An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques. (25th April 2022)

• Record Type: Journal Article
• Authors: Albishry, Nabeel; AlGhamdi, Rayed; Almalawi, Abdulmohsen; Khan, Asif Irshad; Kshirsagar, Pravin R.; BaruDebtera,
• Other Names: Bhardwaj Arpit Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-04-25
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/5061059
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.