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Journal articles on the topic 'Agent Based Intrusion Detection System (AIDS)'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

J., K. Mandal, and Lutful Hassan Khondekar. "A NOVEL TECHNIQUE TO DETECT INTRUSION IN MANET." International Journal of Network Security & Its Applications (IJNSA) 5, no. 5 (2013): 179–83. https://doi.org/10.5281/zenodo.5602381.

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In this paper a novel technique has been proposed for intrusion detection in MANET, where agents are fired from a node for each node randomly and detect the defective nodes. Detection is based on triangular encryption technique (TE)[9,10], and AODV[1,2,3,8] is taken as routing protocol. The scheme is an ‘Agent’ based intrusion detection system. This technique is applied on two types of defective nodes namely packet sinking and black hole attack. For simulation purpose we have taken NS2 (2.33) and three type of parameters are considered. These are number of nodes, percentage of node mobility and type of defective nodes. For analysis purpose 20, 30, 30, 40, 50 and 60 nodes are taken with variability. Percentage of defectiveness as 10%, 20%, 30% and 40%.Packet sink and black hole attack are considered as defectiveness of nodes. We have considered generated packets, forward packets, average delay and drop packets as comparisons and performance analysis parameters.

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Tiwari, S. K., D. S. Pandey, and V. Namdeo. "A Real Time Approach to Strengthen Computer Security By Host Cum Network Agent Based Intrusion Detection System (HCN-AIDS)." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 204–10. http://dx.doi.org/10.26438/ijcse/v6i7.204210.

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3

Dasgupta, D., F. Gonzalez, K. Yallapu, J. Gomez, and R. Yarramsettii. "CIDS: An agent-based intrusion detection system." Computers & Security 24, no. 5 (2005): 387–98. http://dx.doi.org/10.1016/j.cose.2005.01.004.

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Xie, Ping, and Wei Wang. "The Study and Simulation on Campus Network Intrusion Detection System." Advanced Materials Research 490-495 (March 2012): 2657–61. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.2657.

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In this paper, the current intrusion detection systems are analyzed in the full study of the development trend of domestic and foreign country. According to the campus network can be divided into functional independence of the structural characteristics of the subnet, while taking full advantage of agent technology in the intrusion detection system technology, we have referenced to the agent technology and a variety of detection methods for the analysis and comparison, and have analyzed the existing distributed intrusion detection system ,we propose a monitoring and management center with a multi-agent intrusion detection model framework. This model uses a distributed architecture that combines network-and host-based intrusion detection method for intrusion detection.

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Hegazy, I. M., T. Al-Arif, Z. T. Fayed, and H. M. Faheem. "A multi-agent based system for intrusion detection." IEEE Potentials 22, no. 4 (2003): 28–31. http://dx.doi.org/10.1109/mp.2003.1238690.

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Gowadia, Vaibhav, Csilla Farkas, and Marco Valtorta. "PAID: A Probabilistic Agent-Based Intrusion Detection system." Computers & Security 24, no. 7 (2005): 529–45. http://dx.doi.org/10.1016/j.cose.2005.06.008.

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7

Liu, Yang Bin, Liang Shi, Bei Zhan Wang, Yuan Qin Wu, and Pan Hong Wang. "An New Agent Based Distributed Adaptive Intrusion Detection System." Advanced Materials Research 532-533 (June 2012): 624–29. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.624.

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In order to overcome the excessive dependence among the traditional intrusion detection system components, high rate false-alarm phenomenon caused by multiple alarms to the same invasion, inability to adaptively replace mining algorithm when testing environment has changed and other issues, this paper puts forward an Agent based distributed adaptive intrusion detection system, which employs Joint Detection mechanism for mining algorithm module, and Dynamic Election algorithm for the recovery mechanism, thereby improving the system adaptive ability to the external change.

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8

Cai, Yu. "Mobile Agent Based Network Defense System in Enterprise Network." International Journal of Handheld Computing Research 2, no. 1 (2011): 41–54. http://dx.doi.org/10.4018/jhcr.2011010103.

