Generative Adversarial Networks in Anomaly Detection and Malware Detection: A Comprehensive Survey

Yıl 2024, Cilt: 4 Sayı: 1, 18 - 35, 30.08.2024

Bishal Kc , Shushant Sapkota Ashish Adhikari

https://doi.org/10.54569/aair.1442665

Öz

The swiftly changing panorama of machine learning has observed first-rate leaps within the field of Generative Adversarial Networks (GANs). In the beginning, the implantation of a deep neural network seemed quite difficult and poses challenges. However, with the rapid development of huge processing power, different machine learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and GANs have emerged in the past few years. Following Ian Goodfellow’s proposed GANs model in 2014, there has been a huge increase in the research focused on Generative Adversarial Networks. In the present context, not only GANs are used in feature extraction, but it proves itself worthy in the domain of anomaly and malware detection having firmly established in this field. Therefore, in our research paper, we conducted a comprehensive survey of prior and current research attempts in anomaly and malware detection using GANs. This research paper aims to provides detailed insights to the reader about what types of GANs are used for anomaly and malware detection with a general overview of the different types of GANs. These results are provided by analyzing both past and present GAN surveys performed, along with detailed information regarding the datasets used in these surveyed papers. Furthermore, this paper also explores the potential future use of GANs to overcome the advancing threats and malware.

Anahtar Kelimeler

Generative Adversarial Networks (GAN), Network Security, Deep Neural Network (DNN), Research Survey, Threat detection, Malware detection, Adversarial examples

