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

Yahye Abukar Ahmed, Shamsul Huda, Bander Ali Saleh Al-rimy, Nouf Alharbi, Faisal Saeed, Fuad A. Ghaleb and Ismail Mohamed Ali
Additional contact information
Yahye Abukar Ahmed: Faculty of Computing, SIMAD University, Mogadishu 801, Somalia
Shamsul Huda: School of Information Technology, Deakin University, Burwood, Melbourne 3125, Australia
Bander Ali Saleh Al-rimy: School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Nouf Alharbi: College of Computer Science and Engineering, Taibah University, Al-Madinah P.O. Box 344, Saudi Arabia
Faisal Saeed: DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
Fuad A. Ghaleb: School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Ismail Mohamed Ali: Faculty of Computing, SIMAD University, Mogadishu 801, Somalia

Sustainability, 2022, vol. 14, issue 3, 1-15

Abstract: 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.

Keywords: crypto-ransomware; Industrial Internet of Things; enhanced maximum Relevance and minimum Redundancy; TF-IDF; supervised approach (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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