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Security has become the Achilles’ heel of many organizations in today’s computer-dominated society. In this paper, a configurable intrusion detection and response framework named Mobile Agents based Distributed (MAD) security system was proposed for enterprise network consisting of a large number of mobile and handheld devices. The key idea of MAD is to use autonomous mobile agents as lightweight entities to provide unified interfaces for intrusion detection, intrusion response, information fusion, and dynamic reconfiguration. These lightweight agents can be easily installed and managed on mobile and handheld devices. The MAD framework includes a family of autonomous agents, servers and software modules. An Object-based intrusion modeling language (mLanguage) is proposed to allow easy data sharing and system control. A data fusion engine (mEngine) is used to provide fused results for traffic classification and intrusion identification. To ensure Quality-of-Service (QoS) requirements for end users, adaptive resource allocation scheme is also presented. It is hoped that this project will advance the understanding of complex, interactive, and collaborative distributed systems.

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Qasim, Awais, Muhammad Bilal, Adeel Munawar, and Shuja Ur Rehman Baig. "Blockchain based intrusion detection in agent-driven flight operations." Multiagent and Grid Systems 20, no. 2 (2024): 161–83. http://dx.doi.org/10.3233/mgs-240017.

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Security and protection of the data is the core objective of every organization, but since cyber-attacks got more advanced than ever before, the data is compromised more often, resulting in financial loss, life loss, or privacy breaches as its consequences. There must be a system that can deal with the increasing number of cyber-attacks in flight operations, which are increasing in numbers and sophistication. Since we know that the traditional intrusion detection system is not capable enough to protect the data and as many human lives are at stake in flight operations, an unfortunate data corruption attack could give rise to a catastrophe. In this paper, we proposed a blockchain-based intrusion detection system for flight operations framework to protect the data’s privacy and avoid data corruption in flight operations. Blockchain not only protects data from corruption but also circumvents the challenges faced by intrusion detection systems which include trust and consensus building between different nodes in a network that can enhance the capability of the intrusion detection system.

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10

Alazab, Ammar, Michael Hobbs, Jemal Abawajy, and Ansam Khraisat. "Malware Detection and Prevention System Based on Multi-Stage Rules." International Journal of Information Security and Privacy 7, no. 2 (2013): 29–43. http://dx.doi.org/10.4018/jisp.2013040102.

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The continuously rising Internet attacks pose severe challenges to develop an effective Intrusion Detection System (IDS) to detect known and unknown malicious attack. In order to address the problem of detecting known, unknown attacks and identify an attack grouped, the authors provide a new multi stage rules for detecting anomalies in multi-stage rules. The authors used the RIPPER for rule generation, which is capable to create rule sets more quickly and can determine the attack types with smaller numbers of rules. These rules would be efficient to apply for Signature Intrusion Detection System (SIDS) and Anomaly Intrusion Detection System (AIDS).

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Cai, Yan Jing, Xian Yi Cheng, and Yan Pan. "Solutions of Single Point of Failure in Intrusion Detection System." Applied Mechanics and Materials 128-129 (October 2011): 285–88. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.285.

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In this paper, Mobile Agent (MA) and a number of intrusion detection system described. Considering the shortcoming of the current intrusion detection system, a new system called the intrusion detection system based on MA was described. Using the autonomy of MA, Intrusion Detection System based on MA avoids single-point failure, and robusts the system. As a result, the security of network has been increased.

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12

SODIYA, ADESINA SIMON. "MULTI-LEVEL AND SECURED AGENT-BASED INTRUSION DETECTION SYSTEM." Journal of Computing and Information Technology 14, no. 3 (2006): 217. http://dx.doi.org/10.2498/cit.2006.03.05.

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13

Wang, Jin. "An Autonomous Agent-Based Adaptive Distributed Intrusion Detection System." Journal of Computer Research and Development 42, no. 11 (2005): 1934. http://dx.doi.org/10.1360/crad20051116.

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14

Renjit, J. Arokia. "Distributed and cooperative multi-agent based intrusion detection system." Indian Journal of Science and Technology 3, no. 10 (2010): 1070–74. http://dx.doi.org/10.17485/ijst/2010/v3i10.2.

