Aug 2026· International Journal of Innovations in Science, Engineering And Management· 0 citations· 45 references
TL;DR
A comprehensive survey of how GAN-based methods are utilized for identifying unusual and harmful activities in cyber settings and addresses ongoing challenges and potential future avenues for employing GANs to counteract emerging cybersecurity threats.
Abstract
Generative Adversarial Networks or GANs, have become a significant approach in deep learning along with Con-volutional and Recurrent Neural Networks, due to improvements in computing technology and more advanced ways to train these frameworks or models. Since GANs were first introduced in 2014, their application has expanded beyond image generation to include critical security tasks like anomaly detection and malware analysis. This paper offers a comprehensive survey of how GAN-based methods are utilized for identifying unusual and harmful activities in cyber settings. It examines key variants of GANs relevant to this field, explains their fundamental architectures and training methods, and explains their integration into systems to detect anomalies and malware. Additionally, the paper catalogs publicly accessible datasets and evaluation metrics frequently used in the reviewed studies to illustrate common experimental methodologies and research directions. Finally, it addresses ongoing challenges and potential future avenues for employing GANs to counteract emerging cybersecurity threats, highlighting their importance in developing more proactive and robust security measures.
A systematic framework to enhance adversarial robustness is proposed, validated on the Malimg dataset and supersedes previous approaches by 13.15% in terms of the evasion rate and 37.34% in terms of retraining success.
Muhammad Arham Tariq, Allah Bux Sargano, Z. Habib et al.· International Journal of Inf...· 0 citations
Traditional detection techniques are struggling with ever-evolving malware threats like zero-day attacks, polymorphic malware, and adversarial samples. Current detection systems (signature-based, heuristic-based, conventional machine learning) fail to generalize to unseen/obfuscated malware variants. In an attempt to overcome these constraints, this paper investigates the possibilities of employing Generative Neural Networks (GNNs), in the form of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for the purpose of malware be haviour analysis and detection. We aim to create a novel framework for detecting malware samples that provides some of the best performance in terms of accuracy, precision, and recall while remaining robust to new or unseen malware. This work aims to firstly implement a generative learning-based approach and to measure its adversarial robustness in comparison with the four existing detection techniques. Experimental results show the accuracy, precision, recall of the proposed model is found to be 96.5%, 95.9%, 94.6% with the false positive rate of the model which can be negligible and it is 3.2% which outperforms the traditional machine learning and deep learning models. Our results demonstrate that GNN-based malware detection not only addresses the limitations of conventional approaches in terms of scalability but also provides a more robust and adaptable framework that could be integrated into future real-time threat intelligence and automated defense systems.
Wurood A. Jbara, N. A. Hussein· Al-Noor Journal of Engineeri...· 0 citations
Modern malware frameworks use advanced evasion techniques that bypass traditional detection methods; thus entail advanced analytical frameworks for comprehensive and robust analysis. This study provides a comparative analysis of several frameworks that utilize Explainable Artificial Intelligence (XAI), Generative Adversarial Networks (GANs), and Large Language Models (LLMs) to provide a dynamic approach to malware behaviour. The review consisted of five key metrics to assess these three frameworks: detection performance, explainability, robustness against adversarial attacks, behavioral interpretation, and automated reporting capabilities. The results indicate that Deep Learning models have attained high accuracy in detect-ing and identifying malicious code, but are not interpretable. The GAN-based frameworks are highly effective in generating adversarial samples for robustness testing. Conversely, LLM-based approaches are highly effective for generating automated forensic reports, but are not yet fully integrated into malware detection workflows. The analysis highlights a research gap pertaining to the lack of integrated frameworks for adversarial analysis, explainability and automated forensic reporting. This study proposes a unified approach of malware analysis incorpo-rating XAI, GAN and LLM to enable the development of more effective malware detection tools with more transparency, deeper analytical insight and advanced forensic decision-making.
A. Verma, Neha Gupta, Akash Saxena et al.· International Journal of Inn...· 0 citations
The review explores the key adversarial attack classes: poisoning, evasion, model extraction, model extraction, model inversion, and membership inference and also white-box, black-box, and grey-box threat models.
Ujjwal Deshmukh· International Journal of Inn...· 0 citations
A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out of a trained system.
Harsh Verma· International Journal of Sci...· 0 citations
The research methodology involved a systematic literature review using the Scopus database, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and focusing on recent advancements in attack and defence techniques.
