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.<n>We assess its global and local scopes, and examine six distinct assessment measures (descriptive accuracy, sparsity, stability, robustness, efficiency, and completeness)<n>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: http://creativecommons.org/licenses/by/4.0/
• 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

• White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
  We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
  arXiv  Detail & Related papers  (2025-07-11T12:39:25Z)
• Evaluating explainable AI for deep learning-based network intrusion detection system alert classification [0.7864304771129751]
  A Network Intrusion Detection System (NIDS) monitors networks for cyber attacks and other unwanted activities.<n>NIDS solutions often generate an overwhelming number of alerts daily, making it challenging for analysts to prioritize high-priority threats.<n>This study highlights the critical need for explainable artificial intelligence (XAI) in NIDS alert classification to improve trust and interpretability.
  arXiv  Detail & Related papers  (2025-06-09T15:53:30Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks [0.0]
  This study introduces an Explainable and Lightweight AI (ELAI) framework designed for real-time cyber threat detection in edge networks. Our approach integrates interpretable machine learning algorithms with optimized lightweight deep learning techniques, ensuring both transparency and computational efficiency. We evaluate ELAI using benchmark cybersecurity datasets, such as CICIDS and UNSW-NB15, assessing its performance across diverse cyberattack scenarios.
  arXiv  Detail & Related papers  (2025-04-18T23:45:39Z)
• 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)
• 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)
• Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution [38.53065398127086]
  This study investigates the potential of feature attribution methods to filter out uninformative features in input data for regression problems. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
  arXiv  Detail & Related papers  (2024-09-25T09:50:51Z)
• 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)
• Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems [37.02462866600066]
  Evolutionary computation (EC) offers significant potential to contribute to explainable AI (XAI) This paper provides an introduction to XAI and reviews current techniques for explaining machine learning models. We then explore how EC can be leveraged in XAI and examine existing XAI approaches that incorporate EC techniques.
  arXiv  Detail & Related papers  (2024-06-12T02:06:24Z)
• 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)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• 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.