Journal articles: 'Intrusion Prevention System'

1

Kaur, Harpreet. "NETWORK INTRUSION DETECTION AND PREVENTION ATTACKS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 21–23. http://dx.doi.org/10.24297/ijct.v2i3a.2669.

Full text

Abstract:

Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathway for intrusion. This system is designed to detect and combat some common attacks on network systems. It follows the signature based IDs methodology for ascertaining attacks. A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. In this system the attack log displays the list of attacks to the administrator for evasive action. This system works as an alert device in the event of attacks directed towards an entire network.

APA, Harvard, Vancouver, ISO, and other styles

2

Ninawe, Shreyash, Vilas Bariyekar, and Ranjita Asati. "Network Intrusion Prevention System." IJARCCE 8, no. 2 (2019): 196–99. http://dx.doi.org/10.17148/ijarcce.2019.8235.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Farhaoui, Yousef. "Intrusion Prevention System Inspired Immune Systems." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 1 (2016): 168. http://dx.doi.org/10.11591/ijeecs.v2.i1.pp168-179.

Full text

Abstract:

In view of new communication and information technologies that appeared with the emergence of networks and Internet, the computer security became a major challenge, and works in this research axis are increasingly numerous. Various tools and mechanisms are developed in order to guarantee a safety level up to the requirements of modern life. Among them, intrusion detection and prevention systems (IDPS) intended to locate activities or abnormal behaviors suspect to be detrimental to the correct operation of the system. The purpose of this work is the design and the realization of an IDPS inspired from natural immune systems. The study of biological systems to get inspired from them for the resolution of computer science problems is an axis of the artificial intelligence field which gave rise to robust and effective methods by their natural function, the immune systems aroused the interest of researchers in the intrusion detection field, taking into account the similarities of NIS (Natural Immune System) and IDPS objectives. Within the framework of this work, we conceived an IDPS inspired from natural immune system and implemented by using a directed approach. A platform was developed and tests were carried out in order to assess our system performances.

APA, Harvard, Vancouver, ISO, and other styles

4

Stiawan, Deris, Abdul Hanan Abdullah, and Mohd Yazid Idris. "Characterizing Network Intrusion Prevention System." International Journal of Computer Applications 14, no. 1 (2011): 11–18. http://dx.doi.org/10.5120/1811-2439.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Singh, Neha, Deepali Virmani, and Xiao-Zhi Gao. "A Fuzzy Logic-Based Method to Avert Intrusions in Wireless Sensor Networks Using WSN-DS Dataset." International Journal of Computational Intelligence and Applications 19, no. 03 (2020): 2050018. http://dx.doi.org/10.1142/s1469026820500182.

Full text

Abstract:

Intrusion is one of the biggest problems in wireless sensor networks. Because of the evolution in wired and wireless mechanization, various archetypes are used for communication. But security is the major concern as networks are more prone to intrusions. An intrusion can be dealt in two ways: either by detecting an intrusion in a wireless sensor network or by preventing an intrusion in a wireless sensor network. Many researchers are working on detecting intrusions and less emphasis is given on intrusion prevention. One of the modern techniques for averting intrusions is through fuzzy logic. In this paper, we have defined a fuzzy rule-based system to avert intrusions in wireless sensor network. The proposed system works in three phases: feature extraction, membership value computation and fuzzified rule applicator. The proposed method revolves around predicting nodes in three categories as “red”, “orange” and “green”. “Red” represents that the node is malicious and prevents it from entering the network. “Orange” represents that the node “might be malicious” and marks it suspicious. “Green” represents that the node is not malicious and it is safe to enter the network. The parameters for the proposed FzMAI are packet send to base station, energy consumption, signal strength, a packet received and PDR. Evaluation results show an accuracy of 98.29% for the proposed system. A detailed comparative analysis concludes that the proposed system outperforms all the other considered fuzzy rule-based systems. The advantage of the proposed system is that it prevents a malicious node from entering the system, thus averting intrusion.

APA, Harvard, Vancouver, ISO, and other styles

6

Safana Hyder Abbas, Wedad Abdul Khuder Naser, and Amal Abbas Kadhim. "Subject review: Intrusion Detection System (IDS) and Intrusion Prevention System (IPS)." Global Journal of Engineering and Technology Advances 14, no. 2 (2023): 155–58. http://dx.doi.org/10.30574/gjeta.2023.14.2.0031.

Full text

Abstract:

Intrusion detection system (IDS) is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents. If anomaly traffic pass through the network IDS would generate a false positive which means it only detects the malicious traffic, takes no action and generates only alerts but IPS detects the malicious traffic or suspicious activity, takes the actions like terminate, block or drop the connections. This paper provides an explanation of network intrusion, detection, and prevention to overcome them.

APA, Harvard, Vancouver, ISO, and other styles

7

Safana, Hyder Abbas, Abdul Khuder Naser Wedad, and Abbas Kadhim Amal. "Subject review: Intrusion Detection System (IDS) and Intrusion Prevention System (IPS)." Global Journal of Engineering and Technology Advances 14, no. 2 (2023): 155–58. https://doi.org/10.5281/zenodo.7931783.

Full text

Abstract:

Intrusion detection system (IDS) is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents. If anomaly traffic pass through the network IDS would generate  a false positive which means it only detects the malicious traffic, takes no action and generates only alerts but IPS detects the malicious traffic or suspicious activity, takes the actions like terminate, block or drop the connections. This paper provides an explanation of network intrusion, detection, and prevention to overcome them.

APA, Harvard, Vancouver, ISO, and other styles

8

Sharma, Himanshu, Prabhat Kumar, and Kavita Sharma. "Recurrent Neural Network based Incremental model for Intrusion Detection System in IoT." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3778–95. http://dx.doi.org/10.12694/scpe.v25i5.3004.

