2DeepNet: Dynamic Detection of Malwares Using Deep Learning Techniques
Nivaashini, M.1*, Soundariya, R. S.2, Vishnupriya, B.3 and Tharsanee, R. M.4
1 Department of Computer Science & Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
2 Department of Computer Technology, Bannari Amman Institute of Technology Sathyamangalam, Tamil Nadu, India
3 Department of Computer Science & Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
4 Department of Computer Science & Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India
Abstract
The innovation of technologies has become ubiquitous and imperative in day-to-day lives. Consequently, there has been a massive upsurge in malware evolution, which generates a substantial security hazard to organizations and individuals. This advancement in the competencies of malware opens new cybersecurity research dimensions in malware detection. It is quite impossible for anti-virus applications using traditional signature-based methods to find novel malware that incurs high overhead with respect to memory and time. This is because malware developers explore new methodologies to avoid these traditional malware defense approaches. To solve the problem, machine learning algorithms are used to learn the distinctions between malware and benign apps automatically. Unfortunately, traditional machine learning approaches that are constructed on handmade features are ...
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