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Saleh, F., Dadashtabar Ahmadi, K., Keyvanrad, M. (2024). DEKF-Driven Few-Shot Class Imbalance Learning to Enhance Cyber Attack Detection. Electronic and Cyber Defense, 12(3), 119-137.
Fatat Saleh; Kourosh Dadashtabar Ahmadi; Mohammad Ali Keyvanrad. "DEKF-Driven Few-Shot Class Imbalance Learning to Enhance Cyber Attack Detection". Electronic and Cyber Defense, 12, 3, 2024, 119-137.
Saleh, F., Dadashtabar Ahmadi, K., Keyvanrad, M. (2024). 'DEKF-Driven Few-Shot Class Imbalance Learning to Enhance Cyber Attack Detection', Electronic and Cyber Defense, 12(3), pp. 119-137.
Saleh, F., Dadashtabar Ahmadi, K., Keyvanrad, M. DEKF-Driven Few-Shot Class Imbalance Learning to Enhance Cyber Attack Detection. Electronic and Cyber Defense, 2024; 12(3): 119-137.

DEKF-Driven Few-Shot Class Imbalance Learning to Enhance Cyber Attack Detection

پدافند الکترونیکی و سایبری
Article 10, Volume 12, Issue 3 - Serial Number 47, November 2024, Pages 119-137    PDF (1.43 M)
Document Type: Original Article
Authors
Fatat Saleh1; Kourosh Dadashtabar Ahmadi* 2; Mohammad Ali Keyvanrad2
1PhD student, Malek Ashtar University of Technology , Tehran, Iran
2Assistant Professor, Malek Ashtar University of Technology, Tehran, Iran
Receive Date: 23 May 2024,  Revise Date: 06 September 2024,  Accept Date: 07 October 2024 
Abstract
The escalating use of networks and the internet has led to a surge in cyber threats, making it imperative to develop sophisticated intrusion detection systems (IDS) capable of safeguarding against these malicious intrusions. While machine learning techniques have been extensively employed to enhance IDS, challenges persist, notably in handling imbalanced datasets and rare attack detection such as R2L and U2R due to the small number of their samples in the training dataset. Imbalanced datasets, a common challenge in IDS evaluation, often skew toward majority classes, hindering the detection of minority class attacks. Existing machine learning classifiers, primarily accuracy-driven, struggle to excel at identifying rare attacks, which are often more catastrophic. Moreover, overlapping classes complicate feature selection, further impeding accurate detection. To tackle these challenges, this article proposes a solution rooted in Few-Shot Learning, particularly MAML. Traditional MAML has limitations, including slow convergence and computational demands. To enhance MAML's performance, the article introduces the Node Decoupled Extended Kalman Filter (NDEKF) as an alternative to gradient descent in the inner loop. NDEKF optimizes MAML training, offering faster convergence and improved generalization. The DEKF (Decoupled Extended Kalman Filter) variant simplifies calculations, making it suitable for deep neural networks. The combination of MAML and NDEKF, termed NDEKF-based MAML, is applied to address the imbalanced data problem in IDS. The proposed approach is evaluated on the NSL-KDD dataset, demonstrating its potential to improve rare attack detection in intrusion detection systems. By adopting this approach, we achieved improved convergence speed, enhanced ability to generalize, and higher accuracy compared to the original MAML algorithm when dealing with a sparse and unstable dataset such as NSL-KDD. Particularly, our framework demonstrated significant advancements in accurately detecting rare U2R and R2L attacks. The accuracy rates for R2L and U2R attacks using our proposed framework surpassed those of the original MAML, increasing from 61% to 75% and from 51% to 66%, respectively, even with a reduced number of training epochs.
Keywords
: Intrusion detection system; Class Imbalance Learning; Few Shot Learning MAML; Decoupled Extended Kalman Filter
Main Subjects
Cyber defense
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