Full text

Abstract:

The security of Internet of Things (IoT) networks has become a integral problem in view of the exponential growth of IoT devices. Intrusion detection and prevention is an approach ,used to identify, analyze, and block cyber threats to protect IoT from unauthorized access or attacks. This paper introduces an adaptive and incremental intrusion detection and prevention system based on RNNs, to the ever changing field of IoT security. IoT networks require advanced intrusion detection systems that can identify emerging threats because of their various and dynamic data sources. The complexity of IoT network data makes it difficult for traditional intrusion detection techniques to detect potential threats. Using the capabilities of RNNs, a model for creating and deploying an intrusion detection and prevention system (IDPS) is proposed in this paper. RNNs work particularly well for sequential data processing, which makes them an appropriate choice for IoT network traffic monitoring. NSL-KDD dataset is taken, pre-processed, features are extracted, and RNN-based model is built as a part of the proposed work. The experimental findings illustrate how effective the suggested approach is at identifying and blocking intrusions in Internet of Things networks. This paper not only demonstrates the effectiveness of RNNs in enhancing IoT network security but also opens avenues for further exploration in this burgeoning field. It presents a scalable, adaptive intrusion detection and prevention solution, responding to the evolving landscape of IoT security. As IoT networks continue to expand, the research enriches the discourse on developing resilient security strategies to combat emerging threats in scalable computing environments.

APA, Harvard, Vancouver, ISO, and other styles

9

Su, Thawda Win. "Survival of an Intrusion Tolerance Database System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1748–51. https://doi.org/10.5281/zenodo.3591410.

Full text

Abstract:

While traditional secure database systems rely on prevention control and are very limited in surviving malicious attack, an intrusion tolerant database system can operate through attacks in such a way that the system can continue delivering essential services in the face of attacks. The emphasis of survivability is on continuity of operations, with the understanding that the security precautions cannot guarantee that systems will not be penetrated and compromised. In this paper, we propose a framework of model based evaluation of the survivable intrusion tolerant database system. We focus on modeling the behaviors of an intrusion tolerant database system which can detect intrusions, isolate attacks, contain, assess, rejuvenate and repair limited in surviving malicious attacks. We contain the necessary quantitative metrics to measure the availability, integrity, and survivability. Quantitative measures are proposed to characterize the capability of a resilient database system surviving intrusions. Su Thawda Win "Survival of an Intrusion Tolerance Database System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26748.pdf

APA, Harvard, Vancouver, ISO, and other styles

10

Tasneem, Aaliya, Abhishek Kumar, and Shabnam Sharma. "Intrusion Detection Prevention System using SNORT." International Journal of Computer Applications 181, no. 32 (2018): 21–24. http://dx.doi.org/10.5120/ijca2018918280.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Chauhan, Shivam Kumar, Abhishek Sharma, and Avinash Kaur. "Animal Intrusion Detection and Prevention System." International Journal of Computer and Organization Trends 11, no. 2 (2021): 25–28. http://dx.doi.org/10.14445/22492593/ijcot-v11i2p308.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Bhatnagar, Prerna. "Smart Security and Intrusion Prevention System." International Journal for Research in Applied Science and Engineering Technology 7, no. 11 (2019): 908–10. http://dx.doi.org/10.22214/ijraset.2019.11153.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Subasree, S., Michael Jeniston S. Christ, M. Harish, V. Jeevaranjan, and S. Manikandan. "Enhanced Security through Intrusion Detection and Prevention System." Recent Trends in Cyber Criminology Research 1, no. 2 (2025): 1–5. https://doi.org/10.5281/zenodo.15582270.

Full text

Abstract:

This paper presents the design, implementation, and evaluation of a rule-based Intrusion Detection and Prevention System (IDPS) for network security. The proposed system employs signature-based detection, anomaly-based heuristics, and active prevention techniques to detect and mitigate various types of network intrusions with high efficiency. The system was developed to address the growing challenges in network security by providing a lightweight and effective approach that does not rely on machine learning algorithms. Our implementation utilizes Scapy for packet manipulation, custom rule engines, and stateful inspection to analyse network traffic patterns. Experimental results demonstrate that our system achieves over 95% detection accuracy while maintaining low false positive rates. The implementation proves practical viability for real-world deployment in diverse network environments, offering enhanced protection against common attack vectors including DoS, probe attempts, and unauthorized access.

APA, Harvard, Vancouver, ISO, and other styles

14

Sreenivasa Reddy, G., and G. Shyama Chandra Prasad. "INTRUSION DETECTION SYSTEM USING CLUSTERING ALGORITHMS OF NEURAL NETWORKS." International Journal of Advanced Research 11, no. 11 (2023): 607–14. http://dx.doi.org/10.21474/ijar01/17861.

Full text

Abstract:

This research paper explores the application of clustering algorithms in neural networks for enhancing Intrusion Detection Systems (IDS). Intrusion Detection Systems are critical in safeguarding information systems from unauthorized access, misuse, or damage. The dynamic nature of cyber threats necessitates advanced approaches for detection and prevention. Neural networks, with their ability to learn and adapt, offer significant potential in identifying and classifying network intrusions. This paper reviews various neural network architectures and clustering algorithms, their integration in IDS, and evaluates their effectiveness in detecting known and unknown threats.

APA, Harvard, Vancouver, ISO, and other styles

15

Sharma, Gaurav, and Anil Kumar Kapil. "INTRUSION DETECTION AND PREVENTION FRAMEWORK USING DATA MINING TECHNIQUES FOR FINANCIAL SECTOR." Acta Informatica Malaysia 5, no. 2 (2021): 58–61. http://dx.doi.org/10.26480/aim.02.2021.58.61.

Full text

Abstract:

Security becomes the main concern when the resources are shared over a network for many purposes. For ease of use and time saving several services offered by banks and other financial companies are accessible over mobile apps and computers connected with the Internet. Intrusion detection (ID) is the act of detecting actions that attempt to compromise the confidentiality, integrity, or availability of a shared resource over a network. Intrusion detection does not include the prevention of intrusions. A different solution is required for intrusion prevention. The major intrusion detection technique is host-based where major accountabilities are taken by the server itself to detect relevant security attacks. In this paper, an intrusion detection algorithm using data mining is presented. The proposed algorithm is compared with the signature apriori algorithm for performance. The proposed algorithm observed better results. This framework may help to explore new areas of future research in increasing security in the banking and financial sector enabled by an intrusion detection system (IDS).