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15

Singh, Arjun, Surbhi Chauhan, Kamal Kant, and Reshma Dokania. "Agent based Decentralized and Fault Tolerant Intrusion Detection System." International Journal of Computer Applications 47, no. 1 (2012): 1–6. http://dx.doi.org/10.5120/7149-9850.

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16

Chen, Hai Yan. "Development and Design of Intelligent Intrusion Detection System Based on Data Mining." Applied Mechanics and Materials 602-605 (August 2014): 1526–29. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1526.

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With the popularization and development of Internet, the network has penetrated into every corner of social life. Network brings people convenient but at the same time it also brings a series of safety problems. Intrusion detection system is an important part of network security system. Computer security problem is increasingly prominent, which puts forward higher requirements on intrusion detection system. In this paper, the application of data mining and intelligent Agent detection in the intrusion detection system is researched.

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Alazab, Ammar, Michael Hobbs, Jemal Abawajy, Ansam Khraisat, and Mamoun Alazab. "Using response action with intelligent intrusion detection and prevention system against web application malware." Information Management & Computer Security 22, no. 5 (2014): 431–49. http://dx.doi.org/10.1108/imcs-02-2013-0007.

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Purpose – The purpose of this paper is to mitigate vulnerabilities in web applications, security detection and prevention are the most important mechanisms for security. However, most existing research focuses on how to prevent an attack at the web application layer, with less work dedicated to setting up a response action if a possible attack happened. Design/methodology/approach – A combination of a Signature-based Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS), namely, the Intelligent Intrusion Detection and Prevention System (IIDPS). Findings – After evaluating the new system, a better result was generated in line with detection efficiency and the false alarm rate. This demonstrates the value of direct response action in an intrusion detection system. Research limitations/implications – Data limitation. Originality/value – The contributions of this paper are to first address the problem of web application vulnerabilities. Second, to propose a combination of an SIDS and an AIDS, namely, the IIDPS. Third, this paper presents a novel approach by connecting the IIDPS with a response action using fuzzy logic. Fourth, use the risk assessment to determine an appropriate response action against each attack event. Combining the system provides a better performance for the Intrusion Detection System, and makes the detection and prevention more effective.

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18

Ganapathy, S., P. Yogesh, and A. Kannan. "Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM." Computational Intelligence and Neuroscience 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/850259.

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Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

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19

Rey, E. Mosqueira, A. Alonso Betanzos, B. Guijarro Berdinas, D. Alonso Rios, and J. Lago Pineiro. "A Snort-based agent for a JADE multi-agent intrusion detection system." International Journal of Intelligent Information and Database Systems 3, no. 1 (2009): 107. http://dx.doi.org/10.1504/ijiids.2009.023041.

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20

Zachos, Georgios, Ismael Essop, Georgios Mantas, Kyriakos Porfyrakis, José C. Ribeiro, and Jonathan Rodriguez. "An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks." Electronics 10, no. 21 (2021): 2562. http://dx.doi.org/10.3390/electronics10212562.

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Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

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21

Zeng, Xia Ling. "Cluster-Based Intrusion Detection Model for Wireless Sensor Network." Applied Mechanics and Materials 631-632 (September 2014): 914–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.914.

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An intrusion detection system (IDS) using agent technology was designed for wireless sensor network of clustering structure. An IDS agent which includes two different agents was deployed in every node of network. One is local detection agent and another is global detection agent. They complete different tasks of detection. Based on Bluetooth communication technology, Bluetooth scattering network formation algorithm TPSF was employed to construct the cluster layer of sensor network and to finish task assignment of different agents. The TPSF algorithm was improved by limiting the role of nodes to lighten the complexity of nodes, so the IDS agents can work effectively and the safety coefficient of nodes is improved.

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22

Achbarou, Omar, My Ahmed El Kiram, and Salim Elbouanani. "Cloud Security: A Multi Agent Approach Based Intrusion Detection System." Indian Journal of Science and Technology 10, no. 18 (2017): 1–6. http://dx.doi.org/10.17485/ijst/2017/v10i18/109044.