Kaynakça

• U. Bayer, A. Moser, C. Kruegel and E. Kirda, "Dynamic Analysis of Malicious Code," vol. 2, pp. 66-67, 2006; doi: 10.1007/s11416-006-0012-2.
• A. Petrosyan, "Annual number of malware attacks worldwide from 2015 to 2022," 2023. [Online]. Available: https://www.statista.com/statistics/873097/malware-attacks-per-year-worldwide/. [Accessed 2 2 2024].
• N. J. Palatty, "30+ Malware Statistics You Need To Know In 2024," Astra, 2023. [Online]. Available: https://www.getastra.com/blog/security-audit/malware-statistics/. [Accessed 2 2 2024].
• J. Brownlee, "How to develop an auxiliary classifier GAN (AC-GAN) from scratch with Keras," 2021. [Online]. Available: https://machinelearningmastery.com/how-to-develop-an-auxiliary-classifier-gan-ac-gan-from-s. [Accessed 2 2 2024].
• A. Dunmore, J. Jang-Jaccard, F. Sabrina and J. Kwak, "Generative Adversarial Networks for Malware Detection: a Survey," ArXiv, vol. abs/2302.08558, 2023; doi: 10.48550/arXiv.2302.08558.
• S. Gihon, "Ransomware Trends Q4 2023 Report," 2024. [Online]. Available: https://cyberint.com/blog/research/ransomware-trends-and-statistics-2023-report/. [Accessed 2 2 2024].
• W. Hu and Y. Tan, "Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN," ArXiv, vol. abs/1702.05983, 2017.
• T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford and X. Chen, "Improved Techniques for Training GANs," ArXiv, vol. abs/1606.03498, 2016.
• P. Isola, J.-Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5967-5976; doi: 10.1109/CVPR.2017.632.
• J. Ho and S. Ermon, "Generative Adversarial Imitation Learning," in Neural Information Processing Systems, 2016.
• A. Gharakhanian, "Generative Adversarial Networks – Hot Topic in Machine Learning," 2017. [Online]. Available: https://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. [Accessed 2 2 2024].
• J. He, Y. Nie and Z. Mao, "Analysis of Image Generation by different Generator in GANs," Journal of Physics: Conference Series, vol. 1903, 2021.
• A. Radford, L. Metz and S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," CoRR, vol. abs/1511.06434, 2015.
• I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville and Y. Bengio, "Generative Adversarial Nets," in Neural Information Processing Systems, 2014.
• M. Arjovsky and L. Bottou, "Towards Principled Methods for Training Generative Adversarial Networks," ArXiv, vol. abs/1701.04862, 2017.
• Z. Cai, Z. Xiong, H. Xu, P. Wang, W. Li and Y.-L. Pan, "Generative Adversarial Networks," ACM Computing Surveys (CSUR), vol. 54, pp. 1-38, 2021.
• H. Navidan, P. F. Moshiri, M. Nabati, R. Shahbazian, S. A. Ghorashi, V. Shah-Mansouri and D. Windridge, "Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation," ArXiv, vol. abs/2105.04184, 2021.
• D.-O. Won, Y.-N. Jang and S.-W. Lee, "PlausMal-GAN: Plausible Malware Training Based on Generative Adversarial Networks for Analogous Zero-Day Malware Detection," IEEE Transactions on Emerging Topics in Computing, vol. 11, pp. 82-94, 2023; doi: 10.1109/TETC.2022.3170544.
• I. K. Dutta, B. Ghosh, A. H. Carlson, M. W. Totaro and M. A. Bayoumi, "Generative Adversarial Networks in Security: A Survey," 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0399-0405, 2020.
• M. K. Prabakaran, P. M. Sundaram and A. D. Chandrasekar, "An enhanced deep learning‐based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders," IET Information Security, vol. 17, no. 3, pp. 315-551, 2023; doi: 10.1049/ise2.12106.
• T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford and X. Chen, "Improved Techniques for Training GANs," ArXiv, vol. abs/1606.03498, 2016.
• S. T. Barratt and S. Rishi, "A Note on the Inception Score," ArXiv, vol. abs/1801.01973, 2018.
• A. Borji, "Pros and Cons of GAN Evaluation Measures," ArXiv, vol. abs/1802.03446, 2018.
• A. Dunmore, J. Jang-Jaccard, F. Sabrina and J. Kwak, "A Comprehensive Survey of Generative Adversarial Networks (GANs) in Cybersecurity Intrusion Detection," IEEE Access, vol. 11, pp. 76071-76094, 2023; doi: 10.1109/ACCESS.2023.3296707.
• T. Che, Y. Li, A. P. Jacob, Y. Bengio and W. Li, "Mode Regularized Generative Adversarial Networks," ArXiv, vol. abs/1612.02136, 2017.
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818-2826; doi: 10.1109/CVPR.2016.308.
• J. G. Rohra, B. Perumal, S. J. Narayanan, P. Thakur and R. B. Bhatt, "User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization & Gravitational Search Algorithm with Neural Networks," in International Conference on Soft Computing for Problem Solving, 2016.
• F. Meneghello, N. D. Fabbro, D. Garlisi, I. Tinnirello and M. Rossi, "A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels," IEEE Communications Magazine, vol. 61, pp. 146-152, 2023.
• S. Yousefi, N. Hirokazu, S. Dayal, S. Ermon and S. Valaee, "A Survey on Behavior Recognition Using WiFi Channel State Information," IEEE Communications Magazine, vol. 55, pp. 98-104, 2017.