APA, Harvard, Vancouver, ISO, and other styles

16

Korcak, Michal, Jaroslav Lamer, and Frantisek Jakab. "Intrusion Prevention/Intrusion Detection System (IPS/IDS) for Wifi Networks." International journal of Computer Networks & Communications 6, no. 4 (2014): 77–89. http://dx.doi.org/10.5121/ijcnc.2014.6407.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Gandhar, Abhishek, Prakhar Priyadarshi, Shashi Gandhar, S. B. Kumar, Arvind Rehalia, and Mohit Tiwari. "An Effective Deep Learning Model Design for Cyber Intrusion Prevention System." Indian Journal Of Science And Technology 18, no. 10 (2025): 811–15. https://doi.org/10.17485/ijst/v18i10.318.

Full text

Abstract:

Objectives: The increasing frequency of cyber threats necessitates the advancement of Intrusion Prevention Systems (IPS). However, existing IPS models suffer from high false positive rates, inefficiencies in real-time detection, and suboptimal accuracy levels. Methods: This study presents a CNN-LSTM hybrid model optimized for real-time cyber intrusion detection. The CICIDS2018 dataset was utilized for training, incorporating feature selection, hyper-parameter tuning, and dropout-based regularization to improve efficiency and prevent over-fitting. Findings: The proposed system achieved an F1-score of 99.5%, significantly outperforming conventional methods. Additionally, the false positive rate was reduced by 18%, enhancing system reliability in cyber-security applications. Novelty: Unlike prior works, this study integrates optimized feature selection mechanisms with real-time sequence learning through CNN-LSTM, leading to higher detection accuracy, improved generalization, and reduced computational complexity. Keywords: Convolutional neural networks (CNNs), CICIDS2018, Deep Learning, Feature selection, Long Shortterm Memory Networks (LSTMs)

APA, Harvard, Vancouver, ISO, and other styles

18

Chandre, Pankaj Ramchandra, Parikshit Mahalle, and Gitanjali Shinde. "Intrusion prevention system using convolutional neural network for wireless sensor network." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 504. http://dx.doi.org/10.11591/ijai.v11.i2.pp504-515.

Full text

Abstract:

Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed deep learning model using convolutional neural network. The proposed model includes two steps, intrusion detection and intrusion prevention. The proposed model learns useful feature representations from large amount of labeled data and then classifies them. In this work, convolutional neural network is used to prevent intrusion for WSN. To evaluate and check the effectiveness of the proposed system, the wireless sensor network dataset (WSNDS) dataset is used and the tests are performed. The test results show that proposed system has an accuracy of 97% and works better than existing system. The proposed work can be used as future benchmark for the deep learning and intrusion prevention research communities.

APA, Harvard, Vancouver, ISO, and other styles

19

Pankaj, Chandre, Mahalle Parikshit, and Shinde Gitanjali. "Intrusion prevention system using convolutional neural network for wireless sensor network." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 504–15. https://doi.org/10.11591/ijai.v11.i2.pp504-515.

Full text

Abstract:

Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed deep learning model using convolutional neural network. The proposed model includes two steps, intrusion detection and intrusion prevention. The proposed model learns useful feature representations from large amount of labeled data and then classifies them. In this work, convolutional neural network is used to prevent intrusion for WSN. To evaluate and check the effectiveness of the proposed system, the wireless sensor network dataset (WSNDS) dataset is used and the tests are performed. The test results show that proposed system has an accuracy of 97% and works better than existing system. The proposed work can be used as future benchmark for the deep learning and intrusion prevention research communities.

APA, Harvard, Vancouver, ISO, and other styles

20

Guo, Hui Ling. "Research on the Model of Database Intrusion Protection System Based on E-Commerce Platform." Applied Mechanics and Materials 336-338 (July 2013): 2559–62. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2559.

Full text

Abstract:

Based on attack model of database,a model of database intrusion prevention system is proposed in electronic commerce platform. The model is divided into session level intrusion detection model, schema level intrusion detection model and semantic level intrusion detection model according to the abstraction level of test information. It extends the COAST firewall model with intrusion detection, and a layered intrusion prevention model which detect intrusion behavior according to session level, schema level and semantic level information of transactions. Thereby, it updates database security from passive protection to proactive protection.

APA, Harvard, Vancouver, ISO, and other styles

21

Anand, Saloni, and Kshitij Patne. "Network Intrusion Detection and Prevention." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3754–59. http://dx.doi.org/10.22214/ijraset.2022.44761.

Full text

Abstract:

Abstract: Intrusion Detection systems are now increasingly significant in network security. As the number of people using the internet grows, so does the chance of a cyberattack. People are adopting signature-based intrusion detection systems. Snort is a popular open-source signature-based intrusion detection system. It is widely utilised in the intrusion detection and prevention arena across the world. The aim of this research is to provide knowledge about intrusion detection systems, application vulnerabilities, and their prevention methods and to perform a comparison of the latest tools and mechanisms used to detect these threats and vulnerabilities.

APA, Harvard, Vancouver, ISO, and other styles

22

Onwodi, Greg. "Smart Intrusion Prevention System for the Local Area Network of Nigeria Immigration Special Study Centre, Gwagwalada, FCT, Abuja, Nigeria." Advances in Multidisciplinary and scientific Research Journal Publication 10, no. 4 (2022): 73–78. http://dx.doi.org/10.22624/aims/digital/v10n4p10.

