A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT

Ahmed, Yahye and Huda, Shamsul and Al-rimy, Bander and Alharbi, Nouf and Saeed, Faisal and Ghaleb, Fuad and Ali, Ismail (2022) A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT. Sustainability, 14. p. 1231. ISSN 2071-1050

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Ransomware attacks against Industrial Internet of Things (IIoT) have catastrophic consequences not only to the targeted infrastructure, but also the services provided to the public. By encrypting the operational data, the ransomware attacks can disrupt the normal operations, which represents a serious problem for industrial systems. Ransomware employs several avoidance techniques, such as packing, obfuscation, noise insertion, irrelevant and redundant system call injection, to deceive the security measures and make both static and dynamic analysis more difficult. In this paper, a Weighted minimum Redundancy maximum Relevance (WmRmR) technique was proposed for better feature significance estimation in the data captured during the early stages of ransomware attacks. The technique combines an enhanced mRMR (EmRmR) with the Term Frequency-Inverse Document Frequency (TF-IDF) so that it can filter out the runtime noisy behavior based on the weights calculated by the TF-IDF. The proposed technique has the capability to assess whether a feature in the relevant set is important or not. It has low-dimensional complexity and a smaller number of evaluations compared to the original mRmR method. The TF-IDF was used to evaluate the weights of the features generated by the EmRmR algorithm. Then, an inclusive entropy-based refinement method was used to decrease the size of the extracted data by identifying the system calls with strong behavioral indication. After extensive experimentation, the proposed technique has shown to be effective for ransomware early detection with low-complexity and few false-positive rates. To evaluate the proposed technique, we compared it with existing behavioral detection methods.

Item Type: Article
Identification Number: https://doi.org/10.3390/su14031231
14 January 2022Accepted
21 January 2022Published
Uncontrolled Keywords: crypto-ransomware; Industrial Internet of Things; enhanced maximum Relevance and minimum Redundancy; TF-IDF; supervised approach
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-02 - information technology
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 07 Mar 2022 14:10
Last Modified: 07 Mar 2022 14:10
URI: https://www.open-access.bcu.ac.uk/id/eprint/12926

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