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23

Jain, Chandrakant, and Aumreesh Kumar Saxena. "General Study of Mobile Agent Based Intrusion Detection System (IDS)." Journal of Computer and Communications 04, no. 04 (2016): 93–98. http://dx.doi.org/10.4236/jcc.2016.44008.

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24

Nakkeeran, R., T. Aruldoss Albert, and R. Ezumalai. "Agent Based Efficient Anomaly Intrusion Detection System in Adhoc networks." International Journal of Engineering and Technology 2, no. 1 (2010): 52–56. http://dx.doi.org/10.7763/ijet.2010.v2.99.

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Khraisat, Ansam, Iqbal Gondal, Peter Vamplew, Joarder Kamruzzaman, and Ammar Alazab. "Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine." Electronics 9, no. 1 (2020): 173. http://dx.doi.org/10.3390/electronics9010173.

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Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.

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Liu, Zhi Yue, and Jian Wang. "A Research into an Intrusion Detection System Based on Immune Principle and Multi-Agent in WSN." Advanced Materials Research 433-440 (January 2012): 5157–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5157.

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Along with deep-going researches and practical applications of Wireless Sensor Network, security issues contained therein are raising a growing concern. Based on a systematic analysis of the current intrusion detection system and with integration between the mechanism of artificial immune system and multi-agent technology, the paper provides with a new model of an intrusion detection system based on artificial immune system and multi-agent technology, and further introduces the functions of agents in the model as well as the process of antibody selection.

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Khraisat, Gondal, Vamplew, Kamruzzaman, and Alazab. "A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks." Electronics 8, no. 11 (2019): 1210. http://dx.doi.org/10.3390/electronics8111210.

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The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

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28

Bajtoš, Tomáš, Andrej Gajdoš, Lenka Kleinová, Katarína Lučivjanská, and Pavol Sokol. "Network Intrusion Detection with Threat Agent Profiling." Security and Communication Networks 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/3614093.

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With the increase in usage of computer systems and computer networks, the problem of intrusion detection in network security has become an important issue. In this paper, we discuss approaches that simplify network administrator’s work. We applied clustering methods for security incident profiling. We considerK-means, PAM, and CLARA clustering algorithms. For this purpose, we used data collected in Warden system from various security tools. We do not aim to differentiate between normal and abnormal network traffic, but we focus on grouping similar threat agents based on attributes of security events. We suggest a case of a fine classification and a case of a coarse classification and discuss advantages of both cases.

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Viju, G. K., and Khalid Ahmed Ibrahim. "Deployment of Agent-Based Intrusion Detection for Wireless LAN." FES Journal of Engineering Sciences 6, no. 1 (2012): 12. http://dx.doi.org/10.52981/fjes.v6i1.24.

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One of the difficult duties in chemical industrial units is the determination of the level of liquid for real – time monitoring. Determination of this parameter is useful in process control loop. Hence present study is devoted for this purpose by employing microbend based optical fiber sensor. In this work, in order to continuously monitor liquid level in petroleum and chemical industries, an optical fiber sensor based on microbend effect was designed and implemented. The system is consist of a sensor that is composed of a microbend modulator, sensing fiber, emitting / detecting devices, in addition to liquid container unit, and an electronic circuit that was used to control the liquid level. The results showed that the laser technique is both accurate and immediate.

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Liang, Chao, Bharanidharan Shanmugam, Sami Azam, et al. "Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems." Electronics 9, no. 7 (2020): 1120. http://dx.doi.org/10.3390/electronics9071120.

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With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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Javidi, Mohammad Masoud. "Network Attacks Detection by Hierarchical Neural Network." Computer Engineering and Applications Journal 4, no. 2 (2015): 119–32. http://dx.doi.org/10.18495/comengapp.v4i2.108.

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Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier

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Sandosh, S., V. Govindasamy, and G. Akila. "Enhanced intrusion detection system via agent clustering and classification based on outlier detection." Peer-to-Peer Networking and Applications 13, no. 3 (2020): 1038–45. http://dx.doi.org/10.1007/s12083-019-00822-3.

