Abstract
This chapter focuses on online malware detection techniques in cloud IaaS using machine learning and discusses comparative analysis on the performance metrics of various deep learning models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
NS2 tool manual. http://www.isi.edu/nsnam/ns/doc/node509.html.
References
Abdelsalam, Mahmoud, et al. 2019. Online malware detection in cloud auto-scaling systems using shallow convolutional neural networks. In Proceedings of IFIP annual conference on data and applications security and privacy. Berlin: Springer.
Abdelsalam, Mahmoud, Ram Krishnan, Yufei Huang, and Ravi Sandhu. 2018. Malware detection in cloud infrastructures using convolutional neural networks. In Proceedings of IEEE international conference on cloud computing, 162–169.
Abdelsalam, Mahmoud, Ram Krishnan, and Ravi Sandhu. 2017. Clustering-based IaaS cloud monitoring. In Proceedings of IEEE international conference on cloud computing (CLOUD), 672–679.
Alazab, Mamoun, et al. 2011. Zero-day malware detection based on supervised learning algorithms of API call signatures. In Proceedings of the Australasian data mining conference, 171–182, AUS. Australian Computer Society, Inc.
Alazab, Mamoun, Robert Layton, Sitalakshmi Venkatraman, and Paul Watters. 2010. Malware detection based on structural and behavioural features of API calls. In Proceedings of the 1st International cyber resilience conference ed. Craig Valli, 1–10. Edith Cowan University.
Alazab, Mamoun, Sitalakshmi Venkataraman, and Paul Watters. 2010. Towards understanding malware behaviour by the extraction of API calls. In 2010 second cybercrime and trustworthy computing workshop, 52–59. IEEE.
Azab, Ahmad, Mamoun Alazab, and Mahdi Aiash. 2016. Machine learning based botnet identification traffic. In 2016 IEEE Trustcom/BigDataSE/ISPA, 1788–1794. IEEE.
Azmandian, Fatemeh, et al. 2011. Virtual machine monitor-based lightweight intrusion detection. ACM SIGOPS Operating Systems Review 45 (2): 38–53.
Dahbur, Kamal, Bassil Mohammad, and Ahmad Bisher Tarakji. 2011. A survey of risks, threats and vulnerabilities in cloud computing. In Proceedings of the 2011 international conference on intelligent semantic web-services and applications, 1–6.
Dawson, Joel A., et al. 2018. Phase space detection of virtual machine cyber events through hypervisor-level system call analysis. In Proceedings of IEEE international conference on data intelligence and security, ICDIS, 159–167.
Demme, John, et al. 2013. On the feasibility of online malware detection with performance counters. ACM SIGARCH Computer Architecture News 41 (3): 559–570.
Dini, Gianluca, et al. 2012. Madam: A multi-level anomaly detector for android malware. In Computer Network Security, ed. Igor Kotenko, and Victor Skormin, 240–253. Berlin: Springer.
Fan, Yujie, Yanfang Ye, and Lifei Chen. 2016. Malicious sequential pattern mining for automatic malware detection. Expert Systems with Applications 52: 16–25.
Firdausi, Ivan, et al. 2010. Analysis of machine learning techniques used in behavior-based malware detection. In Proceedings of IEEE international conference on advances in computing, control, and telecommunication technologies, 201–203.
Gholami, Ali, and Erwin Laure. 2016. Security and privacy of sensitive data in cloud computing: A survey of recent developments. arXiv:1601.01498.
Grobauer, Bernd, Tobias Walloschek, and Elmar Stocker. 2010. Understanding cloud computing vulnerabilities. IEEE Security & Privacy 9 (2): 50–57.
Gruschka, Nils, et al. 2010. Attack surfaces: A taxonomy for attacks on cloud services. In Proceedings of IEEE international conference on cloud computing, 276–279.
Jensen, Meiko, Jörg Schwenk, Nils Gruschka, and Luigi Lo Iacono. 2009. On technical security issues in cloud computing. In 2009 IEEE international conference on cloud computing, 109–116. IEEE.
Khasawneh, Khaled N., et al. 2015. Ensemble learning for low-level hardware-supported malware detection. In Proceedings of international symposium on recent advances in intrusion detection, 3–25. Berlin: Springer.
Luckett, P., et al. 2016. Neural network analysis of system call timing for rootkit detection, 1–6. In Proceedings of Cybersecurity symposium, CYBERSEC, April.
McDole, Andrew, Mahmoud Abdelsalam, Maanak Gupta, and Sudip Mittal. 2020. Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS. arXiv:2002.06383.
Mell, Peter, and Tim Grance. 2011. The NIST definition of cloud computing. https://csrc.nist.gov/publications/detail/sp/800-145/final.
Piplai, Aritran, Sudip Mittal, Mahmoud Abdelsalam, Maanak Gupta, Anupam Joshi, and Tim Finin. 2020. Knowledge enrichment by fusing representations for malware threat intelligence and behavior. Technical report, UMBC, October
Pirscoveanu, Radu S., et al. 2015. Analysis of malware behavior: Type classification using machine learning. In Proceedings of IEEE international conference on cyber situational awareness, data analytics and assessment, 1–7.
Rhode, Matilda, Pete Burnap, and Kevin Jones. 2018. Early-stage malware prediction using recurrent neural networks. Computers & Security 77: 578–594.
Tobiyama, Shun, et al. 2016. Malware detection with deep neural network using process behavior. In Proceedings of IEEE annual computer software and applications conference vol. 2, 577–582.
Vinayakumar, R., K.P. Mamoun Alazab, Prabaharan Poornachandran Soman, Ameer Al-Nemrat, and Sitalakshmi Venkatraman. 2019. Deep learning approach for intelligent intrusion detection system. IEEE Access 7: 41525–41550.
Vinayakumar, R., K.P. Mamoun Alazab, Prabaharan Poornachandran Soman, and Sitalakshmi Venkatraman. 2019. Robust intelligent malware detection using deep learning. IEEE Access 7: 46717–46738.
Wang, Chengwei. 2009. Ebat: Online methods for detecting utility cloud anomalies. In Proceedings of the middleware doctoral symposium, 1–6.
Watson, Michael R., et al. 2015. Malware detection in cloud computing infrastructures. IEEE Transactions on Dependable and Secure Computing 13 (2): 192–205.
Xiao, Zhifeng, and Yang Xiao. 2012. Security and privacy in cloud computing. IEEE Communications Surveys & Tutorials 15 (2): 843–859.
Xu, Zhixing, et al. 2017. Malware detection using machine learning based analysis of virtual memory access patterns. In Proceedings of IEEE design, automation & test in europe conference & exhibition, 169–174.
Acknowledgements
This work is partially supported by National Science Foundation awards 1565562, 2025682, 2025685, and 2025686.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
McDole, A., Gupta, M., Abdelsalam, M., Mittal, S., Alazab, M. (2021). Deep Learning Techniques for Behavioral Malware Analysis in Cloud IaaS. In: Stamp, M., Alazab, M., Shalaginov, A. (eds) Malware Analysis Using Artificial Intelligence and Deep Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-62582-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-62582-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62581-8
Online ISBN: 978-3-030-62582-5
eBook Packages: Computer ScienceComputer Science (R0)