Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Chadha, Kriti; Jain, Sushma

doi:10.1007/978-3-319-11227-5_17

Cited by 11 publications

(1 citation statement)

References 14 publications

(17 reference statements)

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“…Sinclair et al [128] extract rules using a DT and a Genetic Algorithm (GA) for improving the performance of the IDS model. The authors in [129] and [130] focus on optimizing the IDS model by extracting rules using a GA. To add transparency to the decision process, Dias et al [118] proposed an interpretable and explainable hybrid intrusion detection system. The proposed system integrates expert-written rules and dynamic knowledge generated by a DT algorithm.…”

Section: ) Decision Tree and Rule Basedmentioning

confidence: 99%

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

et al. 2022

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The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network flow that would otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC analysts rely on these systems to make decisions about the detected threats. However, IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This creates a barrier for CSoC analysts, as they are unable to improve their decisions based on the model's predictions. One solution to this problem is to design explainable IDS (X-IDS). This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS. In particular, we discuss black box and white box approaches comprehensively. We also present the tradeoff between these approaches in terms of their performance and ability to produce explanations. Furthermore, we propose a generic architecture that considers human-in-the-loop which can be used as a guideline when designing an X-IDS. Research recommendations are given from three critical viewpoints: the need to define explainability for IDS, the need to create explanations tailored to various stakeholders, and the need to design metrics to evaluate explanations.INDEX TERMS Explainable intrusion detection systems, explainable artificial intelligence, machine learning, deep learning, white box, black box, explainability, cybersecurity.

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Section: ) Decision Tree and Rule Basedmentioning

confidence: 99%