Abstract
Threat Intelligence is evidence-based knowledge that helps to understand, predict and adapt the behavior of an existing or emerging threats. Threat Intelligence can be used to decide on the subject’s response to its threat. Several components provide information about threats. Online Malware Scanners (OMS) are part of the general threat intelligence that the entire system can provide when searching for files, URLs, hashes, CVEs, IP addresses and domain names. Among other industrial technologies, threat intelligence has also proven its importance to reduce the human effort in detection, prevention and analysis of a malware. The paper covers the comparative analysis of OMS that would assist the industrial research and development (R&D) teams, cybersecurity researchers and students to decide the best suitable OMS for their deployed cyber physical systems in order to efficiently automate the whole process of threat intelligence and ultimately reducing the impact of external attacks on the overall cyber infrastructure of industrial organizations.
Keywords
- Malware analysis
- Online Malware Scanners
- Threat intelligence
- Cyber physical systems
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References
Saeed, I.A., Selamat, A., Abuagoub, A.M.A.: A survey on malware and malware detection systems. Int. J. Comput. Appl. 67(16), 25–31 (2013)
Gasse. https://www.kaspersky.com/blog/secure-futures-magazine/malwaretrends-2019/28098/. Accessed 30 Jan 2020
Souppaya, M., Scarfone, K.: Guide to malware incident prevention and handling for desktops and laptops. NIST Spec. Publ. 800, 83 (2013)
Mohamed, G.A.N., Norafida, I.: Survey on representation techniques for malware detection system. Am. J. Appl. Sci. 14, 1049–1069 (2017). https://doi.org/10.3844/ajassp.2017.1049.1069
https://www.reversinglabs.com/solutions/sandbox-malware-analysis. Accessed 31 Jan 2020
Ozsoy, M., et al.: Malware-aware processors: a framework for efficient online malware detection. In: 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA). IEEE (2015)
Damodaran, A., et al.: A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hacking Tech. 13(1), 1–12 (2017)
Juwono, J.T., Lim, C., Erwin, A.: A comparative study of behavior analysis sandboxes in malware detection. In: International Conference on New Media (CONMEDIA) (2015)
Uppal, D., Mehra, V., Verma, V.: Basic survey on malware analysis, tools and techniques. Int. J. Comput. Sci. Appl. (IJCSA) 4(1), 103 (2014)
Galal, H.S., Mahdy, Y.B., Atiea, M.A.: Behavior-based features model for malware detection. J. Comput. Virol. Hacking Tech. 12(2), 59–67 (2016)
Bhattacharya, A., Goswami, R.T.: Comparative analysis of different feature ranking techniques in data mining-based Android malware detection. In: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. Springer, Singapore (2017)
Ranveer, S., Hiray, S.: Comparative analysis of feature extraction methods of malware detection. Int. J. Comput. Appl. 120(5), 1–7 (2015)
Lindorfer, M., Kolbitsch, C., Comparetti, P.M.: Detecting environment-sensitive malware. In: International Workshop on Recent Advances in Intrusion Detection. Springer, Heidelberg (2011)
Pandey, S.K., Mehtre, B.M.: Performance of malware detection tools: a comparison. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies. IEEE (2014)
Bekerman, D., et al.: Unknown malware detection using network traffic classification. In: 2015 IEEE Conference on Communications and Network Security (CNS). IEEE (2015)
Tuvell, G., Lee, C.: Malware detection system and method for limited access mobile platforms. U.S. Patent No. 9,064,115, 23 June 2015
https://www.opswat.jp/blog/metadefender-more-private-alternativevirustotal#hash-lookup. Accessed 19 Feb 2020
Acknowledgments
We would like to acknowledge School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Pakistan and European Union (EU)’s Horizon 2020, Research and Innovation Staff Exchange Evaluations (RISE) under grant agreement no. 823904 – ENHANCE Project (MSCA-RISE 823904) for technical support and funding.
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Johar, A.H., Gerard, A., Athar, N., Asgher, U. (2021). Feature Based Comparative Analysis of Online Malware Scanners (OMS). In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_51
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DOI: https://doi.org/10.1007/978-3-030-51041-1_51
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