• "KDD Cup 1999 Data," 1999. [Online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. [Accessed 2 2 2024].
• "Historical Dataset: RADIOML 2016.10A.," 2016. [Online]. Available: https://www.deepsig.ai/datasets. [Accessed 2 2 2024].
• R. Ronen, M. Radu, C. Feuerstein, E. Yom-To and M. Ahmadi, "Microsoft Malware Classification Challenge," ArXiv, vol. abs/1802.10135, 2018.
• L. Nataraj, S. Karthikeyan, G. Jacobe and B. S. Manjunath, "Malware images: visualization and automatic classification," in Visualization for Computer Security, 2011.
• M. Mirza and S. Osindero, "Conditional Generative Adversarial Nets," ArXiv, vol. abs/1411.1784, 2014.
• X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever and P. Abbeel, "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets," in Neural Information Processing Systems, 2016.
• X. Li, L. Chen, L. Wang, P. Wu and W. Tong, "SCGAN: Disentangled Representation Learning by Adding Similarity Constraint on Generative Adversarial Nets," IEEE Access, vol. 7, pp. 147928-147938, 2019.
• "How CycleGAN Works?," [Online]. Available: https://developers.arcgis.com/python/guide/how-cyclegan-works/. [Accessed 2 2 2024].
• J.-Y. Zhu, T. Park, P. Isola and A. A. Efros, "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks," 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242-2251, 2017.
• P. Isola, J.-Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967-5976, 2016; doi: 10.1109/CVPR.2017.632.
• A. Odena, C. Olah and J. Shlens, "Conditional Image Synthesis with Auxiliary Classifier GANs," in International Conference on Machine Learning, 2016.
• R. Nagaraju and M. Stamp, "Auxiliary-Classifier GAN for Malware Analysis," ArXiv, vol. abs/2107.01620, 2021.
• J. Donahue, P. Krähenbühl and T. Darrell, "Adversarial Feature Learning," ArXiv, vol. abs/1605.09782, 2016.
• W. Xu, J. Jang-Jaccard, T. Liu, F. Sabrina and J. Kwak, "Improved bidirectional GAN-based approach for network intrusion detection using one-class classifier," MDPI (Basel, Switzerland), 2022; doi: 10.3390/computers11060085.
• M. Saito, E. Matsumoto and S. Saito, "Temporal Generative Adversarial Nets with Singular Value Clipping," in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2849-2858; doi: 10.1109/ICCV.2017.308.
• A. Munoz, M. Zolfaghari, M. Argus and T. Brox, "Multi-Variate Temporal GAN for Large Scale Video Generation," ArXiv, vol. abs/2004.01823, 2020.
• X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang and S. P. Smolley, "Least Squares Generative Adversarial Networks," in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2813-2821; doi: 10.1109/ICCV.2017.304.
• M. Arjovsky, S. Chintala and L. Bottou, "Wasserstein GAN," ArXiv, vol. abs/1701.07875.
• I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin and A. C. Courville, "Improved Training of Wasserstein GANs," in Neural Information Processing Systems, 2017.
• C. Wang, C. Xu, X. Yao and D. Tao, "Evolutionary Generative Adversarial Networks," IEEE Transactions on Evolutionary Computation, vol. 23, pp. 921-934, 2019; doi: 10.1109/TEVC.2019.2895748.
• D. Berthelot, T. Schumm and L. Metz, "BEGAN: Boundary Equilibrium Generative Adversarial Networks," ArXiv, vol. abs/1703.10717, 2017.
• T. Karras, T. Aila, S. Laine and J. Lehtinen, "Progressive Growing of GANs for Improved Quality, Stability, and Variation," ArXiv, vol. abs/1710.10196, 2017.
• A. Karnewar, O. Wang and R. S. Iyengar, "MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis," ArXiv, vol. abs/1903.06048, 2019.
• V. Chandola, A. Banerjee and K. Vipin, "Anomaly detection: A survey," ACM Comput. Surv., vol. 41, no. 3, pp. 15:1-15:58, 2009.
• T. Schlegl, P. Seeböck, S. Waldstein, G. Lang and U. Schmidt-Erfurth, "f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks," Medical Image Analysis, vol. 54, pp. 30-44, 2019.
• C.-S. Houssam Zenati and Foo, B. Lecouat, G. Manek and V. R. Chandrasekhar, "Efficient GAN-Based Anomaly Detection," ArXiv, vol. abs/1802.06222, 2018.
• D. Akçay and B. D. Akçay, "Effect of media content and media use habits on aggressive behaviors in the adolescents," The European Research Journal, vol. 5, no. 3, 2019; doi: 10.18621/eurj.395892.
• X. Luo, Y. Jiang, E. Wang and X. Men, "Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders," EURASIP Journal on Advances in Signal Processing, vol. 2022, pp. 1-13, 2022.
• X. Xia, X. Pan, N. Li, X. He, L. Ma, X. Zhang and N. Ding, "GAN-based anomaly detection: A review," Neurocomputing 493, 2022; dio: 10.1016/j.neucom.2021.12.093.
• C. Goetz and B. Humm, "Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints," Artificial Intelligence Enhanced Health Monitoring and Diagnostics), vol. 23, no. 9, p. 4207, 2023; doi: 10.3390/s23094207.
• W. Lim, S. K. C. Y. Sheng and B. T. T. C. C. L. Lau, "Future of generative adversarial networks (GAN) for anomaly detection in network security: A review," Computers & Security, 2024.