Full text

Abstract:

This study examines the Smart Intrusion Prevention System for the Local Area Network of Nigeria Immigration Special Study Centre, Gwagwalada, Abuja. Different research methodologies for Intrusion Prevention System was analysed and the hybrid based methodology was adopted because of its incorporation of the other methodologies. Configuration of the Intrusion Prevention System was studied and implemented using software known as Mikrotik. The Mikrotik software configuration was analysed properly. Among the major findings of the study was that the Local Area Network of Nigeria Immigration Special Study Centre, Gwagwalada, Abuja was relatively insecure with the sophisticated breed of attackers we have now. The study therefore recommended that Nigeria Immigration Special Study Centre, Gwagwalada, Abuja should integrate a known Intrusion Prevention System software and endeavour to keep it updated at all times. Also, they can work on system design and algorithm design for secure communication over complex networks. Keywords: Intrusion Prevention System, LAN, Nigeria, Immigration, Special Study Centre, FCT, Abuja,

APA, Harvard, Vancouver, ISO, and other styles

23

Shirsat,, Yashraj. "Smart Farmland for Crop Prevention & Animal Intrusion Detection Using IOT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41299.

Full text

Abstract:

In modern agriculture, protecting crops and liv estock from intrusions and ensuring their safety is crucial for maintaining farm productivity and reducing losses. This project introduces a smart farmland management system utilizing IoT technologies to detect and prevent animal intrusions. The system employs a Raspberry Pi coupled with a camera to monitor the farmland continuously. When the camera detects the presence of animals, it triggers a buzzer to deter the intruders, creating an immediate response to protect the crops. This real-time intervention helps minimize potential damage and keeps the animals away from the farm. Smart Farmland is an innovative IoT-based system designed to prevent crop damage and detect animal intrusions in agricultural fields.The system is equipped with a GSM module that sends instant notifications to the farmer's mobile device whenever an intrusion is detected. This feature ensures that the farmer is promptly informed of any threats, allowing for timely intervention and management. By integrating these technologies, the system not only enhances farm security but also streamlines communication between the farm and its caretaker, ultimately leading to improved farm management and reduced crop and livestock loss.The goal of Smart Farmland is to create a cutting-edge, IoT-based system for agricultural fields that utilizes real-time monitoring and automated alerts to prevent crop damage and detect animal intrusions, enabling farmers to take swift action and protect their crops. By leveraging innovative technologies, Smart Farmland aims to enhance crop yields, reduce losses, and increase agricultural productivity, ultimately contributing to a more sustainable and efficient farming practice. Key Words: IoT-based farming, crop protection, animal intrusion detection, real-time monitoring, automated alerts, Raspberry Pi, camera, sensor technology, GSM module, smart agriculture, precision farming, farm security, crop yield optimization, agricultural productivity, sustainable farming, farming innovation, technology in agriculture, farm management, animal deterrent

APA, Harvard, Vancouver, ISO, and other styles

24

Murugan, S., and K. Kuppusamy. "Intelligence Intrusion Multi Detection Prevention System Principles." i-manager's Journal on Software Engineering 10, no. 1 (2015): 31–41. http://dx.doi.org/10.26634/jse.10.1.3630.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Bocu, Razvan, and Maksim Iavich. "Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks." Symmetry 15, no. 1 (2022): 110. http://dx.doi.org/10.3390/sym15010110.

Full text

Abstract:

The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios.

APA, Harvard, Vancouver, ISO, and other styles

26

Ugale, Archana R., and Amol D. Potgantwar. "Anomaly Based Intrusion Detection through Efficient Machine Learning Model." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 616–22. http://dx.doi.org/10.37391/ijeer.110251.

Full text

Abstract:

Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify system or network intrusions for data prevention. The amount, diversity, and speed of network data make data analysis to identify assaults challenging. IDS uses machine learning methods for precise and efficient development of data security mechanism. This work presented intrusion detection model using machine learning, which utilised feature extraction, feature selection and feature modelling for intrusion detection classifier.

APA, Harvard, Vancouver, ISO, and other styles

27

S, Bhaggiaraj, Shanthini S, Sugantha Mallika S.S., and Muthuram R. "NEXT-GENERATION INTRUSION DETECTION AND PREVENTION SYSTEMS FOR IT AND NETWORK SECURITY." ICTACT Journal on Communication Technology 14, no. 3 (2023): 2992–97. http://dx.doi.org/10.21917/ijct.2023.0445.

Full text

Abstract:

In cybersecurity, the constant evolution of threats demands the development of next-generation Intrusion Detection and Prevention Systems (IDPS) to safeguard IT infrastructure and networks effectively. This research embarks on the journey of designing an innovative IDPS using a Dense VGG classifier, fueled by IoT data as its primary input source. Our approach combines the robustness of the Dense VGG architecture with the rich information generated by Internet of Things (IoT) devices, enhancing the system ability to detect and prevent intrusions. We gather diverse IoT data from sensors and devices within the IT infrastructure, ensuring the availability of labeled data that signifies known intrusion events. After meticulous preprocessing and feature engineering, we adapt the Dense VGG model, originally designed for image classification, to work with tabular IoT data. Transfer learning techniques are applied, leveraging pre-trained VGG models to expedite convergence and enhance performance. Real-time data streaming mechanisms are established to seamlessly integrate IoT data, making the system proactive in identifying threats. Upon detection, the system can respond by isolating affected devices, blocking suspicious network traffic, or initiating incident response protocols. Continuous monitoring and evaluation ensure the system reliability, with key metrics serving as indicators of its efficacy. Deployment considerations, such as scalability and redundancy, guarantee the system readiness to handle the influx of IoT data. Furthermore, integration with other security tools and compliance with regulatory standards strengthen the system overall cybersecurity posture. The core of our system lies in its intrusion detection logic, a set of rules and thresholds that trigger alerts or preventive measures based on model predictions. In testing, our system demonstrated an impressive intrusion detection accuracy of over 95%, significantly reducing false positives.

APA, Harvard, Vancouver, ISO, and other styles

28

Jiang, Ya Ping, Shi Hui Cheng, and Yong Gan. "Network Security Prevention Model Based-Immune." Applied Mechanics and Materials 16-19 (October 2009): 881–85. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.881.

