SFC-NIDS: a sustainable and explainable flow filtering based concept drift-driven security approach for network introspection

Singh, Arjun; Mishra, Preeti; Vinod, P.; Gaur, Avantika; Conti, Mauro

doi:10.1007/s10586-024-04444-0

Cluster Comput

2024

DOI: 10.1007/s10586-024-04444-0

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SFC-NIDS: a sustainable and explainable flow filtering based concept drift-driven security approach for network introspection

Arjun Singh,

Preeti Mishra,

P. Vinod

et al.

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Cited by 1 publication

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Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

Mutalib,

Sabri,

Wahab

et al. 2024

Artif Intell Rev

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In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

show abstract

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

Mutalib,

Sabri,

Wahab

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

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SFC-NIDS: a sustainable and explainable flow filtering based concept drift-driven security approach for network introspection

Cited by 1 publication

References 38 publications

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

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