Multivariate Statistic Approach to Field Specifications of Binary Protocols in SCADA System

Choi, Seungoh; Chang, Yeop; Yun, Jeong-Han; Kim, Woonyon

doi:10.1007/978-3-319-15087-1_27

Cited by 2 publications

(1 citation statement)

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“…EUFuzz can quickly identify the packet structure and guide the fuzzing process, which has achieved good performance. Choi et al [18] proposed a multivariate static method to extract the protocol specification from the binary protocols used in ICS systems, thus using the protocol specification to guide fuzz testing.…”

Section: B Ics Protocols Vulnerability Miningmentioning

confidence: 99%

Machine Learning Methods for Industrial Protocol Security Analysis: Issues, Taxonomy, and Directions

Men

Lv²,

Zhou

et al. 2020

IEEE Access

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Machine learning has been widely studied in the security analysis of Industrial Control Systems (ICSs). However, in industrial scenarios, the amount of data as well as the speed of data generation are very different from standard machine learning data sets. Using these heterogeneous data and finding meaningful insights for practical security applications in ICSs is a big challenge. In addition, ICSs have been built for quite a long time. Security has not been seriously taken into account when ICSs were built. Security assessment or attack prevention cannot always be done in real time, as an ICS requires to be online all the time, especially when it comes to systems that affect critical infrastructure. In this work, we are motivated to a provide a clear and comprehensive survey of the state-of-the-art work that employs machine learning in security applications in ICSs, including vulnerability analysis, vulnerability detection and exploitation, anomaly detection and security assessment. Based on our in-depth survey, we highlight the issues of industrial protocol analysis with machine learning methods, provide the security applications with machine learning in ICSs and indicate the future directions.

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Section: B Ics Protocols Vulnerability Miningmentioning

confidence: 99%