Jul 2026· International Conference Computing Methodologies and Communication· pp. 1187-1191· 0 citations· 17 references
Abstract
The rapid growth of zero-day malware attacks has resulted in the reduction of the effectiveness of traditional malware detection tools, which normally use signature-based or rule-based malware detection techniques. Traditional malware detection techniques have been ineffective in detecting new malware attacks or zero-day attacks before they occur or even after they occur if they have been encoded or obfuscated. As a result of these challenges, there is a need for an innovative solution for malware detection. In this work, an Adaptive Malware Detection System is proposed using machine learning with reinforcement learning for effective malware detection. The proposed system uses a random forest classifier for intelligent decision-making based on the analysis of data collected from the feature-based data. In addition, reinforcement learning is used for decision-making using a Q-learning algorithm for effective decision-making. The proposed system is capable of adapting to new malware attacks using reinforcement learning, which learns from past experiences. Moreover, an interactive visualization module has been added to the proposed system for better decision-making based on the analysis of decision patterns and reward trends. The proposed solution is effective for use in the latest malware attacks.
Malware has become one of the biggest threats to computer systems and digital networks, affecting individuals, businesses, and government organizations. Traditional malware detection methods mainly depend on signatures and predefined rules, making them less effective against newly developed and constantly evolving attacks. As cybercriminals continue to use advanced techniques such as code obfuscation and polymorphism, there is a growing need for smarter and faster detection methods. This project proposes a robust malware detection system using deep learning to identify both known and unknown malware with improved accuracy. The model automatically learns meaningful patterns from malware data without relying heavily on manual feature extraction. By analyzing the behavior and characteristics of malicious files, the proposed system can classify malware efficiently while reducing false alarms. The approach improves detection speed, enhances security, and supports real-time threat analysis. This makes it a reliable solution for protecting modern computer systems against rapidly changing cyber threats.
S.Srikar, G.Rajini· International Journal of Dat...· 0 citations
Experimental results demonstrate that the proposed approach achieves high classification performance while improving transparency in malware detection decisions, making it suitable for practical cybersecurity applications.
V. Padmapriya, S Uma, S Sumathi et al.· International journal of com...· 0 citations
A Hybrid Neural Network–Convolutional Neural Network (NN–CNN) Deep Learning Framework for malware detection, malware-family classification, and malware-variant identification and considers two important issues in practical malware detection: model explainability and generalization to previously unseen malware.
Chioma Grace Nwankwo, B. C. Amanze, Ikechukwu Amaefule· World Journal of Advanced Re...· 0 citations
Malware is a serious threat in the cybersecurity area because of its dynamic nature, the variety of malware families, stealth, propagation and the capability of evading traditional security products. Therefore, proper malware detection and classification are crucial for detecting malicious software and for securing computer systems from unauthorized access and data stealing, and for disrupting systems. This study covers all the bases when it comes to deep learning approaches for malware detection and classification. It covers the principles, different forms of malware, how to detect deep learning malware, how to represent data, obtaining features, and applications. The traditional detection methods are described with their drawbacks, namely based on signature, behavioral and heuristic methods. The report also delves into the methodologies used by deep learning to classify malware, namely CNNs and Bidirectional Long Short-Term Memory (BiLSTM) networks. BiLSTM models excel at learning sequential features from code-or behavior-related data, whereas CNN-based representation learning approaches excel at learning spatial features from malware representations. Moreover, the various detection techniques (static, dynamic and hybrid) are discussed so that their role in malware analysis can be understood. The survey identifies the current challenges and gaps in research and emphasizes the need for strong, scalable and adaptive deep-learning models to combat new malware threats and enhance cybersecurity protection.
Manjula Jain· International Journal of Cyb...· 0 citations
An intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance is presented, providing an effective and reliable solution for securing Android devices against evolving malware threats.
Aishwarya Eklar, G.Rajini· International Journal of Eng...· 0 citations
A novel malware binary to image transformation technique HilEnT is proposed based on a combination of Hilbert curve-based transformation of malware binary and the entropy feature comparison of malware file with benign and malware classes.
Rahul Kale, Thesath Wijayasiri, K. Fok et al.· 0 citations