• M. Usama, M. Asim, S. Latif, J. Qadir and Ala-Al-Fuqaha, "Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems," in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019, pp. 78-83; doi: 10.1109/IWCMC.2019.8766353.
• L. Zilong, Y.-y. Shi and X. Zhi, "IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection," ArXiv, vol. abs/1809.02077, 2018.
• E. Seo, H. M. Song and H. K. Kim, "GIDS: GAN based Intrusion Detection System for In-Vehicle Network," in 2018 16th Annual Conference on Privacy, Security and Trust (PST), 2018, pp. 1-6; doi: 10.1109/PST.2018.8514157.
• M. A. Salem, S. Taheri and J.-S. Yuan, "Anomaly Generation Using Generative Adversarial Networks in Host-Based Intrusion Detection," 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 683-687, 2018.
• M. Shahpasand, L. Hamey, D. Vatsalan and M. Xue, "Adversarial Attacks on Mobile Malware Detection," in 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile (AI4Mobile), 2019, pp. 17-20; doi: 10.1109/AI4Mobile.[phone].
• J. Kargaard, T. Drange, A.-L. Kor, H. Twafik and E. Butterfield, "Defending IT systems against intelligent malware," in 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), 2018, pp. 411-417; doi: 10.1109/DESSERT.2018.8409169.
• J.-Y. Kim, S.-J. Bu and C. Sung-Bae, "Malware Detection Using Deep Transferred Generative Adversarial Networks," in International Conference on Neural Information Processing, 2017.
• J.-Y. Kim, S.-J. Bu and S.-B. Cho, "Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders," Inf. Sci., Vols. 460-461, pp. 83-102, 2018.
• A. Chowdhary, K. Jha and M. Zhao, "Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications," Sensors (Basel, Switzerland), vol. 23, 2023.
• M. Singh, P. Singh and P. Kumar, "An Analytical Study on Cross-Site Scripting," in 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 2020, pp. 1-6.
• R. Shobana and M. Suriakala, "A Thorough Study On Sql Injection Attack-Detection And Prevention Techniques And Research Issues," Journal of Information and Computational Science, vol. 10, no. 5, 2020.
• C. Binnie, "Password Cracking with Hashcat," in Linux Server Security: Hack and Defend, 2016; doi: 10.1002/9781119283096.ch9.
• K. Marchetti and P. Bodily, "John the Ripper: An Examination and Analysis of the Popular Hash Cracking Algorithm," in 2022 Intermountain Engineering, Technology and Computing (IETC), 2022, pp. 1-6. doi: 10.1109/IETC54973.2022.9796671; doi: 10.1109/IETC54973.2022.9796671
• M. Kuperberg, "Markov Models," in Dependability Metrics, 2005, pp. 48-55. doi: 10.1007/978-3-540-68947-8_8.
• Z. Chi and S. Geman, "Estimation of Probabilistic Context-Free Grammars," Computational Linguistics, vol. 24, no. 2, pp. 298-305, 1998.
• S. Nam, S. Jeon, H. Kim and J. Moon, "Recurrent GANs Password Cracker For IoT Password Security Enhancement †," Sensors (Basel, Switzerland), vol. 20, 2022.
• L. Monostori, "Cyber-physical production systems: roots from manufacturing science and technology," at - Automatisierungstechnik, vol. 63, pp. 766-776, 2015.
• S. R. Chhetri, A. B. Lopez, J. Wan and M. A. Al Faruque, "GAN-Sec: Generative Adversarial Network Modeling for the Security Analysis of Cyber-Physical Production Systems," in 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018, pp. 770-775; doi: 10.23919/DATE.2019.8715283.
• M. Rigaki and S. Garcia, "Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection," in 2018 IEEE Security and Privacy Workshops (SPW), 2018, pp. 70-75; doi: 10.1109/SPW.2018.00019.
• D. Saxena and J. Cao, "Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions," ACM Computing Surveys, vol. 54, no. 3, pp. 1-42, 2021; doi: 10.1145/3446374
• J. Li, Y. Liu and L. Qijie, "Generative Adversarial Network and Transfer Learning Based Fault Detection for Rotating Machinery with Imbalance Data Condition," Measurement Science and Technology, vol. 33, no. 4, 2022; doi: 10.1088/1361-6501/ac3945.
• A. Yang, C. Lu, J. Li, X. Huang, T. Ji, X. Li and Y. Sheng, "Application of meta-learning in cyberspace security: a survey," Digital Communications and Networks, vol. 9, no. 1, pp. 67-78, 2023; doi: 10.1016/j.dcan.2022.03.007.
• J. Soenen, K. Leuven, E. V. Wolputte, L. Perini, V. Vercruyssen, W. Meert, J. Davis and H. Blockeel, "The Effect of Hyperparameter Tuning on the Comparative Evaluation of Unsupervised Anomaly Detection Methods," 2021.
• X. Xuan, X. Pan, N. Li, X. He, L. Ma, X. Zhang and N. Ding, "GAN-based anomaly detection: A review," Neurocomputing, vol. 493, pp. 497-535, 2022; dio: 10.1016/j.neucom.2021.12.093.
• Z. Dehghanian, S. Saravani, M. Amirmazlaghani and M. Rahmati, "Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN," 2023.
• J. Fu, W. Lina, J. Ke, K. Yang and R. Yu, "GANAD:A GAN-based method for network anomaly detection," 2023; doi: 10.21203/rs.3.rs-2081269/v1.