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.
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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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