scale. Equipping aircraft with autonomous anti-collision systems can help solve key problems. The use of simulation techniques in the process of testing anti-collision systems allows the implementation of test scenarios that may be burdened with too much risk in real flights. Social implications This paper aims for possible improvement of safety in light-sport aviation. Originality/value This paper conducts verification of classic flight simulator software suitability for carrying out anti-collision systems tests and development of a flight simulator platform dedicated to such tests.
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Naing, Thiri, Kyi Kyi Khaing, and Tin Tin Nwet. "Perimeter intrusion detective system using arduino." APTIKOM Journal on Computer Science and Information Technologies 4, no. 3 (2020): 135–40. http://dx.doi.org/10.34306/csit.v4i3.101.
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Perimeter intrusion detection systems are an integral part of most physical security systems. The mainfocus of the Perimeter Intrusion Detection System (PID) is on the fence intrusion. To secure public and private placessuch as military bases, airports, power station and security related application, the used of perimeter fencing iswidely applicable for isolating and protecting. Fence structures merely prevent a percentage of intrusions orpostpone them. Therefore, to monitor and investigate activities on or around the university, a higher level of securityis needed. The system used major components as Arduino board, 8x8 LED display, ultrasonic sensor, 16x2 LCDdisplay module and speaker. The unauthorized person who tries to intrude the university woulld be sensed, detectedand alarm would generate a signal that an intruder was trying to enter the university. The sound level depended onthe distance, the nearer the intruder and stronger would be the alarm signal. PID system displayed the distance of theobject or personal found in its region on the LCD display. This system was very useful for security applications.Ultrasonic sensor would be searching if there was a motion in its range.
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Khaing, Kyi Kyi, Kyi Kyi Khaing, and Tin Tin Nwet. "Perimeter Intrusion Detective System using Arduino." APTIKOM Journal on Computer Science and Information Technologies 4, no. 3 (2019): 135–40. http://dx.doi.org/10.11591/aptikom.j.csit.154.
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Perimeter intrusion detection systems are an integral part of most physical security systems. Perimeter fencing is widely used to isolate and protect public and private places such as airports, military bases, power stations and security related applications. Fence structures merely prevent a percentage of intrusions or postpone them. A higher level of security needed to monitor and investigate activities on or around the university. Perimeter Intrusion Detection System (PID) focusing on the fence intrusions. The system used major components as Arduino board, 8x8 LED display, ultrasonic sensor, 16x2 LCD display module and speaker. The unauthorized person who tries to intrude the university would be sensed, detected and alarm would generate a signal that an intruder was trying to enter the university. The sound level depended on the distance, the nearer the intruder and stronger would be the alarm signal. PID system displayed the distance of the object or personal found in its region on the LCD display. This system was very useful for security applications. Ultrasonic sensor would be searching if there was a motion in its range.
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Mala, V., and K. Meena. "Hybrid classification model to detect advanced intrusions using data mining techniques." International Journal of Engineering & Technology 7, no. 2.4 (2018): 10. http://dx.doi.org/10.14419/ijet.v7i2.4.10031.
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Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.
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Forlenza, Lidia, Giancarmine Fasano, Domenico Accardo, and Antonio Moccia. "Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems." International Journal of Aerospace Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/542165.
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This paper is focused on the development and the flight performance analysis of an image-processing technique aimed at detecting flying obstacles in airborne panchromatic images. It was developed within the framework of a research project which aims at realizing a prototypical obstacle detection and identification System, characterized by a hierarchical multisensor configuration. This configuration comprises a radar, that is, the main sensor, and four electro-optical cameras. Cameras are used as auxiliary sensors to the radar, in order to increase intruder aircraft position measurement, in terms of accuracy and data rate. The paper thoroughly describes the selection and customization of the developed image-processing techniques in order to guarantee the best results in terms of detection range, missed detection rate, and false-alarm rate. Performance is evaluated on the basis of a large amount of images gathered during flight tests with an intruder aircraft. The improvement in terms of accuracy and data rate, compared with radar-only tracking, is quantitatively demonstrated.
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Kanoje, Shubhankar, Soham Chakraborty, Jerrin Jiju Chacko, Mohit Hitendra Bisht, and Dr Shaveta Malik. "Intruder Detection System Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 774–81. http://dx.doi.org/10.22214/ijraset.2022.46288.
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In the modern era, security and surveillance are important issues. Recent acts of theft have highlighted the urgent need for efficient video surveillance and on-the-spotnotification of ongoing thefts to house owners. A number of surveillance solutions are currently available on the market for the populace, such as CCTV cameras and digital video recorders (DVRs) that can record the unauthorized activities of a trespasser, however they demand excessive amounts of investment in terms of funds and timeand cannot distinguish between authorised and non-authorised users in most cases. Also the commonly available systems are often targeted towards medium sized franchises and are out of reach of common people. The task of face detection and the recognition of an intruder become very difficult when the intruder hides their face partially or fully. Hence the following system makes use of a program to perform motion detection by analyzing the relative differences between two consecutive frames. To facilitate this process the frames are further processed using the various image processing methods for ensuring the highest possible precision while distinguishing genuine motion from ‘noise’ disturbance
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Cao, Lai Cheng, Wei Han, and Sheng Dong. "A New Intrusion Detection Method Based on Machine Learning in Mobile Ad Hoc NETwork." Applied Mechanics and Materials 548-549 (April 2014): 1304–10. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1304.