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Wang, Yue, Ran Tao, and Hao Zhang. "Research on distributed intrusion detection system based on multi-living agent." Science China Information Sciences 53, no. 5 (2010): 1067–77. http://dx.doi.org/10.1007/s11432-010-0086-9.

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Gondal, Farzana Kausar. "Mobile Agent (MA) Based Intrusion Detection Systems (IDS): A Systematic Review." Innovative Computing Review 1, no. 2 (2021): 85–102. http://dx.doi.org/10.32350/icr.0102.05.

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An Intrusion Detection System (IDS) identifies the attacks by analysing the events, considered undesirable from a security perspective, in systems and networks. It is necessary for organizations to install IDS for the protection of sensitive data due to an increase in the number of incidents related to network security. It is difficult to detect intrusions from a segment that is outside a network as well as an intrusion that originated from inside a distributed network. It should be the responsibility of IDS to analyse a huge amount of data without overloading the networks and monitoring systems. Mobile agents (MA) emerged due to the deficiencies and limitations in centralized IDS. These agents can perform predefined actions by detecting malicious activities. From previously published literature, it was deduced that most of the existing IDS based on MA are not significantly effective due to limited intrusion detection and high detection time. This study categorized existing IDS and available MA-IDS to conduct a strategic review focusing on the classification of each category, that is, data collection modes, architecture, analysis techniques, and security. The limitations and strengths of the discussed IDS are presented/showcased wherever applicable. Additionally, this study suggested ways to improve the efficiency of available MA-IDS in order to secure distributed networks in the future. This overview also includes different implementations of agent based IDS. INDEX TERMS: data mining, distributed systems, Intrusion Detection System (IDS), Mobile Agents (MA), network security

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Enyindah, P., and Uzochukwu C. Onwuachu. "An Intrusion Detection System Application for an Organisation." Circulation in Computer Science 2, no. 3 (2017): 23–28. http://dx.doi.org/10.22632/ccs-2017-251-87.

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This paper is aimed at developing an Intrusion Detection System (IDS) that will guarantee the integrity of data housed in databases and connected through a network to other computers. There is need to protect clients’ and organization’s sensitive information as well as gain the benefits of Information Technology without compromising reliability of information. The objective of this software is to detect and track down details of intrusion attempt by unauthorized users such as cases of Internet Fraud. The Structured System Analysis and Development Methodology (SSADM) is adopted to developing this system as it aids effective analysis of the above problem using a series of well defined steps that builds upon each other. The development is done using technologies such as html, css, javascript for its frontend; php for its backend and the Mysql database technology for an organized data collection. The expected result will be a network based Intrusion Detection System that will help prevent unauthorized access to a network and its linked data.

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Amrita, Amrita, Arun .., and Aditi Sharma. "A Hybrid Intrusion Detection Approach for Cyber Attacks." Journal of Cybersecurity and Information Management 8, no. 2 (2024): 08–18. http://dx.doi.org/10.54216/jcim.130201.

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The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in real-time. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomaly-based IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns.

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Battini Sujatha, Et al. "An Efficient Fuzzy Based Multi Level Clustering Model Using Artificial Bee Colony For Intrusion Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 264–73. http://dx.doi.org/10.17762/ijritcc.v11i11.9390.

