Experimental Analysis of Malware Detection and Classification System using Intelligent Deep Learning Methodology

Computer system security relies heavily on the ability to identify and eliminate malware. Signature-based approaches are widely used, although they are not able to effectively identify zero-day assaults or polymorphic infections. This is why there is a demand for detection methods based on deep learning and categorization. Computers, Smartphones, and apps are all fair game for malicious software. Malware is software that has to be watched out for since it might be a hazard for both computer users and internet-based systems. Malware was developed with the intention of stealing sensitive data from a user's computer or taking remote control of a user's device. Malware is software designed to disrupt or even destroy data on a user's computer. Malicious software can enter a computer in a number of ways, one of which occurs over a network that already has the virus installed. This project will investigate and deploy high-precision classification models using deep learning technologies from the discipline of computational intelligence to enhance the efficiency and accuracy of malware detection. Learning based Classification Scheme for Improved Malware Detection (LCSIMD) is a unique technique described in this work and utilized as the primary training model. The suggested LCSIMD framework is trained with malware visualization tools. Models with higher classification accuracy can be found by transforming malware features into images and then modifying their characteristics and input procedures; similarly, prototypes with proposed model can benefit from the use of suitable characteristics and preprocessing methods. The final step is to generate characteristics for network-generated instances, which improves the precision of small-scale training. Every one of us desire to utilize malware that impacts various devices as a teaching example, as described above. The output efficiency of the suggested technique is cross-validated with the classic Neural Network (NN) model to assess the performance of the new scheme.