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In a Mobile Ad hoc NETwork (MANET), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder (s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. Unfortunately, many anomaly intrusion detection systems (IDS) take on higher false alarm rate (FAR) and false negative rate (FNR). In this paper, we propose and implement a new intrusion-detection system using Adaboost, a prevailing machine learning algorithm, and its detecting model adopts a dynamic load-balancing algorithm, which can avoid packet loss and false negatives in high-performance severs with handling heavy traffic loads in real-time and can enhance the efficiency of detecting work. Compared to contemporary approaches, our system demonstrates an especially low false positive rate and false negative rate in certain circumstances while does not greatly affect the network performance.
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Vasuki, P., Sesu Priya A, and Soundarya R. "A Smart Watchdog - Intruder Detection System." International Journal of Engineering & Technology 7, no. 3.34 (2018): 231. http://dx.doi.org/10.14419/ijet.v7i3.34.18971.
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In todays world, Security is a matter of great concern. Security controls play a vital role in protecting resources from espionage, sabotage, damage and theft. Our proposed system is to develop a security system with improved facilities, which tries to eliminate the limitations posed by the existing security systems. The current manual security system depends mostly on human involvement, which is prone to error, and the security is concentrated only at the front door which requires subjects cooperation. To solve these issues we have proposed a Smart Watchdog System. The system watches the environment, and if there is a human activity, the system captures it. The system automatically detects faces of the individual from the activity using firmware. We have planned to maintain the database of authorised inmates and workers of a place and verifies of every individual arriver. This feature enables the system to automatically recognises the unauthorised users and gives an alert when it encounters entry of unauthorised users even without the human assistance. The system also detects the unauthorised entry in the mass. The entire system is planned to be ported to Raspberry-Pi based Embedded System supported with DC power back up. This method can be employed in ladies hostels as well as to the secured places like the data centre, atomic research centre and military where the unauthorised entry is restricted.
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Zaiets, I., V. Brydinskyi, D. Sabodashko, Yu Khoma, Kh Ruda, and M. Shved. "UTILIZATION OF VOICE EMBEDDINGS IN INTEGRATED SYSTEMS FOR SPEAKER DIARIZATION AND MALICIOUS ACTOR DETECTION." Computer systems and network 6, no. 1 (2024): 54–66. http://dx.doi.org/10.23939/csn2024.01.054.
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This paper explores the use of diarization systems which employ advanced machine learning algorithms for the precise detection and separation of different speakers in audio recordings for the implementation of an intruder detection system. Several state-of-the-art diarization models including Nvidia’s NeMo Pyannote and SpeechBrain are compared. The performance of these models is evaluated using typical metrics used for the diarization systems such as diarization error rate (DER) and Jaccard error rate (JER). The diarization system was tested on various audio conditions including noisy environment clean environment small number of speakers and large number of speakers. The findings reveal that Pyannote delivers superior performance in terms of diarization accuracy and thus was used for implementation of the intruder detection system. This system was further evaluated on a custom dataset based on Ukrainian podcasts and it was found that the system performed with 100% recall and 93.75% precision meaning that the system has not missed any criminal from the dataset but could sometimes falsely detect a non-criminal as a criminal. This system proves to be effective and flexible in intruder detection tasks in audio files with different file sizes and different numbers of speakers which are present in these audio files. Keywords: deep learning diarization speaker embeddings speaker recognition cyber security.
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Shree Goyal, Sangya Medhavi, Tanish Mathur, Shwetha S, and Sharadadevi K S. "Secure Authentication Process with Intruder Detection and Reporting Mechanism." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26213.
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Abstract—The implementation of robust authentication mechanisms is essential for ensuring the security of sensitive systems and preventing unauthorized access. This research paper presents a novel approach to enhance the authentication process by incorporating an intruder detection and reporting mechanism. The proposed system captures the image of an individual attempting unauthorized access after multiple failed login attempts and sends it to the administrator using the Twilio platform. Additionally, the system tracks the IP address of the intruder and provides geolocation information to aid in identifying the potential threat. The integration of these security measures enhances the overall security posture and aids in timely response and mitigation against potential threats. Keywords—Authentication mechanisms, Security, Unauthorized access, Intruder detection, Reporting mechanism, Image capture, Twilio platform, IP address tracking, Geolocation information, Security measures, Timely response, Mitigation, and Potential threats
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Nirmala, P., T. Manimegalai, J. R. Arunkumar, S. Vimala, G. Vinoth Rajkumar, and Raja Raju. "A Mechanism for Detecting the Intruder in the Network through a Stacking Dilated CNN Model." Wireless Communications and Mobile Computing 2022 (April 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/1955009.
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Rapid advancements in the technology and telecommunication areas have led to a massive expansion of network density and information. As a consequence, numerous intruder assaults are being attempted, making it difficult for cybersecurity to identify intruders effectively. The increasing amount of network traffic data has poses a major problem for conventional intrusion detection systems. Moreover, intruders with the intent of launching various assaults inside the networks could not be overlooked. The classification in the article is based on the DL methodologies used in constructing network-based IDS technologies, and it first describes the idea of intrusion detection system. The effectiveness of extracted features and classifications is closely related to detection accuracy, yet typical extraction of features and classification techniques do not function well in this situation. Basic traffic data is also uneven that has a significant effect on classifications findings. A novel intrusion detection model using stacked dilated convolutional autoencoders is proposed and tests it on two additional intrusion detection databases. Many tests have been conducted to define the effectiveness of the strategy. The use of the concept in extensive and practical network systems has a lot of potentiality and possibility. The CTU-UNB database as well as CTU-UNB database is made up of trafficking data from multiple sources. The suggested efficiency of the algorithm is used to evaluate, three types of classification. The deep learning strategy is compared to other ways that were similar. The implications of a number of key hyperparameters are investigated further. The comparison experimental findings show that the suggested approach can reach significantly high efficiency, fulfilling the needs for network intrusion detection systems (NIDS) with higher accuracy.
