Deep Learning for Zero-Day Flash Malware Detection: Prospective and Challenges

The rise of zero-day Flash malware has introduced significant security challenges due to its ability to exploit previously unknown vulnerabilities and evade traditional detection systems. This paper presents a novel deep learning-based approach leveraging a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to detect zero-day Flash malware effectively. Unlike conventional signature-based or heuristic detection mechanisms, our method automatically extracts and learns both spatial and temporal features from Flash file samples to improve detection accuracy and resilience against evasion techniques. The model was trained and evaluated on a robust, diversified dataset consisting of benign and malicious Flash samples, demonstrating superior performance compared to existing methods. Performance evaluation was conducted using precision, recall, F1-score, and ROC-AUC metrics. The experimental results show a detection accuracy of 97.5%, with a significantly reduced false positive rate. This study highlights the potential of deep learning, especially hybrid architectures, in addressing the evolving threat of zero-day malware. It further opens new avenues for real-time, intelligent malware detection systems applicable in broader cybersecurity contexts.

Zero-day malware, Flash exploits

1. Symantec, "Internet Security Threat Report," Symantec Corporation, 2020. [Google Scholar] [Crossref]

2. Adobe, "The End of Flash," Adobe Flash Player EOL General Information Page, 2021. [Google Scholar] [Crossref]

3. N. Ye et al., "A Survey on Malware Detection Using Data Mining Techniques," ACM Computing Surveys, vol. 50, no. 3, 2017. [Google Scholar] [Crossref]

4. Bayer, U., Comparetti, P.M. and Kruegel, C,"Scalable, Behavior-Based Malware Clustering," NDSS, 2009. [Google Scholar] [Crossref]

5. Aburomman, N. S. and Khalil, M. B. I. "A Novel SVM-kNN-PSO Ensemble Method for Intrusion Detection," Applied Soft Computing, vol. 38, pp. 360–372, 2016. [Google Scholar] [Crossref]

6. Saxe, H. and Berlin, K. "Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features," 10th International Conference on Malicious and Unwanted Software, 2015. [Google Scholar] [Crossref]

7. Rabiner, L. R. "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, no. 2, 1989. [Google Scholar] [Crossref]

8. Hochreiter S. and Schmidhuber, J. "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [Google Scholar] [Crossref]

9. Pascanu, R. Mikolov,T. and Bengio,Y "On the Difficulty of Training Recurrent Neural Networks," ICML, 2013. [Google Scholar] [Crossref]

10. Gibert, M., Mateu, C. and Planes, J. "The Rise of Machine Learning for Detection and Classification of Malware: Research Developments, Trends and Challenges," Journal of Network and Computer Applications, vol. 153, 2020. [Google Scholar] [Crossref]

11. Berman S. "Malicious Macro Detection Using Neural Networks," Black Hat USA, 2017. [Google Scholar] [Crossref]

12. Lundberg S. M. and Lee, S. "A Unified Approach to Interpreting Model Predictions," Advances in Neural Information Processing Systems, vol. 30, 2017. [Google Scholar] [Crossref]

13. Williams R. J. and Zipser, D. "A Learning Algorithm for Continually Running Fully Recurrent Neural Networks," Neural Computation, vol. 1, no. 2, pp. 270–280, 1989. [Google Scholar] [Crossref]

14. Vigna G. "Comparative Analysis of Malware Detection Techniques," IEEE Security & Privacy, vol. 13, no. 2, 2015. [Google Scholar] [Crossref]

15. Anderson, B., Quist, D. and Storlie, D. "Graph-Based Malware Detection Using Dynamic Analysis," Journal of Computer Virology and Hacking Techniques, vol. 9, no. 4, 2013. [Google Scholar] [Crossref]

16. Mohaisen, A. and Alrawi, O. "AvMeter: Evaluating the Efficacy of Antivirus Programs on Zero-Day Malware," DIMVA, 2014. [Google Scholar] [Crossref]

17. Tobiyama, S., Yamaguchi, Y., Shimada, H. Ikuse, T., and Yagi,T., "Malware Detection with Deep Neural Network Using Process Behavior," 2016 IEEE 40th Annual Computer Software and Applications Conference. [Google Scholar] [Crossref]

18. Hou, S. Saas, A., Chen, L., and Ye, Y. "Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs," in 2018 IEEE/WIC/ACM International Conference on Web Intelligence. [Google Scholar] [Crossref]

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