Full text

Abstract:

With the concepts of self, nonself, antibody, vaccine and antigen in an intrusion detection and prevention system presented in this paper, the architecture of network intrusion and prevention based on immune principle is proposed. The intrusion information gotten from current monitored network is encapsulated and sent to the neighbor network as bacterin; therefore the neighbor network can make use of the bacterin and predict the danger of network. The experimental results show that the new model not only actualizes an active prevention method but also improves the ability of intrusion detection and prevention than that of the traditional passive intrusion prevention systems.

APA, Harvard, Vancouver, ISO, and other styles

29

Erskine, Samuel Kofi. "Real-Time Large-Scale Intrusion Detection and Prevention System (IDPS) CICIoT Dataset Traffic Assessment Based on Deep Learning." Applied System Innovation 8, no. 2 (2025): 52. https://doi.org/10.3390/asi8020052.

Full text

Abstract:

This research utilizes machine learning (ML), and especially deep learning (DL), techniques for efficient feature extraction of intrusion attacks. We use DL to provide better learning and utilize machine learning multilayer perceptron (MLP) as an intrusion detection (IDS) and intrusion prevention (IPS) system (IDPS) method. We deploy DL and MLP together as DLMLP. DLMLP improves the high detection of all intrusion attack features on the Internet of Things (IoT) device dataset, known as the CICIoT2023 dataset. We reference the CICIoT2023 dataset from the Canadian Institute of Cybersecurity (CIC) IoT device dataset. Our proposed method, the deep learning multilayer perceptron intrusion detection and prevention system model (DLMIDPSM), provides IDPST (intrusion detection and prevention system topology) capability. We use our proposed IDPST to capture, analyze, and prevent all intrusion attacks in the dataset. Moreover, our proposed DLMIDPSM employs a combination of artificial neural networks, ANNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Consequently, this project aims to develop a robust real-time intrusion detection and prevention system model. DLMIDPSM can predict, detect, and prevent intrusion attacks in the CICIoT2023 IoT dataset, with a high accuracy of above 85% and a high precision rate of 99%. Comparing the DLMIDPSM to the other literature, deep learning models and machine learning (ML) models have used decision tree (DT) and support vector machine (SVM), achieving a detection and prevention rate of 81% accuracy with only 72% precision. Furthermore, this research project breaks new ground by incorporating combined machine learning and deep learning models with IDPS capability, known as ML and DLMIDPSMs. We train, validate, or test the ML and DLMIDPSMs on the CICIoT2023 dataset, which helps to achieve higher accuracy and precision than the other deep learning models discussed above. Thus, our proposed combined ML and DLMIDPSMs achieved higher intrusion detection and prevention based on the confusion matrix’s high-rate attack detection and prevention values.

APA, Harvard, Vancouver, ISO, and other styles

30

Erlansari, Aan, Funny Farady Coastera, and Afief Husamudin. "Early Intrusion Detection System (IDS) using Snort and Telegram approach." SISFORMA 7, no. 1 (2020): 21. http://dx.doi.org/10.24167/sisforma.v7i1.2629.

Full text

Abstract:

Computer network security is an important factor that must be considered. Guaranteed security can avoid losses caused by attacks on the network security system. The most common prevention against network attacks is to place an administrator, but problems will arise when the administrator is not supervising the network, so to overcome these problems a system called IDS (Intrusion Detection System) can detect suspicious activity on the network through automating the work functions of an administrator. Snort is one of the software that functions to find out the intrusion. Data packets that pass through network traffic will be analyzed. Data packets detected as intrusion will trigger alerts which are then stored in log files. Thus, administrators can find out intrusions that occur on computer networks, and the existence of instant messaging applications can help administrators to get realtime notifications, one of which is using the Telegram application. The results of this study are, Snort able to detect intrusion of attacks on computer networks and the system can send alerts from snort to administrators via telegram bot in real-time.

APA, Harvard, Vancouver, ISO, and other styles

31

Suryayusra, Suryayusra, and Dedi Irawan. "PERBANDINGAN INTRUSION PREVENTION SYSTEM (IPS) PADA LINUX UBUNTU DAN LINUX CENTOS." Jurnal Teknologi Informasi Mura 12, no. 02 (2020): 131–44. http://dx.doi.org/10.32767/jti.v12i02.1023.

Full text

Abstract:

Perkembangan teknologi yang Semakin hari semakin meningkat, kita di tuntut untuk meningkatkan system keamanan jaringan yang kita miliki, karena semakin mudahnya orang bisa mengetahui tentang hacking dan cracking dan juga didukung oleh tools yang mudah didapatkan secara gratis. Dan untuk mencegah hal itu kita harus megamankan jaringan yang kita gunakan, untuk mengamankan jaringan tersebut peneliti menggunakan sebuah metode keamanan yang bernama Intrusion Prevention System (IPS), merupakan media yang banyak digunakan dalam membangun sebuah system keamanan komputer, lalu IPS di gabungkan dengan menggunkan Teknik firewall dan metode Intrusioan Detection System, dalam penelitian ini penulis menggunakan sistem operasi Linux yaitu Ubuntu dan CentOS, karena linux merupakan software yang bersifat free/opensource sehingga untuk memperolehnya dapat diunduh secara gratis. Pada awalnya linux merupakan system operasi yang cocok untuk jaringan tapi sekarang linux sudah berubah menjadi system operasi yang tidak hanya handal dari segi jaringan dan server tapi juga sudah menjelma menjadi sistem operasi yang enak dipakai di lingkungan desktop baik untuk keperluan pribadi atau bahkan untuk perkantoran. Untuk mengamankan jaringan tersebut menggunakan sebuah mtode keamanan yaitu Intrusion Prevention System (IPS), juga dibantu dengan sebuah tools dalam sistem Operasi Linux yang berfungsi sebagai alat untuk melakukan filter (penyaring) terhadap lalulintas data (trafic), yaitu IPTables.  Technological developments are increasing day by day, we are required to improve our network security system, because the easier it is for people to find out about hacking and cracking and it is also supported by tools that are easily available for free. And to prevent that we have to secure the network that we use, to secure the network researchers use a security method called the Intrusion Prevention System (IPS), which is a medium that is widely used in building a computer security system, then IPS is combined with using techniques. firewall and Intrusioan Detection System method, in this study the author uses the Linux operating system, namely Ubuntu and CentOS, because Linux is a free / opensource software so that it can be downloaded for free. Initially, linux was an operating system suitable for networking, but now linux has turned into an operating system that is not only reliable in terms of networks and servers but has also been transformed into an operating system that is comfortable to use in a desktop environment for personal use or even for offices. To secure the network using a security method, namely the Intrusion Prevention System (IPS), it is also assisted by a tool in the Linux operating system which functions as a tool for filtering data traffic, namely IPTables