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Network security is becoming increasingly important as computer technology advances. One of the most important components in maintaining a secure network is an Intrusion Detection System (IDS). An IDS is a collection of tools used to detect and report network anomalies. Threats to computer networks are increasing at an alarming rate. As a result, it is critical to create and maintain a safe computing environment. For network security, researchers employ a range of technologies, including anomaly-based intrusion detection systems (AIDS). These anomaly-based detections face a major challenge in the classification of data. Optimization algorithms that mimic the foraging behavior of bees in nature, such as the artificial bee colony algorithm, is a highly successful tool. A computer network's intrusion detection system (IDS) is an essential tool for keeping tabs on the activities taking place in the network. Artificial Bee Colony (ABC) algorithm is used in this research for effective intrusion detection. More and more intrusion detection systems are needed to keep up with the increasing number of attacks and the increase in Internet bandwidth. Detecting developing threats with high accuracy at line rates is the prerequisite for a good intrusion detection system. As traffic grows, current systems will be overwhelmed by the sheer volume of false positives and negatives they generate. In order to detect intrusions based on anomalies, this research employs an Efficient Fuzzy based Multi Level Clustering Model using Artificial Bee Colony (EFMLC-ABC). A semi-supervised intrusion detection method based on an artificial bee colony algorithm is proposed in this paper to optimize cluster centers and identify the best clustering options. In order to assess the effectiveness of the proposed method, various subsets of the KDD Cup 99 database were subjected to experimental testing. Analyses have shown that the proposed algorithm is suitable and efficient for intrusion detection system.

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Seyedeh, Yasaman Rashida. "HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NETWORK." International Journal of Network Security & Its Applications (IJNSA) 5, no. 3 (2013): 45–54. https://doi.org/10.5281/zenodo.4267213.

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In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects. 

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Lokbani, Ahmed Chaouki, Ahmed Lehireche, Reda Mohamed Hamou, and Mohamed Amine Boudia. "An Approach based on Social Bees for an Intrusion Detection System by Scenario." International Journal of Organizational and Collective Intelligence 5, no. 3 (2015): 44–67. http://dx.doi.org/10.4018/ijoci.2015070104.

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The aim of the authors' work is to model the intrusion detection system by scenario with a bio-inspired method in this case the system of protection of social bees. The natural pattern of social bees produces security efficiency by its three filters. In this paper, the authors focus on scenario approach they chose as a strategy to intrusion odor recognition of bees. They propose a new philosophy based on limited responsibility for each agent. This proposition aims to better exploit the performance of their hardware, and to use intelligently a kddcup'99 corpus.

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Alazab, Ammar, Ansam Khraisat, Sarabjot Singh, Savitri Bevinakoppa, and Osama A. Mahdi. "Routing Attacks Detection in 6LoWPAN-Based Internet of Things." Electronics 12, no. 6 (2023): 1320. http://dx.doi.org/10.3390/electronics12061320.

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The Internet of Things (IoT) has become increasingly popular, and opened new possibilities for applications in various domains. However, the IoT also poses security challenges due to the limited resources of the devices and its dynamic network topology. Routing attacks on 6LoWPAN-based IoT devices can be particularly challenging to detect because of its unique characteristics of the network. In recent years, several techniques have been proposed for detecting routing attacks, including anomaly detection. These techniques leverage different features of network traffic to identify and classify routing attacks. This paper focuses on routing attacks that target the Routing Protocol for Low-Power and Lossy Networks (RPL), which are widely used in 6LoWPAN-based IoT systems. The attacks discussed in this paper can be categorized as either inherited from Wireless Sensor Networks or exploiting vulnerabilities unique to RPL (known as RPL-specific attacks). The paper describes various RPL attacks, including Flood Attacks, Data-DoS/DDoS Attacks, Wormhole Attacks, RPL Rank Attacks, Blackhole Attacks, Version Attacks, and Sinkhole Attacks. In this paper, a novel Hybrid Intrusion Detection System (HIDS) that combines a decision tree classifier and a one-class Support Vector Machine classifier is proposed to detect routing attacks. The HIDS draws on the strengths of both a Signature Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS) to identify routing attacks with a high degree of accuracy and a low false alarm rate. The routing dataset, which features genuine IoT network traffic and various kinds of routing attacks, was used to test the proposed HIDS. According to the findings, the hybrid IDS proposed in this study outperforms SIDS and AIDS approaches, with higher detection rates and lower false positive rates.

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Almania, Moaad, Anazida Zainal, Fuad A. Ghaleb, Ahmad Alnawasrah, and Mahmoud Al Qerom. "Adaptive Intrusion Detection System with Ensemble Classifiers for Handling Imbalanced Datasets and Dynamic Network Traffic." Journal of Robotics and Control (JRC) 6, no. 1 (2025): 114–23. https://doi.org/10.18196/jrc.v6i1.23648.

