International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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EXPLAINX-MALDETECT: ENHANCING MALWARE DETECTCION WITH INTERPRETABLE Mrs. P. Swathi1 ,Lohith.M2, Brijesh Kumar goud. T3, Harshini.K4, Charitharth swamy.M5 1Assistant Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India
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Abstract – Explain X-Maledict is an AI-driven malware
However, despite their accuracy, these models operate as “black boxes,” providing no clarity behind their decisions. This lack of transparency makes it difficult for security analysts to interpret predictions, validate model reliability, or understand system behavior—limiting their practical adoption in cybersecurity workflows. To address this challenge, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME have gained importance. These frameworks make AI models interpretable by highlighting key features that influence a classification decision. By integrating explainability with ML-based malware detection, it becomes possible to combine accuracy with trust, improving both system reliability and user confidence. Explain X-Mal Detect is a hybrid AI-driven malware detection system designed to classify files as malicious or benign while providing clear interpretability for each prediction. The system utilizes multiple ML/DL models— including Random Forest, Decision Tree, Logistic Regression, MLP, and Deep Learning—to analyses static features of dataset samples. When a file is identified as malware, the system automatically deletes it to prevent potential harm. Through XAI visualizations, users gain insights into why a file was flagged, bridging the gap between high-performance detection and transparency. Overall, Explain X-Mal Detect enhances cyber security by offering accurate detection, human understandable explanations, and automated defensive actions. The system contributes toward building intelligent, interpretable, and practical malware detection solutions suitable for modern threat landscapes.
detection system designed to address the limitations of traditional signature-based antivirus tools, which struggle to detect rapidly evolving and zero-day threats. Existing malware detection systems often operate as black-box models, offering high accuracy but no transparency into how decisions are made. This lack of interpretability reduces trust and makes detailed threat analysis difficult for security professionals. Explain X-Mal Detect overcomes these challenges by integrating Machine Learning and Deep Learning models— such as Random Forest, Decision Tree, Logistic Regression, MLP, and a custom deep neural network—with Explainable AI (XAI) methods like SHAP and LIME. The system classifies files or dataset samples as benign or malicious and automatically removes malware-classified files to prevent system compromise. XAI techniques provide clear, feature-level explanations that help analysts understand why a file was flagged, ensuring transparency and informed decision-making. Technically, the system uses Python-based ML workflows, a modular architecture for easy updates, and visualization tools for interpretability. Overall, Explain XMal Detect improves detection accuracy, enhances trust through explainability, reduces manual analysis time, and contributes to a more secure and insight-driven malware defines ecosystem. Key Words: Explainable Artificial Intelligence (XAI), Malware Detection, Zero-Day Attacks, Machine Learning, Deep Learning, SHAP, LIME, Random Forest, Decision Tree, Logistic Regression, Multilayer Perceptron (MLP), Neural Networks, Feature Interpretability, Cybersecurity, AI-Driven Threat Detection, Transparent Security Systems.
1.1 Limitations of Traditional Malware Detection Systems
1. INTRODUCTION
Traditional malware detection systems mainly rely on signature-based techniques, where known malware patterns are stored in a database and matched against incoming files. While effective against previously identified threats, these systems fail when dealing with new, polymorphic, or zeroday malware, which continuously evolve to evade detection. Frequent signature updates are required, and even minor changes in malicious code can bypass traditional defenses. Additionally, conventional systems lack adaptability and struggle to scale against the rapidly growing volume of malware. This results in delayed detection, increased false negatives, and higher vulnerability to sophisticated cyberattacks, making traditional approaches insufficient for modern cybersecurity needs.
In today’s digital environment, malware has become one of the most persistent and sophisticated threats to computing systems. Traditional signature-based antivirus tools can no longer keep pace with rapidly evolving and obfuscated malware variants. These systems mainly rely on predefined signatures, making them ineffective against newly generated or zero-day threats. As a result, modern cybersecurity requires intelligent and adaptable detection mechanisms capable of identifying unknown threats with high accuracy. Machine Learning (ML) and Deep Learning (DL) models have emerged as powerful alternatives for malware detection due to their ability to learn complex patterns from large datasets.
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