2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 1 , PP: 129-143, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Amjad Hijazi 1 * , Nizar Alhafez 2 , Iyad Al-khayat 3
Doi: https://doi.org/10.54216/JISIoT.120110
In the realm of cybersecurity, the incessant evolution of network attacks necessitates advanced and robust intrusion detection systems (IDS). The major issues with these systems are numerous: false positive/negative alarms, delayed response and detection time, size of processed data, adaptability to future threats, scalability of the system, difficulty in detecting distributed attacks, and downtime (fault tolerance). We propose a system that introduces a distributed framework aimed at enhancing network security by effectively identifying subtle deviations from normal network behavior. This is achieved through transfer learning based on artificial neural networks, and support vector machine (SVM), capitalizing on their complementary strengths in recognizing complex patterns and addressing high-dimensional datasets. To validate the efficacy of the proposed approach, the NSL-KDD dataset is utilized within a distributed IDS architecture. It consists of several intrusion detection nodes representing subnetworks. A node consists of two agents that work collaboratively. A way is proposed to avoid interference between analysis agents: the network agents manager monitors the functioning of the nodes and displays the results of each vulnerability-detecting node in each subnet separately. Such communication between agents should reduce FPAS (false positive alarms) significantly. The Detection engine extracts relevant features of network attacks to solve the problem of SVM in processing huge sizes of data and detect adaptive future threats to detect famous distributed denial of services (DDOS) attacks in real-time. The system is highly scalable by increasing the number of intrusion detection system nodes if necessary. Central processing is avoided to circumvent a system failure situation, where processing and decision-making take place at the detection node level within each subnet.
distributed intrusion detection system , network security , agents, denial of service , artificial neural networks , support vector machine.
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