A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security

• URL: http://arxiv.org/abs/2501.07801v1
• Date: Tue, 14 Jan 2025 02:57:20 GMT
• Title: A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
• Authors: Osvaldo Arreche, Mustafa Abdallah,
• Abstract summary: This paper applies and evaluating White-Box XAI techniques (particularly LRP, IG, and DeepLift) for NIDS via an end-to-end framework for neural network models. We assess its global and local scopes, and examine six distinct assessment measures (descriptive accuracy, sparsity, stability, robustness, efficiency, and completeness) The results show that using White-box XAI techniques scores high in robustness and completeness, which are crucial metrics for IDS.
• Score: 1.3022753212679383
• License:
• Abstract: New research focuses on creating artificial intelligence (AI) solutions for network intrusion detection systems (NIDS), drawing its inspiration from the ever-growing number of intrusions on networked systems, increasing its complexity and intelligibility. Hence, the use of explainable AI (XAI) techniques in real-world intrusion detection systems comes from the requirement to comprehend and elucidate black-box AI models to security analysts. In an effort to meet such requirements, this paper focuses on applying and evaluating White-Box XAI techniques (particularly LRP, IG, and DeepLift) for NIDS via an end-to-end framework for neural network models, using three widely used network intrusion datasets (NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021), assessing its global and local scopes, and examining six distinct assessment measures (descriptive accuracy, sparsity, stability, robustness, efficiency, and completeness). We also compare the performance of white-box XAI methods with black-box XAI methods. The results show that using White-box XAI techniques scores high in robustness and completeness, which are crucial metrics for IDS. Moreover, the source codes for the programs developed for our XAI evaluation framework are available to be improved and used by the research community.

Related papers

• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI. We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts. We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications [2.0681376988193843]
  "Black box" characteristic of AI models constrains interpretability, transparency, and reliability. This study presents a unified XAI evaluation framework to evaluate correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models.
  arXiv  Detail & Related papers  (2024-12-05T05:30:10Z)
• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• An Adaptive End-to-End IoT Security Framework Using Explainable AI and LLMs [1.9662978733004601]
  This paper presents an innovative framework for real-time IoT attack detection and response that leverages Machine Learning (ML), Explainable AI (XAI), and Large Language Models (LLM) Our end-to-end framework not only facilitates a seamless transition from model development to deployment but also represents a real-world application capability that is often lacking in existing research.
  arXiv  Detail & Related papers  (2024-09-20T03:09:23Z)
• Explainable AI for Enhancing Efficiency of DL-based Channel Estimation [1.0136215038345013]
  Support of artificial intelligence based decision-making is a key element in future 6G networks. In such applications, using AI as black-box models is risky and challenging. We propose a novel-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications.
  arXiv  Detail & Related papers  (2024-07-09T16:24:21Z)
• X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System [2.556190321164248]
  Using machine learning (ML) and deep learning (DL) models in Intrusion Detection Systems has led to a trust deficit due to their non-transparent decision-making. This paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data. Our approach achieves high accuracy with 99.47% in threat detection and provides clear, actionable explanations of its analytical outcomes.
  arXiv  Detail & Related papers  (2024-02-01T18:29:16Z)
• Representation Engineering: A Top-Down Approach to AI Transparency [132.0398250233924]
  We identify and characterize the emerging area of representation engineering (RepE) RepE places population-level representations, rather than neurons or circuits, at the center of analysis. We showcase how these methods can provide traction on a wide range of safety-relevant problems.
  arXiv  Detail & Related papers  (2023-10-02T17:59:07Z)
• AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios [51.94807626839365]
  We propose the attention-inspired numerical solver (AttNS) to solve differential equations due to limited data. AttNS is inspired by the effectiveness of attention modules in Residual Neural Networks (ResNet) in enhancing model generalization and robustness.
  arXiv  Detail & Related papers  (2023-02-05T01:39:21Z)
• Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method [12.466212057641933]
  We propose a novel framework called Attack-Inspired GAN (AI-GAN) to generate adversarial examples conditionally. Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly.
  arXiv  Detail & Related papers  (2021-11-19T05:54:14Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.