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Ben-Zvi, T., and J. V. Nickerson. "Intruder Detection: An Optimal Decision Analysis Strategy." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 2 (2012): 249–53. http://dx.doi.org/10.1109/tsmcc.2011.2126043.
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Teixidó, Pedro, Juan Antonio Gómez-Galán, Rafael Caballero, et al. "Secured Perimeter with Electromagnetic Detection and Tracking with Drone Embedded and Static Cameras." Sensors 21, no. 21 (2021): 7379. http://dx.doi.org/10.3390/s21217379.
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Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.
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Rodelas, Nelson C., and Melvin A. Ballera. "Intruder detection and recognition using different image processing techniques for a proactive surveillance." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 843. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp843-852.
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To innovate a proactive surveillance camera, there is a need for efficient face detection and recognition algorithm. The researchers used one of the ViolaJones algorithm and used different image processing techniques to recognize intruders or not. The goal of the research is to recognize the fastest way on how the homeowners will be informed if an intruder or burglar enters their home using a proactive surveillance device. This device was programmed based on the different recognition algorithms and a criteria evaluation framework that could recognize intruders and burglars and the design used was developmental research to satisfy the research problem. The researchers used the Viola-Jones algorithm for face detection and five algorithms for face recognition. The criteria evaluation was used to identify the best face recognition algorithm and was tested in a real-world situation and captured a series of images camera and processed by proactive face detection and recognition. The result shows that the system can detect and recognize intruders and proactively send a notification to the homeowners via mobile application. It is concluded that the system can recognize the intruders and proactively notify the household members using the mobile applications and activate the alarm system of the house.
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Yadav, Abhinesh Kumar, Anuj Maurya, Amit Verma, and Aman Tomar. "Facial Recognition Based Automated Door." International Research Journal of Computer Science 11, no. 01 (2024): 06–10. http://dx.doi.org/10.26562/irjcs.2024.v1101.02.
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To ensure the accuracy and efficiency of intruder identification, the proposed method is combined with Haar clssifier technology for face detection. When someone comes to the door, the Pi camera captures the image and starts the face detection process. In this research, we implement a facial recognition component to capture human images, comparing them with stored data in a database. Upon a match with an authorized individual, the system unlocks the door through an electromagnetic lock. The demand for a rapid and accurate face recognition system persists, continuously evolving to swiftly identify intruders and restrict unauthorized access to highly secure areas, thereby reducing human error. Facial recognition stands as a crucial component within secure systems, surpassing biometric pattern recognition methods, and finds widespread application across various domains.
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Wang, Lixin, Jianhua Yang, Michael Workman, and Peng-Jun Wan. "A Framework to Test Resistency of Detection Algorithms for Stepping-Stone Intrusion on Time-Jittering Manipulation." Wireless Communications and Mobile Computing 2021 (August 6, 2021): 1–8. http://dx.doi.org/10.1155/2021/1807509.
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Hackers on the Internet usually send attacking packets using compromised hosts, called stepping-stones, in order to avoid being detected and caught. With stepping-stone attacks, an intruder remotely logins these stepping-stones using programs like SSH or telnet, uses a chain of Internet hosts as relay machines, and then sends the attacking packets. A great number of detection approaches have been developed for stepping-stone intrusion (SSI) in the literature. Many of these existing detection methods worked effectively only when session manipulation by intruders is not present. When the session is manipulated by attackers, there are few known effective detection methods for SSI. It is important to know whether a detection algorithm for SSI is resistant on session manipulation by attackers. For session manipulation with chaff perturbation, software tools such as Scapy can be used to inject meaningless packets into a data stream. However, to the best of our knowledge, there are no existing effective tools or efficient algorithms to produce time-jittered network traffic that can be used to test whether an SSI detection method is resistant on intruders’ time-jittering manipulation. In this paper, we propose a framework to test resistency of detection algorithms for SSI on time-jittering manipulation. Our proposed framework can be used to test whether an existing or new SSI detection method is resistant on session manipulation by intruders with time-jittering.
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Widodo, Rio, and Imam Riadi. "Intruder Detection Systems on Computer Networks Using Host Based Intrusion Detection System Techniques." Buletin Ilmiah Sarjana Teknik Elektro 3, no. 1 (2021): 21. http://dx.doi.org/10.12928/biste.v3i1.1752.
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The openness of access to information raises various problems, including maintaining the validity and integrity of data, so a network security system is needed that can deal with potential threats that can occur quickly and accurately by utilizing an IDS (intrusion detection system). One of the IDS tools that are often used is Snort which works in real-time to monitor and detect the ongoing network by providing warnings and information on potential threats in the form of DoS attacks. DoS attacks run to exhaust the packet path by requesting packets to a target in large and continuous ways which results in increased usage of CPU (central processing unit), memory, and ethernet or WiFi networks. The snort IDS implementation can help provide accurate information on network security that you want to monitor because every communication that takes place in a network, every event that occurs and potential attacks that can paralyze the internet network are monitored by snort.
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Horielyshev, Stanislav, Pavlo Volkov, Igor Boikov, et al. "Study of the secondary characteristics of the bistatic scattering of a combined object in a covert radar surveillance system." EUREKA: Physics and Engineering, no. 4 (July 30, 2022): 137–51. http://dx.doi.org/10.21303/2461-4262.2022.002493.
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The emergence of new means of attack, reconnaissance and methods of sabotage imposes special requirements on the technical means of protecting important state facilities (ISF). Modern trends in the construction of ISF physical protection systems are the integration of engineering barriers, perimeter signaling and detection tools. Detection tools should provide covert receipt of information about the approach of the intruder in "distant" intrigues. To do this, it is possible to use technical means built on the principle of semi-active bistatic radar with an external illumination source. However, in order to identify intruders in the ISF protection zone, it is necessary to have a priori information about the radar visibility of the combined location objects. The combined object is typically a complex object having both metallic and dielectric elements.
