Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges

• URL: http://arxiv.org/abs/2409.13723v2
• Date: Fri, 07 Feb 2025 16:44:06 GMT
• Title: Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges
• Authors: Harikha Manthena, Shaghayegh Shajarian, Jeffrey Kimmell, Mahmoud Abdelsalam, Sajad Khorsandroo, Maanak Gupta,
• Abstract summary: Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. This survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.
• Score: 0.0
• License:
• Abstract: Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy; however, their lack of transparency poses a significant challenge. Traditional black-box models often fail to provide interpretable justifications for their predictions, limiting their adoption in security-critical environments where understanding the reasoning behind a detection is essential for threat mitigation and response. Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. Additionally, we explore recent advancements and highlight open research challenges in the field of explainable malware analysis. By providing a structured overview of XAI-driven malware detection approaches, this survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.

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)
• Survey on AI-Generated Media Detection: From Non-MLLM to MLLM [51.91311158085973]
  Methods for detecting AI-generated media have evolved rapidly. General-purpose detectors based on MLLMs integrate authenticity verification, explainability, and localization capabilities. Ethical and security considerations have emerged as critical global concerns.
  arXiv  Detail & Related papers  (2025-02-07T12:18:20Z)
• Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines [0.0]
  This study addresses the need for effective malware detection strategies by leveraging Machine Learning (ML) techniques. Our research aims to develop an advanced ML model that accurately predicts malware vulnerabilities based on the specific conditions of individual machines.
  arXiv  Detail & Related papers  (2025-01-05T10:04:58Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis [53.24804865821692]
  This study introduces a taxonomy for log anomalies and explores automated data labeling to mitigate labeling challenges. The study envisions a future where root cause analysis follows anomaly detection, unraveling the underlying triggers of anomalies.
  arXiv  Detail & Related papers  (2023-12-22T15:04:20Z)
• Adversarial attacks and defenses in explainable artificial intelligence: A survey [11.541601343587917]
  Recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models.
  arXiv  Detail & Related papers  (2023-06-06T09:53:39Z)
• Harnessing the Speed and Accuracy of Machine Learning to Advance Cybersecurity [0.0]
  Traditional signature-based methods of malware detection have limitations in detecting complex threats. In recent years, machine learning has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are difficult for humans to identify.
  arXiv  Detail & Related papers  (2023-02-24T02:42:38Z)
• Practical Machine Learning Safety: A Survey and Primer [81.73857913779534]
  Open-world deployment of Machine Learning algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities. New models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects.
  arXiv  Detail & Related papers  (2021-06-09T05:56:42Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Towards interpreting ML-based automated malware detection models: a survey [4.721069729610892]
  Most of the existing machine learning models are black-box, which made their pre-diction results undependable. This paper aims to examine and categorize the existing researches on ML-based malware detector interpretability.
  arXiv  Detail & Related papers  (2021-01-15T17:34:40Z)
• Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis [2.6397379133308214]
  cybersecurity analysts always prefer solutions that are as interpretable and understandable as rule-based or signature-based detection. The objective of this paper is to evaluate the current state-of-the-art ML models interpretability techniques when applied to ML-based malware detectors.
  arXiv  Detail & Related papers  (2020-01-27T19:10:50Z)

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.