Identification Of Dominant Features in Non-Portable Executable Malicious File
Tukkappa K. Gundoor, Sridevi · 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) · 2022
Malware group identification is a crucial procedure that involves extracting unique properties from a collection of malware samples. Several malware developers now employ different tactics, such as encryption and obfuscation, to avoid the detection of their programmers’ distinctive properties. This research proposes features for extracting malware from file contents. it searches a sample dataset for non-portable executable files, then disassemble the file, and removes structural discrimination characteristics from non-Portable harmful files. dominant features combined with analysis techniques, and retrieved characteristics help to tell the difference between harmful and non-malicious files. The purpose of the research is to bring a noble collection of qualities to well-known malware characteristics.