 To this end, a technique has been developed for estimating the radar cross-section (RCS) of combined objects in the field of external illumination. The electromagnetic field (EMF) scattered by a combined object in the meter and decimeter wavelength ranges is calculated as a coherent sum of fields, taking into account their phase, scattered by its metal and dielectric elements. This made it possible to take into account the electromagnetic interaction of the elements of the combined object. The method of integral equations (IE) was used to find the current density and magnetic field strength.
 The scatter diagrams of the person-intruder, the person-intruder in personal armor protection (PAP) under different conditions of irradiation and reception and illumination frequencies are obtained and analyzed. This made it possible to evaluate the effect of metallic elements on the scatter diagram of the combined object.
 The obtained a priori information is of significant practical importance at the stage of optimizing signal processing algorithms and designing new means of covert detection
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Horielyshev, Stanislav, Pavlo Volkov, Igor Boikov, et al. "Study of the secondary characteristics of the bistatic scattering of a combined object in a covert radar surveillance system." EUREKA: Physics and Engineering, no. 4 (July 30, 2022): 137–51. https://doi.org/10.21303/2461-4262.2022.002493.
Full textAbstract:
The emergence of new means of attack, reconnaissance and methods of sabotage imposes special requirements on the technical means of protecting important state facilities (ISF). Modern trends in the construction of ISF physical protection systems are the integration of engineering barriers, perimeter signaling and detection tools. Detection tools should provide covert receipt of information about the approach of the intruder in "distant" intrigues. To do this, it is possible to use technical means built on the principle of semi-active bistatic radar with an external illumination source. However, in order to identify intruders in the ISF protection zone, it is necessary to have a priori information about the radar visibility of the combined location objects. The combined object is typically a complex object having both metallic and dielectric elements. To this end, a technique has been developed for estimating the radar cross-section (RCS) of combined objects in the field of external illumination. The electromagnetic field (EMF) scattered by a combined object in the meter and decimeter wavelength ranges is calculated as a coherent sum of fields, taking into account their phase, scattered by its metal and dielectric elements. This made it possible to take into account the electromagnetic interaction of the elements of the combined object. The method of integral equations (IE) was used to find the current density and magnetic field strength. The scatter diagrams of the person-intruder, the person-intruder in personal armor protection (PAP) under different conditions of irradiation and reception and illumination frequencies are obtained and analyzed. This made it possible to evaluate the effect of metallic elements on the scatter diagram of the combined object. The obtained a priori information is of significant practical importance at the stage of optimizing signal processing algorithms and designing new means of covert detection
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Ahanger, Tariq Ahamed, Usman Tariq, Atef Ibrahim, Imdad Ullah, and Yassine Bouteraa. "IoT-Inspired Framework of Intruder Detection for Smart Home Security Systems." Electronics 9, no. 9 (2020): 1361. http://dx.doi.org/10.3390/electronics9091361.
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The proliferation of IoT devices has led to the development of smart appliances, gadgets, and instruments to realize a significant vision of a smart home. Conspicuously, this paper presents an intelligent framework of a foot-mat-based intruder-monitoring and detection system for a home-based security system. The presented approach incorporates fog computing technology for analysis of foot pressure, size, and movement in real time to detect personnel identity. The task of prediction is realized by the predictive learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) through which the proposed model can estimate the possibility of an intruder. In addition to this, the presented approach is designed to generate a warning and emergency alert signals for real-time indications. The presented framework is validated in a smart home scenario database, obtained from an online repository comprising 49,695 datasets. Enhanced performance was registered for the proposed framework in comparison to different state-of-the-art prediction models. In particular, the presented model outperformed other models by obtaining efficient values of temporal delay, statistical performance, reliability, and stability.
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Lan, Chien-Wu, and Chi-Yao Chang. "Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags." Applied Sciences 10, no. 3 (2020): 974. http://dx.doi.org/10.3390/app10030974.
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Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.
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Keerthanna, G. S., Kumar P. Praveen, and Prasad K. |. Ms. S. Sri Heera Vishnu. "A Modern Technique for Unauthorized Human Detection and Intimation." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 170–74. https://doi.org/10.31142/ijtsrd21659.
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Technological advancements are inevitable and the field of IoT is no exception. The utilization of the technologies in various sectors is highly employed. Even though we use technology in various sectors, the employment of technology for security purposes is very low. The Existing security in various places only CCTV is used for monitoring and recording. Even there are existing security systems where an alert is sent via an email which requires a stable internet connection. It is unlikely that we expect the user to be always connected to an internet source. In the Proposed system, authorized users faces will be trained and stored in a Database. Initially, when an unknown known person enters in the zone the camera module will capture the intruders face. The captured intruder's face will be compared with the trained faces in the database. If the person's face doesn't match, the micro controller will send an alert SMS to the recognized user and also the intruder's captured image will be E mailed to the user. The authorized user should acknowledge the SMS message. If he fails to acknowledge the message within a threshold time limit, an alert call will be made to the concerned user. By this the user gets intimated in real time. Keerthanna G. S | Praveen Kumar P | Vishnu Prasad K | Ms. S. Sri Heera "A Modern Technique for Unauthorized Human Detection and Intimation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21659.pdf
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reddy, N. Ajay kumar, T. Ajay kumar, Mohammad Usman, and U. Soma Naidu. "IoT-Driven Smart Home Security with Voice Commands." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40274.
