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p-Index From 2021 - 2026
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This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Raghad Yousif
Erbil Polytechnic University
Computer Science & IT Electrical & Electronics Engineering Engineering

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An Extensive Analysis and Taxonomy of Explainable Artificial Intelligence for Malware Identification Dauan Aziz; Firas Amien; Raghad Yousif
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i3.5143

Abstract

As malware continues to evolve in sophistication and scale, traditional detection methods struggle to keep pace, especially when facing obfuscated or zero-day threats. In response, Machine Learning (ML) and Deep Learning (DL) techniques have shown significant promise in enhancing malware detection through pattern recognition and anomaly classification. However, their increasing complexity has introduced major interpretability challenges, particularly in high-stakes cybersecurity contexts. This paper provides a comprehensive survey of eXplainable Artificial Intelligence (XAI) methods applied to malware detection across diverse computing platforms, including Windows PE files, PDF, Linux, and hardware-based systems. We propose a novel taxonomy that categorizes explainable malware detection approaches by model transparency, explanation technique (model-agnostic or model-specific), and deployment environment. We also discuss major trends, highlight underexplored domains, and outline future research directions aimed at enhancing real-time interpretability, adversarial robustness, and human-in-the-loop integration. This work aims to bridge the gap between high-performance malware detection models and actionable, transparent security decision-making.
A Hybrid Deep Learning Framework for Malware Detection Using Metaheuristic Feature Selection and Explainable AI: A Comprehensive Literature Review Dauan Aziz; Firas Amien; Raghad Yousif
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i3.5152

Abstract

Malware, including ransomware, trojans, rootkits, spyware, and advanced persistent threats (APTs), poses a growing challenge to modern computing systems. Traditional detection methods, such as signature- and heuristic-based approaches, struggle to detect polymorphic, metamorphic, and zero-day malware. Deep learning has emerged as a powerful solution due to its ability to automatically learn hierarchical features. However, key challenges remain: lack of model transparency, high-dimensional feature redundancy from multimodal analysis, and poor cross-dataset generalization. This paper presents a systematic literature review of state-of-the-art malware detection techniques published between 2020 and 2025, covering static, dynamic, visualization-based, and deep learning approaches (e.g., CNN, LSTM, BiLSTM, and hybrid models), along with metaheuristic feature selection and Explainable AI (XAI) methods such as SHAP, Grad-CAM, and LIME. Analysis of 35 studies identifies critical gaps, particularly the absence of integrated metaheuristic optimization and XAI-driven hybrid frameworks, motivating future research directions.
Co-Authors Dauan Aziz Firas Amien