APA, Harvard, Vancouver, ISO, and other styles

32

Vargas Martínez, Cyntia, and Birgit Vogel-Heuser. "Towards Industrial Intrusion Prevention Systems: A Concept and Implementation for Reactive Protection." Applied Sciences 8, no. 12 (2018): 2460. http://dx.doi.org/10.3390/app8122460.

Full text

Abstract:

System intrusions violate the security of a system. In order to maintain it, it is necessary to decrease the chances of intrusions occurring or by detecting them as soon as they ensue in order to respond to them in a timely manner. These responses are divided in two types: passive or reactive responses. Passive responses are limited to only notification and alerting; whereas, reactive responses influence the intrusion by undoing or diminishing its consequences. Unfortunately, some reactive responses may influence the underlying system where the intrusion has occurred. This is especially a concern in the field of Industrial Automation Systems, as these systems are critical and have a well-defined set of operational requirements that must be maintained. Hence, automatic reactive responses are often not considered or are limited to human intervention. This paper addresses this issue by introducing a concept for reactive protection that integrates the automatic execution of active responses that do not influence the operation of the underlying Industrial Automation System. This concept takes into consideration architectural and security trends, as well as security and operational policies of Industrial Automation Systems. It also proposes a set of reactive actions that can be taken in the presence of intrusions in order to counteract them or diminish their effects. The feasibility and applicability of the presented concept for Industrial Automation Systems is supported by the implementation and evaluation of a prototypical Reactive Protection System.

APA, Harvard, Vancouver, ISO, and other styles

33

Benisha, R. B., and S. Raja Ratna. "Design of Intrusion Detection and Prevention in SCADA System for the Detection of Bias Injection Attacks." Security and Communication Networks 2019 (November 22, 2019): 1–12. http://dx.doi.org/10.1155/2019/1082485.

Full text

Abstract:

Intrusion detection and prevention system detects malicious activities that occur in the real-time SCADA systems. This system has a problem without a profound solution. The challenge of the existing intrusion detection is accuracy in the process of detecting the anomalies. In SCADA, wind turbine data are modified by the intruders and forged details are given to the server. To overcome this, the biased intrusion detection system is used for detecting the intrusion with encrypted date, time, and file location with less false-positive and false-negative rates and thereby preventing the SCADA system from further intrusion. It is done in three phases. First, Modified Grey Wolf Optimization (MGWO) is used to extract the features needed for classification and to find the best weight. Second, Entropy-based Extreme Learning Machine (EELM) is used to extort the features and detect the intruded data with its intruded time, file location, and date. Finally, the data are encrypted using the Hybrid Elliptical Curve Cryptography (HECC) to prevent further attack. Experimental results show better accuracy in both detection as well as prevention.

APA, Harvard, Vancouver, ISO, and other styles

34

Thenmozhi, K., and R. Sabin Begum. "Intrusion Prevention in Cloud Computing Using Blockchain." Data Analytics and Artificial Intelligence 4, no. 3 (2024): 8–13. http://dx.doi.org/10.46632/daai/4/3/2.

Full text

Abstract:

The integration of blockchain technology with cloud computing to establish a more secure and transparent Intrusion prevention mechanism. The limitations of traditional Intrusion prevention methods, including security, transparency, and scalability challenges. Blockchain technology has emerged as a promising solution to enhance Intrusion prevention and permissions in a tamper-proof and transparent ledger in cloud computing environments. Blockchain technology has the potential to revolutionize Intrusion prevention in cloud computing by providing a more secure, transparent, and scalable framework. Scalability is an issue since processing many Intrusion prevention transactions on the blockchain might cause network congestion and sluggish processing. Transactions take time to upload to the blockchain, which can delay realtime access choices. It takes skill to integrate and manage blockchain and cloud technologies together. Choosing the correct consensus mechanism affects system efficiency and security. Consider the costs of establishing and maintaining such a system and the difficulty of fixing faults owing to blockchain immutability. In this paper we highlighted the importance of Intrusion prevention in cloud computing, emphasizing the need for secure and transparent management of sensitive data and resources. It also underscores the limitations of traditional Intrusion prevention methods, which can lead to security breaches and unauthorized access. In conclusion, this paper presents a compelling argument for the integration of blockchain technology with cloud computing to establish a more secure and transparent Intrusion prevention mechanism. Keywords: Blockchain, Cloud Computing, Intrusion Prevention, Scalability, immutability

APA, Harvard, Vancouver, ISO, and other styles

35

Veerasingam, Prakaash, Shukor Abd Razak, Ahmad Faisal Amri Abidin, Mohamad Afendee Mohamed, and Siti Dhalila Mohd Satar. "INTRUSION DETECTION AND PREVENTION SYSTEM IN SME'S LOCAL NETWORK BY USING SURICATA." Malaysian Journal of Computing and Applied Mathematics 6, no. 1 (2023): 21–30. http://dx.doi.org/10.37231/myjcam.2023.6.1.88.