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Home security systems are really important to provide personal comfort and protection for goods, especially in the digital world that is evolving at an increasingly high speed. When humans come together with the Internet of Things (IoT) and smart homes, very efficient systems can be created. The integration sows convenience, efficiency,security, and responsiveness for installation which includes intelligence of automation in it. We are studying an IoT-based home safety system using passive infrared (PIR) sensors, working on a better version by focusing on user interfaces—interfaces with PIRs, secure voice controls for automation based on various communication protocols. This includes provisions for lighting, and an alarm system for movement [sic]. It tests the voice commands and PIR sensor-based intruder detection. The method of the research comprises bright interfe rence control and PIR intruder detection, coupled with a variable time-delay for response identification. The study's results are presented in terms of the mean actual response time, distinguishing between performance over Wi-Fi and fourth- and fifth- generation mobile networks. DISCUSS AT GOOGLEThe results show consistent light toggling via Google Assistant. In the end, this research contributes to an existing body of work by creating an IoT system that can be readily adopted as part of normal life. Keywords: Node-MCU, home automation; home security system; Internet of Things; intruder Detection; PIR sensor; smart home; voice commands
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Yang, Jianhua, Lixin Wang, Maochang Qin, and Noah Neundorfer. "Detecting Stepping-Stone Intrusion and Resisting Intruders’ Manipulation via Cross-Matching Network Traffic and Random Walk." Electronics 12, no. 2 (2023): 394. http://dx.doi.org/10.3390/electronics12020394.
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Attackers can exploit compromised hosts to launch attacks over the Internet. This protects an intruder, placing them behind a long connection chain consisting of multiple compromised hosts. Such attacks are called stepping-stone intrusions. Many algorithms have been proposed to detect stepping-stone intrusions, but most detection algorithms are weak in resisting intruders’ session manipulation, such as chaff-perturbation. This paper proposes a novel detection algorithm: Packet Cross-Matching and RTT-based two-dimensional random walk. Theoretical proof shows network traffic cross matching can be effective in resisting attackers’ chaff attack. Our experimental results over the AWS cloud show that the proposed algorithm can resist attackers’ chaff attacks up to a chaff rate of 100%.
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Minwalla, Cyrus, Dan Tulpan, Nabil Belacel, Fazel Famili, and Kristopher Ellis. "Detection of Airborne Collision-Course Targets for Sense and Avoid on Unmanned Aircraft Systems Using Machine Vision Techniques." Unmanned Systems 04, no. 04 (2016): 255–72. http://dx.doi.org/10.1142/s2301385016500102.
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Detecting collision-course targets in aerial scenes from purely passive optical images is challenging for a vision-based sense-and-avoid (SAA) system. Proposed herein is a processing pipeline for detecting and evaluating collision course targets from airborne imagery using machine vision techniques. The evaluation of eight feature detectors and three spatio-temporal visual cues is presented. Performance metrics for comparing feature detectors include the percentage of detected targets (PDT), percentage of false positives (POT) and the range at earliest detection ([Formula: see text]). Contrast and motion-based visual cues are evaluated against standard models and expected spatio-temporal behavior. The analysis is conducted on a multi-year database of captured imagery from actual airborne collision course flights flown at the National Research Council of Canada. Datasets from two different intruder aircraft, a Bell 206 rotor-craft and a Harvard Mark IV trainer fixed-wing aircraft, were compared for accuracy and robustness. Results indicate that the features from accelerated segment test (FAST) feature detector shows the most promise as it maximizes the range at earliest detection and minimizes false positives. Temporal trends from visual cues analyzed on the same datasets are indicative of collision-course behavior. Robustness of the cues was established across collision geometry, intruder aircraft types, illumination conditions, seasonal environmental variations and scene clutter.
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Owoeye, Samuel, Folasade Durodola, Adekunle Oyelami, et al. "Implementation of a Smart Home Intruder Detection System using a Vibrometer and ESP 32 CAM." ABUAD Journal of Engineering Research and Development (AJERD) 8, no. 1 (2025): 14–20. https://doi.org/10.53982/ajerd.2025.0801.02-j.
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Nigeria today is rife with occurrences of intruders breaking into homes at every slight opportunity. The topic of security is quite important; hence this paper presents the development and implementation of a Smart Intruder Detection System utilizing the ESP32-CAM board, the Vibrometer and the ATMega328P microcontroller to enhance lighting, security, and surveillance functionalities. The ESP32-CAM serves as the central control unit, leveraging its built-in Wi-Fi and camera capabilities, while communicating with the ATMega328P microcontroller responsible for managing lighting, security, and surveillance components. The Vibrometer adds a vital layer of security by detecting vibrations associated with forced entry attempts. Upon sensing significant vibrations, the Vibrometer triggers the ESP32-CAM to start an immediate recording of potential intrusions. In the realm of lighting control, the ATMega328P regulates diverse light sources such as LEDs and smart bulbs. The ESP32-CAM facilitates a user-friendly experience, enabling seamless control and automation of the lighting system through a dedicated mobile application or voice commands. For surveillance purposes, the ESP32-CAM captures real-time video, streaming it to the user's mobile device or a centralized monitoring station. The ATMega328P contributes to the system's intelligence by supporting motion detection algorithms, which, in turn, trigger automated alerts and activate lighting or alarm systems in response to detected movement. The precision performance of the components was carried out and the average precision for all the components was 95%. The synergistic integration of the ESP32-CAM board and ATMega328P microcontroller results in a cohesive and intelligent smart home automation solution.
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Mane, Prof Dipali, Chaitanya Chaudhari, Saurabh Shitole, Mubin Shaikh, and Shivam Sashte. "A Machine Learning Approach for Intrusion Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3811–14. http://dx.doi.org/10.22214/ijraset.2023.51086.
