Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors

Malware is any programme that gains access to or instals itself on a computer without the permission of the system's administrators. For cyber-criminals to achieve their nefarious objectives and purposes, a variety of viruses has been widely deployed. To tackle the growing number of malicious programmes and lessen their hazard, a novel deep learning framework is developed that employs NLP approaches as a starting point and combines CNN and LSTM neurones to record locally spatial correlations and learn from sequential longterm dependencies. As a result, for the malware classification job, high-level abstractions and representations are automatically derived. The accuracy of categorization rises from 0.81 (best by Random Forest) to about 1.0.