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Intrusion Detection Systems (IDS) are crucial for network security, but their effectiveness often diminishes in dynamic environments due to outdated models and imbalanced datasets. This paper presents a novel Adaptive Intrusion Detection System (AIDS) that addresses these challenges by incorporating ensemble classifiers and dynamic retraining. The AIDS model integrates K-Nearest Neighbors (KNN), Fuzzy c-means clustering, and weight mapping to improve detection accuracy and adaptability to evolving network traffic. The system dynamically updates its reference model based on the severity of changes in network traffic, enabling more accurate and timely detection of cyber threats. To mitigate the effects of imbalanced datasets, ensemble classifiers, including Decision Tree (DT) and Random Forest (RF), are employed, resulting in significant performance improvements. Experimental results show that the proposed model achieves an overall accuracy of 97.7% and a false alarm rate (FAR) of 2.0%, outperforming traditional IDS models. Additionally, the study explores the impact of various retraining thresholds and demonstrates the model's robustness in handling both common and rare attack types. A comparative analysis with existing IDS models highlights the advantages of the AIDS model, particularly in dynamic and imbalanced network environments. The findings suggest that the AIDS model offers a promising solution for real-time IDS applications, with potential for further enhancements in scalability and computational efficiency.

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Saxena, Arun, and A. K. Sharma. "An Agent based Distributed Security System for Intrusion Detection in Computer Networks." International Journal of Computer Applications 12, no. 3 (2010): 18–27. http://dx.doi.org/10.5120/1659-2234.

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Singh, Namita, Siddharth Krishan, and Uday Kumar Singh. "An Enhanced Multi-Agent based Network Intrusion Detection System using Shadow Log." International Journal of Computer Applications 100, no. 9 (2014): 1–5. http://dx.doi.org/10.5120/17550-8146.

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Zhai, Shuang-can, Chen-jun Hu, and Wei-ming Zhang. "Multi-Agent Distributed Intrusion Detection System Model Based on BP Neural Network." International Journal of Security and Its Applications 8, no. 2 (2014): 183–92. http://dx.doi.org/10.14257/ijsia.2014.8.2.19.

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Eesa, Adel S., Adnan M. Abdulazeez, and Zeynep Orman. "A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree." Science Journal of University of Zakho 5, no. 4 (2017): 313. http://dx.doi.org/10.25271/2017.5.4.382.

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Different Distributed Intrusion Detection Systems (DIDS) based on mobile agents have been proposed in recent years to protect computer systems from intruders. Since intrusion detection systems deal with a large amount of data, keeping the best quality of features is an important task in these systems. In this paper, a novel DIDS based on the combination of Cuttlefish Optimization Algorithm (CFA) and Decision Tree (DT) is proposed. The proposed system uses an agent called Rule and Feature Generator Agent (RFGA) to generate a subset of features with corresponding rules. RFGA agent uses CFA to search for optimal subset of features, while DT is used as a measurement on the selected features. The proposed model is tested on the KDD Cup 99 dataset. The obtained results show that the proposed system gives a better performance even with a small subset of 5 features when compared with using all 41 features.

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Karraz, George. "Develop an Intelligent Anomaly Intrusion Detection System in Computer Networks based on Resilient Back-propagation Neural Network." Damascus University Journal for Basic Sciences, no. 10566-196 (April 29, 2024): 1–18. https://doi.org/10.5281/zenodo.11087938.

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Various anomaly attacks and disruptions to information networks are considered serious problems that affect the protection of information exchanged between these networks and affect the maintenance of reliability and confidentiality of information exchange. In the past decade, researchers around the world have faced many challenges and need to propose a set of systems with flexible architectures to accurately and automatically detect anomaly intrusion attacks to address their complexity. Related research has proposed many full-scale solutions based on machine learning ML techniques. Recent research has focused on building an anomaly intrusion detection system AIDS from a mathematical and architectural point of view, using sophisticated methods such as support vector machines (SVMs) and convolutional neural networks (CNNs). Many studies use moderate and low complexity AIDS based on the classical multilayer neural network MLNN. Therefore, the accuracy of MLNN classifiers in the testing phase is moderate or low. Based on relevant AIDS studies proposed in the literature and our detailed investigation, we find that the resilient backpropagation RBP algorithm is not used as a learning method for MLNN-based AIDS. In particular, RBP is an effective tool in many nonlinear binary classifiers. In this paper, we present an AIDS construction method based on MLNN trained by the RBP algorithm, using well-known related data NSL-KDD and CIC-DDoS2019. In this study, we carefully selected an appropriate AIDS architecture and made many attempts to avoid the above difficulties. Our AIDS was found to be stably trained without limitations in a reasonable amount of time, and subsequently tested on unprecedented data, with an accuracy of about 99%. We also compared the performance of our algorithm with other well-known MLNN learning algorithms (Levenberg Marquardt LM, Bayesian Regulated BR, and Quasi-Newton QN) using the same AIDS architecture and data set. The comparison results show that the RBP algorithm has the best performance among many algorithms.

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Al-Shourbaji, Ibrahim, and Samaher Al-Janabi. "Intrusion Detection and Prevention Systems in Wireless Networks." Kurdistan Journal of Applied Research 2, no. 3 (2017): 267–72. http://dx.doi.org/10.24017/science.2017.3.48.

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In society today, public and personal communication are often carried out through wireless technology. These technologies can be vulnerable to various types of attacks. Attackers can access the signal to listen or to cause more damage on the wireless networks. Intrusion Detection and Prevention System (IDPS) technology can be used to monitor and analyze the signal for any infiltration to prevent interception or other malicious intrusion. An overview description of IDPSs and their core functions, the primary types of intrusion detection mechanisms, and the limitations of IDPSs are discussed. This work perceives the requirements of developing new and sophisticated detection and prevention methods based on, and managed by, combining smart techniques including machine learning, data mining, and game theory along with risk analysis and assessment techniques. This assists wireless networks toremain secure and aids system administrators to effectively monitor their systems.

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Franklin, Eichie, and Bernardi Pranggono. "Anomaly-Based Intrusion Detection System for the Internet of Medical Things." IJID (International Journal on Informatics for Development) 12, no. 2 (2024): 374–85. https://doi.org/10.14421/ijid.2023.4308.

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The use of the Internet of Things (IoT) in the health sector, known as the Internet of Medical Things (IoMT), allows for personalized and convenient (e)-health services for patients. However, there are concerns about security and privacy as unethical hackers can compromise these network systems with malware. We proposed using hyperparameter-optimized Machine and Deep Learning models to address these concerns to build more robust security solutions. We used a representative Anomaly Intrusion Detection System (AIDS) dataset to train six state-of-the-art Machine Learning (ML) and Deep Learning (DL) architectures, with the Synthetic Minority Oversampling Technique (SMOTE) algorithm used to handle class imbalance in the training dataset. Our hyperparameter optimization using the Random search algorithm accurately classified normal cases for all six models, with Random Forest (RF) and K-Nearest Neighbors (KNN) performing the best in accuracy. The attention-based Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model was the second-best performer, while the hybrid CNN-LSTM model performed the worst. However, there was no single best model in classifying all attack labels, as each model performed differently in terms of different metrics.

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Dwivedi, Abhijit, Y. K. Rana Y.K.Rana, and B. P. Patel B.P.Patel. "A Real Time Host and Network Mobile Agent based Intrusion Detection System (HNMAIDS)." International Journal of Computer Applications 113, no. 12 (2015): 33–40. http://dx.doi.org/10.5120/19881-1895.

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Kumar Saxena, Aumreesh, Sitesh Sinha, and Piyush Shukla. "Performance Analysis of Classification Techniques by using Multi Agent Based Intrusion Detection System." International Journal of Computer Network and Information Security 10, no. 3 (2018): 17–24. http://dx.doi.org/10.5815/ijcnis.2018.03.03.

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