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Abstract: Computer networks and virtual machine security are very essential in today’s era. IDS monitors a network or system for malicious action and protects a computer network from unofficial access from users, including perhaps insiders. Various existing systems have already been developed to detect malicious activity on target machines; sometimes any external user creates some malicious behavior and gets unauthorized access to victim machines to such a behavior system considered as malicious activities or Intruder. Machine Learning (ML) algorithms are applied in IDS in order to identify and classify security threats. Numerous machine learning and soft computing techniques are designed to detect the activities in real-time network log audit data. KKDDCUP99 and NLSKDD most utilized data sets to detect the Intruder on the benchmark data set. In this paper, we proposed the identification of impostors using machine learning algorithms. Two different techniques have been proposed a signature with detection and anomaly-based detection. The experimental analysis demonstrates SVM, Naïve Bayes, and ANN algorithms with various data sets and demonstrates system performance in the real-time network environment.
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Najbile, Rakshit, Twinkle Rawalani, Vedant Panda, Pratyush Mishra, and Dr Prasanna Deshpande. "Multi-layer Anti-Theft System with an Intruder System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1445–52. http://dx.doi.org/10.22214/ijraset.2023.49673.
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Abstract: People are more concerned about the safety of their valuables like jewelry, money, important documents, etc. which is why safe deposit boxes are the safest place to keep them. The advent of rapidly growing technologies enables users to operate high security systems with electronic identification options. In this work, a design of a multilayer security system is proposed. The safety concern parameters like user password, RF identification, and fingerprint recognition, use of one time password and a motion detection and alert system are in place. An email notification of the motion detection image near the safe may also be received by a user. Our system may be considered as a useful multi layered security anti-theft product.
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Chueh, Hao-En, Shun-Chuan Ho, Shih-Peng Chang, and Ping-Yu Hsu. "Online Intrusion Behaviors: Sequences and Time Intervals." Social Behavior and Personality: an international journal 38, no. 10 (2010): 1307–12. http://dx.doi.org/10.2224/sbp.2010.38.10.1307.
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In this study we model the sequences and time intervals of online intrusion behaviors. To maintain network security, intrusion detection systems monitor network environments; however, most existing intrusion detection systems produce too many intrusion alerts, causing network managers to investigate many potential intrusions individually to determine their validity. To solve this problem, we combined a clustering analysis of the time intervals of online users' behaviors with a sequential pattern analysis to identify genuine intrusion behaviors. Knowledge of the patterns generated by intruder behaviors can help network managers maintain network security.
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Kumar, M. Adarsh, K. Nithin Kumar, N. Sandeep, and Mr B. Sai Reddy. "Advanced Alerting System by Motion Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3504–7. http://dx.doi.org/10.22214/ijraset.2023.51008.
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Abstract: Human technological advances have marked the emergence of alarm systems. The man began shouting a message to inform others of terrible information. Then people adopted new techniques to develop alarm systems to replace them. Security has always been a great concern in this developing nation. Security always plays a vital role in safeguarding and protecting property. In this internet age there is a new paradigm of technology that is clearly assisting the major concerns of security, i.e., 'Internet of things' and we thereby come with an Espressif Systems based microcontroller board alert system. The paper offers a camera module and microcontroller-based development platform that acts as a sophisticated alert indicator system embedded with passive infra-red sensor to detect the intruder motion. The designed prototype is configured with required functionality and obtained accurate results.
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Bhati, Nitesh Singh, Manju Khari, Vicente García-Díaz, and Elena Verdú. "A Review on Intrusion Detection Systems and Techniques." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp02 (2020): 65–91. http://dx.doi.org/10.1142/s0218488520400140.
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An Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our disposal, many solutions have been found to create many intrusion detection systems. However, it is difficult to identify the perfect solution from the vast options we have available. Hence, motivated by the need of a better security system, this paper presents a survey of different published solutions that have been developed and/or researched on the topic of intrusion detection techniques during the period from 2000 to 2019, including the accuracy of the output. With the help of this survey, an all-inclusive view of the different papers would be at one’s disposal.
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Shterenberg, S. I., and M. A. Poltavtseva. "A Distributed Intrusion Detection System with Protection from an Internal Intruder." Automatic Control and Computer Sciences 52, no. 8 (2018): 945–53. http://dx.doi.org/10.3103/s0146411618080230.
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Babar, Muhammad, Sarah Kaleem, Adnan Sohail, Muhammad Asim, and Muhammad Usman Tariq. "An Improved Big Data Analytics Architecture for Intruder Classification Using Machine Learning." Security and Communication Networks 2023 (December 4, 2023): 1–7. http://dx.doi.org/10.1155/2023/1216192.
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The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies.
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Laiby, Thomas, and Bhat Subrahmanya. "Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review." International Journal of Management, Technology, and Social Sciences (IJMTS) 6, no. 2 (2022): 296–314. https://doi.org/10.5281/zenodo.5814702.
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<strong>Purpose:</strong><em> The authors attempt to examine the work done in the area of Intrusion Detection System in IoT utilising Machine Learning/Deep Learning technique and various accessible datasets for IoT security in this review of literature.</em> <strong>Methodology</strong>: <em>The papers in this study were published between 2014 and 2021 and dealt with the use of IDS in IoT security. Various databases such as IEEE, Wiley, Science Direct, MDPI, and others were searched for this purpose, and shortlisted articles used Machine Learning and Deep Learning techniques to handle various IoT vulnerabilities.</em> <strong>Findings/Result</strong>: <em>In the past few years, the IDS has grown in popularity as a result of their robustness. The main idea behind intrusion detection systems is to detect intruders in a given region. An intruder is a host that tries to connect to other nodes without permission in the world of the Internet of Things. In the field of IDS, there is a research gap. Different ML/DL techniques are used for IDS in IoT. But it does not properly deal with complexity issues. Also, these techniques are limited to some attacks, and it does not provide high accuracy. </em> <strong>Originality: </strong><em>A review had been executed from various research works available from online databases and based on the survey derived a structure for the future study.</em> <strong>Paper Type: </strong><em>Literature Review.</em>
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Nurellari, Edmond, Daniel Bonilla Licea, Mounir Ghogho, and Mario Eduardo Rivero-Angeles. "On Trajectory Design for Intruder Detection in Wireless Mobile Sensor Networks." IEEE Transactions on Signal and Information Processing over Networks 7 (2021): 236–48. http://dx.doi.org/10.1109/tsipn.2021.3067305.
