Electronic Resource
A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT
| العنوان: | A Weighted Minimum Redundancy Maximum Relevance Technique for Ransomware Early Detection in Industrial IoT |
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| المؤلفون: | Ahmed, YA, Huda, Shamsul, Al-Rimy, BAS, Alharbi, N, Saeed, F, Ghaleb, FA, Ali, IM |
| بيانات النشر: | MDPI 2022-01-01 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | 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 FrequencyInverse 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. |
| مصطلحات الفهرس: | Science & Technology, Life Sciences & Biomedicine, Green & Sustainable Science & Technology, Environmental Sciences, Environmental Studies, Science & Technology - Other Topics, Environmental Sciences & Ecology, crypto-ransomware, Industrial Internet of Things, enhanced maximum Relevance and minimum Redundancy, TF-IDF, supervised approach, DYNAMIC-ANALYSIS, CYBER THREAT, SYSTEM, FEATURES, MODEL, Journal Article |
| URL: | issn: 2071-1050 issn: 2071-1050 Sustainability |
| الاتاحة: | Open access content. Open access content |
| ملاحظة: | 15 p. English |
| Other Numbers: | LD0 oai:dro.deakin.edu.au:DU:30162292 1306169744 |
| المصدر المساهم: | DEAKIN UNIV From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1306169744 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |