An Extensive Survey on Malware Detection and Prevention Mechanisms Using Advanced Technologies

Abstract

The exponential growth in malware attacks,especiallyransomware,isacriticalchallengeto digital infrastructure and global cybersecurity. Conventional signature-based detection techniques are less effective against sophisticated polymorphic and metamorphic malware. This paper offers a comprehensive survey of malware detection methods, with emphasis on Behavioral signature-baseddetection. It examines state-of-the-art technologies, such as dynamicanalysis,machinelearning,deeplearning,and generative adversarial networks (GANs), and compares them in terms of their efficacy in detecting malware based on behavior patterns instead of static code. It draws from 46 peer-reviewed papers and emphasizeskey findings, detection architectures, and innovations like RansomNet, DeepCodeLock, and PlausMal-GAN. The review ends by summarizing current limitations, issues such as Behavioral drift, and directions for the future in hybrid and intelligent malware detection systems.

Introduction

Malware threats are increasingly frequent and sophisticated, evolving from simple viruses to complex forms like ransomware and polymorphic malware. Traditional signature-based antivirus tools struggle against zero-day threats and advanced evasion tactics. Consequently, cybersecurity has shifted towards behavior-based malware detection using AI, machine learning (ML), deep learning (DL), and generative adversarial networks (GANs).

This survey reviews malware detection approaches from 2011 to 2024, focusing on behavioral signatures. It contrasts static signature-based methods with dynamic behavior-based detection, highlighting tools such as RansomNet and DeepCodeLock that improve ransomware detection via deep learning. Challenges include behavioral drift, adversarial evasion, high false positives, dataset scarcity, and explainability issues.

Detection techniques covered include static analysis, dynamic behavioral monitoring (system calls, API hooking, file/registry tracking), ML and DL models (SVMs, CNNs, LSTMs, transformers), GAN-based adversarial training, hardware-assisted monitoring, and honeypot/sandbox environments. Each method has unique strengths and weaknesses in accuracy, robustness, and resource demands.

Behavioral malware detection has diverse practical applications in finance, government, healthcare, mobile security, cloud/edge computing, enterprise networks, and industrial control systems. It offers more adaptive, context-aware protection than traditional signature-based systems, essential for combating modern cyber threats.

Conclusion

Malware continues to be one of the most pervasive and damaging cybersecurity threats in the digital era, with attackers constantly evolving their strategies to bypass traditional detection mechanisms. This paper presentedan extensive survey on malware detection techniqueswith a focus on behavioral signature-basedapproaches. The shift from static, signature-dependent detection to dynamic, behavior-driven models marks a critical evolution in cybersecurity practices. Our analysis reveals that behavioral detection offers significant advantages in identifying novel, polymorphic, and zero-day malware by monitoring runtime activities such as file access, systemcalls, and networkbehavior. It also supports the development of proactive, rather than reactive, security systems. Furthermore, the integration of machine learning, deep learning,andgenerativeadversarialnetworks(GANs) has opened new frontiers in malware classification and Behavioral pattern recognition. However, these models still face challenges, including adversarial attacks, explainability, high false positives, and the need forlarge, diverse datasets. This survey also highlighted innovative tools like MalHunter, DeepCodeLock, and PlausMal-GAN, which exemplify the state of the art in behavior-based malware detection. The comparison between various approaches emphasized the trade-offs between accuracy, speed, scalability, and complexity. In conclusion, behavior-based malware detection is no longer an experimental paradigm but a necessary evolution in modern threat intelligence. Continued research into adaptive models, explainable AI, edge computing, and collaborative threat sharing will be essential for building resilient, future-proof cybersecurity systems.

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Copyright

Copyright © 2025 Vishwanath Chiniwar, Prerana Joshi, Mr. Pavan Mitragotri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.