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Vitalii, Martovytskyi, Sievierinov Оleksandr, Liashenko Oleksii, et al. "Devising an approach to the identification of system users by their behavior using machine learning methods." Eastern-European Journal of Enterprise Technologies 3, no. 3 (117) (2022): 23–34. https://doi.org/10.15587/1729-4061.2022.259099.
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One of the biggest reasons that lead to violations of the security of companies’ services is obtaining access by the intruder to the legitimate accounts of users in the system. It is almost impossible to fight this since the intruder is authorized as a legitimate user, which makes intrusion detection systems ineffective. Thus, the task to devise methods and means of protection (intrusion detection) that would make it possible to identify system users by their behavior becomes relevant. This will in no way protect against the theft of the data of the accounts of users of the system but will make it possible to counteract the intruders in cases where they use this account for further hacking of the system. The object of this study is the process of protecting system users in the case of theft of their authentication data. The subject is the process of identifying users of the system by their behavior in the system. This paper reports a functional model of the process of ensuring the identification of users by their behavior in the system, which makes it possible to build additional means of protecting system users in the case of theft of their authentication data. The identification model takes into consideration the statistical parameters of user behavior that were obtained during the session. In contrast to the existing approaches, the proposed model makes it possible to provide a comprehensive approach to the analysis of the behavior of users both during their work (in a real-time mode) and after the session is over (in a delayed mode). An experimental study on the proposed approach of identifying users by their behavior in the system showed that the built patterns of user behavior using machine learning methods demonstrated an assessment of the quality of identification exceeding 0.95
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Cheong, Kah-Meng, Yih-Liang Shen, and Tai-Shih Chi. "Active acoustic scene monitoring through spectro-temporal modulation filtering for intruder detection." Journal of the Acoustical Society of America 151, no. 4 (2022): 2444–52. http://dx.doi.org/10.1121/10.0010070.
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An indoor acoustic scene monitoring system using a periodic impulse signal was previously developed. Compared with the impulse signal, the chirp signal is more robust against environmental noise due to its specific spectro-temporal structure. Such specific structure makes the chirp sound easily detected using a spectro-temporal modulation filtering mechanism. In this paper, we demonstrated a system that employs a two-dimensional spectro-temporal filtering mechanism on a Fourier spectrogram to measure the total energy of the reverberations of the chirp signal as the representation of the acoustic scene. The system compares the reverberation energy difference between consecutive chirps with a predefined threshold to automatically detect the change in the acoustic scene. Simulations were conducted in real living rooms with various types of background noise. Test results demonstrated that the proposed system is much more effective than previously developed systems for detecting the acoustic scene changes due to the intruder silently walking in the rooms. In the biggest test room (18 × 9.8 × 2.5 m3) with heavy background noise, the proposed system can still yield a correct identification rate higher than 80% to the intruder walking at 7 m from the microphone without producing any false alarms.
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Venkatesh, S., and N. Bhalaji. "A Distance Vector Hop and Differential Evolution based Interception Strategy for detecting Cross Border Infiltration in Underground Tunnel." Defence Science Journal 72, no. 3 (2022): 392–401. http://dx.doi.org/10.14429/dsj.72.17207.
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Securing the external border of a nation through potential surveillance is considered to be highly essential for safeguarding them from terrorists and other national armies that intentionally try to conquer regions. The infiltration of trespassers and terrorists into a territory is considered to greatly influence the harmony and peace of a nation. In this context, conventional border surveillance systems safeguard the border regions based on the pre-determined routes at various time intervals. However, the intensive involvement of human in patrolling is determined as the major challenge in the process of safeguarding longer border areas. Moreover, detecting the infiltration of terrorists through the underground tunnel is considered to be the other challenge. At this juncture, wireless sensor networks are considered to be the best suitable candidate for safeguarding the external borders through real-time monitoring to attain maximized accuracy, efficiency in the detection and least human intervention. In this paper, Distance Vector-Hop (DV-Hop) and Differential Evolution (DE)-based Interception Strategy (DV-Hop-DE-IS) is proposed for accurate detection of cross border infiltration in the underground tunnel. This proposed DV-Hop-DE-IS includes the merits of converting the discrete values of hop count into a highly accurate continuous value depending on the information received from the number of shared one-hop nodes that exists between neighbouring nodes. This problem of intruder detection is formulated as the minimum optimization problem that could be optimally solved through the utilization of the Differential Evolution algorithm with maximized efficiency. The simulation results of the DV-Hop-DE-IS confirmed better detection rate, accuracy with a reduced false positive rate compared to the benchmarked intruder detection approaches.
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Armoogum, Sheeba, and Nawaz Mohamudally. "A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems." International Journal of Computer Network and Information Security 13, no. 6 (2021): 13–25. http://dx.doi.org/10.5815/ijcnis.2021.06.02.