Full text

Abstract:

In the present era, Cybercriminals are increasingly focusing their attention on the local networks of SMEs. Due to the lack of resources and skilled workers in the cybersecurity field., SMEs struggle to prevent and detect fraudulent activities within their networks. To address this challenge, an Intrusion Detection and Prevention System (IDPS) is crucial for optimising network security in SMEs. This research paper explores the implementation of Suricata, an IDS/IPS tool, on a Raspberry Pi 2B embedded platform to create an effective IDPS for SMEs' the study demonstrates the viability of Suricata on low-budget IoT networks with low data traffic. Previous research has shown that Suricata outperforms other systems such as Snort in terms of accuracy and packet loss rate when running on multi-core configurations. The proposed solution offers real-time intrusion detection and prevention capabilities, protecting small business networks from unauthorised access and providing users with timely notifications of network attacks. With Suricata running on OPNsense, SMEs can enhance their network security and safeguard their valuable assets against intrusions.

APA, Harvard, Vancouver, ISO, and other styles

36

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.

Full text

Abstract:

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).

APA, Harvard, Vancouver, ISO, and other styles

37

Mahesh T R, V Vivek, and Vinoth Kumar. "Implementation of Machine Learning-Based Data Mining Techniques for IDS." International Journal of Information Technology, Research and Applications 2, no. 1 (2023): 7–13. http://dx.doi.org/10.59461/ijitra.v2i1.23.

Full text

Abstract:

The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract user behaviour patterns from network traffic data. Association rules and sequence rules are the main foundations of data mining used for intrusion detection. Given the Auto encoder algorithm's traditional method's bottleneck of frequent itemsets mining, we provide a Length-Decreasing Support to Identify Intrusion based on Data Mining, which is an upgraded Data Mining Techniques based on Machine Learning for IDS. Based on test results, it appears that the suggested strategy is successful

APA, Harvard, Vancouver, ISO, and other styles

38

Ms., Deepali D. Rane, Shraddha T. Shelar Mrs., and Vinod Mane Mr. "Survey on Detection and Prevention of Intrusion." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2500–2502. https://doi.org/10.31142/ijtsrd15660.

Full text

Abstract:

Intrusion Detection and Prevention System using GSM modem is proposed, which is designed to detect unwanted attempts of accessing, manipulating and or disabling computer system. If intruder cracks or guess the password then proposed system will deny access of system resources with the help of security question known to owner of the system. Proposed system will take snapshot of intruder using webcam. Unwanted attempts can be avoided by using cell phone and GSM modem. System will send alert message to authorized user on mobile phone. Authorized user can control or prevent own machine by sending commands through message like lock desktop PC, shutdown PC etc. Ms. Deepali D. Rane | Mrs. Shraddha T. Shelar | Mr. Vinod Mane "Survey on Detection and Prevention of Intrusion" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15660.pdf

APA, Harvard, Vancouver, ISO, and other styles

39

Tetskyi, Artem, та Artem Perepelitsyn. "Можливості використання апаратних прискорювачів у системах виявлення та запобігання вторгненням". Aerospace Technic and Technology, № 6 (21 листопада 2024): 94–102. https://doi.org/10.32620/aktt.2024.6.09.

Full text

Abstract:

The subject of this study is the capabilities of FPGA technology for cybersecurity solutions with the network interface accelerators of SmartNIC, as well as the technologies for building, deploying, supporting, and accelerating intrusion detection systems and intrusion prevention systems. The goal of this work is to increase the performance of the network protection components of modern datacenters using hardware network interface accelerator cards based on FPGA technology. The task is to analyze the classification of cyber threats, to analyze methods of detecting cyber threats, to analyze the capabilities of modern FPGA accelerator cards for the creation of SmartNICs, to propose the architecture for hardware implementation of intrusion prevention system based on FPGA accelerator cards, and to propose the sequence of steps for creation of hardware implementation of intrusion prevention system based on FPGA acceleration. According to the tasks, the following results were obtained. The analysis of the main categories of common cyberthreats that should be considered when creating systems is performed. Two main principles of intrusion detection including the signature method and the anomaly detection method are analyzed. The analysis of the possibilities of using FPGA accelerator cards for hardware acceleration of network interfaces and the creation of SmartNICs is performed. The architecture of hardware implementation of network interface components for intrusion prevention system based on FPGA accelerator cards in data centers is proposed. The sequence of steps for creation of FPGA-based implementation of intrusion prevention system is proposed. Conclusions. The scientific novelty of the obtained results is in the fact that the analysis of the specifics of cyberthreats of datacenters and capabilities of FPGA accelerator cards with support of high-speed network interfaces allows to propose the set of recommendations for the creation of intrusion detection systems and intrusion prevention systems with the transfer of work to hardware implementation, which will make it possible to offload the computing resources of server and thereby increase its performance. The software component of the solution provides the possibility of improvements and continuously updating the operating profile of the hardware component of such intrusion detection and intrusion prevention systems directly in the system.

APA, Harvard, Vancouver, ISO, and other styles

40

Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

Full text

Abstract:

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.

APA, Harvard, Vancouver, ISO, and other styles

41

Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.

Full text

Abstract:

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.

APA, Harvard, Vancouver, ISO, and other styles

42

Kurniawan, Rifky, and Fajar Prakoso. "Implementasi Metode IPS (Intrusion Prevention System) dan IDS (Intrusion Detection System) untuk Meningkatkan Keamanan Jaringan." SENTINEL 3, no. 1 (2020): 231–42. http://dx.doi.org/10.56622/sentineljournal.v3i1.20.

Full text

Abstract:

Intrusion Detection System (IDS) dapat didefinisikan sebagai kegiatan yang bersifat anomaly,incorrect, inappropriate yang terjadi di jaringan atau host. Dan IDS sendiri adalah sistemkeamanan yang bekerja bersama Firewall untuk mengatasi Intrusion. IDS mampu mendeteksipenyusup dan memberikan respon secara real time. Terdapat dua teknik yang digunakandalam IDS yaitu, NIDS (Network Based Intrusion Detection System) dan HIDS (Host BasedIntrusion Detection System). Pada percobaan kali ini IDS dibagun menggunakan perangkatlunak Snort. Snort merupakan Open Source Intrusion Detection System (IDS) yang digunakanuntuk pemantauan dan pencegahan terhadap gangguan pada jaringan komputer. Agarmempermudah administrator dalam melihat dan membaca hasil log dari setiap paket datayang masuk atau keluar maka menggunakan Basic Analysis and Security Engine (BASE). Padapercobaan ini PC Server menggunakan sistem operasi Linux Ubuntu 16.04 LTS. Pengujiandilakukan pada Local Area Network dengan topologi Star. Dimana hasil gangguan yangdisebabkan DoS dan Port Scanner dapat dikenali oleh Snort IDS dan menampilkan log secaralengkap, baik dari waktu, tanggal kejadian dan sumber IP Address dari pengganggu.

