A hypergraph based Kohonen map for detecting intrusions over cyber–physical systems traffic
Autor: | V Subramaniyaswamy, V. S. Shankar Sriram, Sujeet S. Jagtap |
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Rok vydání: | 2021 |
Předmět: |
Self-organizing map
Computer Networks and Communications Computer science Payload (computing) Cyber-physical system 020206 networking & telecommunications 02 engineering and technology Intrusion detection system computer.software_genre Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Data mining computer Software |
Zdroj: | Future Generation Computer Systems. 119:84-109 |
ISSN: | 0167-739X |
DOI: | 10.1016/j.future.2021.02.001 |
Popis: | Cyber–Physical System acts as a cornerstone in Industry 4.0 by integrating information-technology, electrical, and mechanical engineering under the same crown. This cybernetic–mechatronic augmentation expanded the attack vectors in critical infrastructure’s network, which gained the attraction of both cyber-offenders and cybersecurity researchers. Though the recent research works focus on developing proficient cybersecurity mechanisms, they often fail to address the major challenges such as handling the unseen zero-day exploits and detecting data irregularities that result in a poor attack detection rate. Hence to address the aforementioned challenges, this research article proposes an intelligent multi-level intrusion detection system to detect data-abnormalities in process-control network packets. The proposed approach involves the following phases: (i) Bloom-filter based payload level detection, (ii) partition-based Kohonen mapping for learning abnormal data patterns using a deep version of Kohonen neural network enhanced by principal component analysis and partitioning property of Hypergraph, and (iii) BLOSOM – a hybrid anomaly detection model. The impact of the proposed approach has been validated with the high-dimensional and heterogeneous benchmark datasets obtained from Mississippi State University (Gas-pipeline dataset) and Singapore University of Technology and Design (Secure WAter Treatment dataset). The proposed approach outscores the existing State-of-the-art approaches in terms of Precision, Recall, F-Score & Classification Accuracy and found to be robust, scalable & computationally attractive. |
Databáze: | OpenAIRE |
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