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Voice over Internet Protocol (VoIP) is a recent voice communication technology and due to its variety of calling capabilities, the system is expected to fuel the market value even further in the next five years. However, there are serious concerns since VoIP systems are frequently been attacked. According to recent security alliance reports, malicious activities have increased largely during the current pandemic against VoIP and other vulnerable networks. This hence implies that existing models are not sufficiently reliable since most of them do not have a hundred percent detection rate. In this paper, a review of our most recent Intrusion Detection & Prevention Systems (IDPS) developed is proposed together with a comparative analysis. The final work consisted of ten models which addressed flood intentional attacks to mitigate VoIP attacks. The methodological approaches of the studies included the quantitative and scientific paradigms, for which several instruments (comparative analysis and experiments) were used. Six prevention models were developed using three sorting methods combined with either a modified galloping algorithm or an extended quadratic algorithm. The seventh IDPS was designed by improving an existing genetic algorithm (e-GAP) and the eighth model is a novel deep learning method known as the Closest Adjacent Neighbour (CAN). Finally, for a better comparative analysis of AI-based algorithms, a Deep Analysis of the Intruder Tracing (DAIT) model using a bottom-up approach was developed to address the issues of processing time, effectiveness, and efficiency which were challenges when addressing very large datasets of incoming messages. This novel method prevented intruders to access a system without authorization and avoided any anomaly filtering at the firewall with a minimum processing time. Results revealed that the DAIT and the e-GAP models are very efficient and gave better results when benchmarking with models. These two models obtained an F-score of 98.83%, a detection rate of 100%, a false rate of 0%, an accuracy of 98.7%, and finally a processing time per message of 0.092 ms and 0.094 ms respectively. When comparing with previous models in the literature from which it is specified that detection rates obtained are 95.5% and false-positive alarm of around 1.8%, except for one recent machine learning-based model having a detection rate of 100% and a processing time of 0.53 ms, the DAIT and the e-GAP models give better results.
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Almanza-Ortega, Nelva Nely, Joaquin Perez-Ortega, Monterrubio Sergio Mauricio Martínez, and Juan A. Recio-Garcia. "Clustering-Based Cyber Situational Awareness: A Practical Approach for Masquerade Attack Detection." Journal of Artificial Intelligence and Computing Applications 2, no. 1 (2024): 35–39. https://doi.org/10.5281/zenodo.14933955.
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Cyber Situational Awareness (CSA) is crucial for detecting and mitigating security threats in evolving digital environments. Traditional intrusion detection systems face challenges related to computational efficiency, scalability, and interpretability, particularly in the detection of masquerade attacks, where attackers mimic legitimate user behavior. This exploratory study conducts a preliminary investigation into a clustering-based approach that integrates OK-Means, an optimized variant of K-Means, with k-Nearest Neighbors (k-NN) to improve intrusion detection. The proposed approach is evaluated using the Windows-Users and Intruder Simulations Logs (WUIL) dataset to assess its feasibility and preliminary performance. Experimental results suggest that this method can achieve up to 99\% recall in masquerade attack detection while reducing execution time by 85\% compared to conventional k-NN classifiers. Additionally, the integration of explainability mechanisms, such as clustering visualization and attack introspection tools, provides security analysts with interpretable insights into system decisions. As an initial exploration, this study provides early-stage insights into clustering-based CSA methods and lays the groundwork for future research. The findings suggest that this approach can be further developed and extended to other cybersecurity domains, such as phishing and malware detection, contributing to AI-driven security frameworks.Cyber Situational Awareness (CSA) is crucial for detecting and mitigating security threats in evolving digital environments. Traditional intrusion detection systems face challenges related to computational efficiency, scalability, and interpretability, particularly in the detection of masquerade attacks, where attackers mimic legitimate user behavior. This exploratory study conducts a preliminary investigation into a clustering-based approach that integrates OK-Means, an optimized variant of K-Means, with k-Nearest Neighbors (k-NN) to improve intrusion detection. The proposed approach is evaluated using the Windows-Users and Intruder Simulations Logs (WUIL) dataset to assess its feasibility and preliminary performance. Experimental results suggest that this method can achieve up to 99\% recall in masquerade attack detection while reducing execution time by 85\% compared to conventional k-NN classifiers. Additionally, the integration of explainability mechanisms, such as clustering visualization and attack introspection tools, provides security analysts with interpretable insights into system decisions. As an initial exploration, this study provides early-stage insights into clustering-based CSA methods and lays the groundwork for future research. The findings suggest that this approach can be further developed and extended to other cybersecurity domains, such as phishing and malware detection, contributing to AI-driven security frameworks.
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Abraham, Aibin, Bibin Mathew, Devika Panikkar, and Jaya John. "Literature Review on Detection Systems for Wild Animal Intrusions." March 2023 5, no. 1 (2023): 50–59. http://dx.doi.org/10.36548/jscp.2023.1.005.
Full textAbstract:
Agriculture is a crucial contributor to the economy, and farmers aim to increase their crop yields annually. However, with the increasing deforestation and destruction of wildlife habitats, wild animals are venturing out of the forest in search of food and often end up in nearby agricultural fields, leading to conflicts between farmers and wildlife. To address this issue, technology can be used to detect animal intrusions. Wireless sensors and animal intrusion detection systems, equipped with object detection and segmentation, can alert farmers regarding any animal incursions on their fields even when they are not present. When an animal enters the field, cameras at various locations capture images and send them to processors for analysis. The system then sends automatic notifications with images to landowners and foresters, thus providing an early warning so that appropriate action depending on the type of intruder can be taken. The system uses feature extraction and image matching techniques, along with regression algorithms, to identify and classify the intruding animal. This survey focuses on exploring the various steps, tools, and experimental setups that can be used to prevent human-wildlife conflicts and protect lives.
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Netinant, Paniti, Thitipong Utsanok, Meennapa Rukhiran, and Suttipong Klongdee. "Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands." IoT 5, no. 1 (2024): 79–99. http://dx.doi.org/10.3390/iot5010005.
Full textAbstract:
With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.