B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

Dong-Wook Kim¹, Gun-Yoon Shin¹, Myung-Mook Han^{2, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1891-1904, 2020, DOI:10.32604/cmc.2020.010933 - 16 September 2020

Abstract This study was conducted to enable prompt classification of malware, which was becoming increasingly sophisticated. To do this, we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified. Initially, the analysis features were extracted using Cuckoo Sandbox, an open-source malware analysis tool, then the features were divided into five categories using the extracted information. The 804 extracted features were reduced by 70% after selecting only the most suitable ones for malware classification using a learning model-based feature selection More >