APA, Harvard, Vancouver, ISO, and other styles

43

Khadafi, Shah, Budanis Dwi Meilani, and Samsul Arifin. "SISTEM KEAMANAN OPEN CLOUD COMPUTING MENGGUNAKAN IDS (INTRUSION DETECTION SYSTEM) DAN IPS (INTRUSION PREVENTION SYSTEM)." Jurnal IPTEK 21, no. 2 (2017): 67. http://dx.doi.org/10.31284/j.iptek.2017.v21i2.207.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Atmadji, Ery Setiyawan Jullev, Bekti Maryuni Susanto, and Rahardian Wiratama. "Pemanfaatan IPTables Sebagai Intrusion Detection System (IDS) dan Intrusion Prevention System (IPS) Pada Linux Server." Teknika 6, no. 1 (2017): 19–23. http://dx.doi.org/10.34148/teknika.v6i1.55.

Full text

Abstract:

Keamanan jaringan menjadi hal yang penting untuk semua industri dan perusahaan untuk melindungi data dan informasi penting yang berada didalamnnya. Perlindungan keamanan dalam suatu jaringan umumnya berbasis pada keamanan transmisi data yang dibuat dan diaplikasikan untuk membantu mengamankan suatu jaringan tertentu. Untuk lebih mengoptimalkan pengambilan keputusan maka diperlukan sebuah mesin yang mampu berkolaborasi dengan database IDS maupun IPS, sehingga tipikal serangan yang sangat beragam dapat dipetakan dengan lebih optimal. Salah satu database yang mempunyai rule yang sudah ada adalah IPTABLES, hal ini dikarenakan pada IPTABLES terdapat fungsi firewall yang mampu menangani jenis serangan yang berlipat serta masif. Server yang akan digunakan adalah server dengan sistem operasi Linux. Sedangkan database serangan IDS yang digunakan adalah database KDD 99 yang sudah diakui sebagai salah satu database serangan yang sangat kompleks. Dengan pemanfaatan IPTABLES ini maka diharapkan keamanan server akan bisa dimonitor dengan lebih optimal. IPTABLES biasanya digunakan sebagai salah satu firewall yang digunakan pada server.

APA, Harvard, Vancouver, ISO, and other styles

45

Nycz, Mariusz, Mirosław Hajder, and Alicja Gerka. "New architecture of system intrusion detection and prevention." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 16, no. 2 (2017): 20. http://dx.doi.org/10.17951/ai.2016.16.2.20.

Full text

Abstract:

In this article there has been presented new intrusion detection and prevention algorithm implemented on Raspberry Pi platform. The paper begins with the presentation of research methodology in the field of Intrusion Detection Systems. Adequate supervision and control over network traffic is crucial for the security of information and communication technology. As a result of the limited budget allocated for the IT infrastructure of small businesses and the high price of dedicated solutions, many companies do not use mentioned systems. Therefore, in this order, there has been proposed monitoring solution based on the generally available Raspberry Pi platform. The paper is addressed to network administrators.

APA, Harvard, Vancouver, ISO, and other styles

46

Letou, Kopelo, Dhruwajita Devi, and Y. Jayanta Singh. "Host-based Intrusion Detection and Prevention System (HIDPS)." International Journal of Computer Applications 69, no. 26 (2013): 28–33. http://dx.doi.org/10.5120/12136-8419.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

MaqboolBeigh, Bilal, Uzair Bashir, and Manzoor Chahcoo. "Intrusion Detection and Prevention System: Issues and Challenges." International Journal of Computer Applications 76, no. 17 (2013): 26–30. http://dx.doi.org/10.5120/13340-0701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Jo, Jinyong, Heejin Jang, Kyungmin Lee, and JongUk Kong. "SDN-Based Intrusion Prevention System for Science DMZ." Journal of Korean Institute of Communications and Information Sciences 40, no. 6 (2015): 1070–80. http://dx.doi.org/10.7840/kics.2015.40.6.1070.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Dave, Sweta, and Prof Sandip Chauhan. "Intrusion Detection and Prevention System in IoT Environment." International Journal of Research in Advent Technology 7, no. 3 (2019): 1498–502. http://dx.doi.org/10.32622/ijrat.732019124.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

LYUBENOVA, Simona, Milen PETROV та Adelina ALEKSIEVA - PETROVA. "А Graph Database Intrusion Detection and Prevention System". Eurasia Proceedings of Science Technology Engineering and Mathematics 29 (15 грудня 2024): 182–91. https://doi.org/10.55549/epstem.1566169.

Full text

Abstract:

Network threats are perceived as a serious and current problem due to the presence of different types of attacks, the purpose of which is to penetrate the security of a certain system using vulnerabilities and fraud techniques. They can appear anywhere, making them more difficult to detect and prevent. The victims of such type of attacks are constantly increasing, resulting in great losses not only in financial terms, but also in breaches of data privacy and business processes. As a result, protecting confidential information from unpredictable attacks has become a pressing issue and a difficult task that would be impossible without the help of intrusion detection systems (IDS) and intrusion prevention systems (IPS). The goal of the paper is to propose and design general architecture and implement a prototype for protection of an existing network of devices by detecting and preventing threats through the extraction and analysis of information from the devices located in the network, with the necessary data being stored in a graph database offering the possibility of visualization. To implement device network protection, it is necessary to enable software tools that, based on certain rules, impose restrictions on devices on the network and prevent future malicious actions.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Intrusion Prevention System'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles