A Review of Cloud-Based Malware Detection System: Opportunities, Advances and Challenges
Cloud computing has an important role in all aspects of storing information and providing services online. It brings several advantages over traditional storing and sharing schema such as an easy access, on-request storage, scalability and decreasing cost. Using its rapidly developing technologies can bring many advantages to the protection of Internet of Things (IoT), Cyber-Physical Systems (CPS) from a variety of cyber-attacks, where IoT, CPS provides facilities to humans in their daily lives. Since malicious software (malware) is increasing exponentially and there is no well-known approach to detecting malware, the usage of cloud environments to detect malware can be a promising method. A new generation of malware is using advanced obfuscation and packing techniques to escape from detection systems. This situation makes almost impossible to detect complex malware by using a traditional detection approach. The paper presents an extensive review of cloud-based malware detection approach and provides a vision to understand the benefit of cloud for protection of IoT, CPS from cyber-attack. This research explains advantages and disadvantages of cloud environments in detecting malware and also proposes a cloud-based malware detection framework, which uses a hybrid approach to detect malware.
Steve Morgan, “cybersecurity almanac: 100 facts, figures, predictions and statistics,” Cybercrime Magazine Cisco and Cybersecurity Ventures, 2019.
Ömer Aslan, Refik Samet, and Ömer Özgür Tanrıöver, “Using a Subtractive Center Behavioral Model to Detect Malware,” Security and Communication Networks 2020, 2020.
Ajeet Singh and Anurag Jain, “Study of cyber-attacks on cyber-physical system,” In Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT). 26–27, 2018.
R Samani and G Davis, “McAfee Mobile Threat Report Q1,” 2019. https://www.mcafee.com/enterprise/en-us/assets/reports/rpmobile-threat-report-2019.pdf.
Ömer Aslan and Refik Samet, “A comprehensive review on malware detection approaches,” IEEE Access 8, 6249–6271, 2020.
Hao Sun, Xiaofeng Wang, Rajkumar Buyya, and Jinshu Su, “CloudEyes: Cloud-based malware detection with reversible sketch for resource-constrained internet of things (IoT) devices,” Software: Practice and Experience 47(3), 421–441, 2017.
Deepti Gupta, Smriti Bhatt, Maanak Gupta, Olumide Kayode, and Ali Saman Tosun, “Access control model for google cloud iot. In 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity),” IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 198–208, 2020.
Olumide Kayode, Deepti Gupta, and Ali Saman Tosun, “Towards a distributed estimator in smart home environment,” In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 1–6, 2020.
Yanfang Ye, Tao Li, Shenghuo Zhu, Weiwei Zhuang, Egemen Tas, Umesh Gupta, and Melih Abdulhayoglu, “Combining file content and file relations for cloud based malware detection,” In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 222–230, 2011.
Wei Yan, “CAS: A framework of online detecting advance malware families for cloud-based security,” In 2012 1st IEEE International Conference on Communications in China (ICCC). IEEE, 220–225, 2012.
Mohammad M Masud, Tahseen M Al-Khateeb, Kevin W Hamlen, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham, “Cloud-based malware detection for evolving data streams,” ACM transactions on management information systems (TMIS) 2(3), 1–27, 2011.
Rahul Kumar, Kamalakanta Sethi, Nishant Prajapati, Rashmi Ranjan Rout, and Padmalochan Ber, “Machine Learning based Malware Detection in Cloud Environment using Clustering Approach,” In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 1–7, 2020.
Qublai K Ali Mirza, Irfan Awan, and Muhammad Younas, “A Cloud-Based Energy Efficient Hosting Model for Malware Detection Framework,” In 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 1–6, 2018.
Aditya Agrawal and Karan Wahie, “Analyzing and optimizing cloud-based antivirus paradigm,” In 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH). IEEE, 203–207, 2016.
Sang Kil Cha, Iulian Moraru, Jiyong Jang, John Truelove, David Brumley, and David G Andersen, “SplitScreen: Enabling efficient, distributed malware detection,” Journal of Communications and Networks 13(2), 187–200, 2011.
Mahmoud Abdelsalam, Ram Krishnan, Yufei Huang, and Ravi Sandhu, “Malware detection in cloud infrastructures using convolutional neural networks,” In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). IEEE, 162–169, 2018.
Ram Mahesh Yadav,“Effective analysis of malware detection in cloud computing,” Computers & Security 83, 14–21, 2019.
Nicholas Penning, Michael Hoffman, Jason Nikolai, and Yong Wang, “Mobile malware security challeges and cloud-based detection,” In 2014 International Conference on Collaboration Technologies and
Systems (CTS). IEEE, 181–188, 2014.
Liang Xiao, Yanda Li, Xueli Huang, and XiaoJiang Du, “Cloud-based malware detection game for mobile devices with offloading,” IEEE Transactions on Mobile Computing 16(10), 2742–2750, 2017.
Deepti Gupta, Olumide Kayode, Smriti Bhatt, Maanak Gupta, and Ali Saman Tosun, “Learner’s Dilemma: IoT Devices Training Strategies in Collaborative Deep Learning,” In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 1–6, 2020.
Deepti Gupta, Paras Bhatt, and Smriti Bhatt, “A Game Theoretic Analysis for Cooperative Smart Farming,” arXiv preprint arXiv:2011.11098, 2020.
Deyannis, D., Papadogiannaki, E., Kalivianakis, G., Vasiliadis, G., & Ioannidis, S. “Trustav: Practical and privacy preserving malware analysis in the cloud,” In Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy (pp. 39-48), 2020.
Mishra, P., Aggarwal, P., Vidyarthi, A., Singh, P., Khan, B., Alhelou, H. H., & Siano, P. “VMShield: Memory Introspection-based Malware Detection to Secure Cloud-based Services against Stealthy Attacks,” IEEE Transactions on Industrial Informatics, 2021.
Ömer Aslan and Refik Samet. "Investigation of possibilities to detect malware using existing tools." IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017.
Ren, Z., Wu, H., Ning, Q., Hussain, I., & Chen, B. “End-to-end malware detection for android IoT devices using deep learning,” Ad Hoc Networks, 101, 102098, 2020.
Yazı A. F. Elezaj O. Ahmed J Catak, F. O, “Deep learning based Sequential model for malware analysis using Windows exe API Calls,” PeerJ Computer Science 6, e285, 2020.