Search in Medwell
 
 
Journal of Engineering and Applied Sciences
Year: 2018 | Volume: 13 | Issue: 15 | Page No.: 6281-6292
DOI: 10.36478/jeasci.2018.6281.6292  
Malware Analysis and Detection Approaches: Drive to Deep Learning
Toqeer Ali , Salman Jan , Shahrul Niza Musa and Atiqur Rahman
 
Abstract: The growing number of malware attacks poses serious threats to private data and to the expensive computing resources. To detect malware and their associated families, anti-malware companies rely on signatures which indeed include regular expressions and strings. The recent malware attacks in the last few years including the resurgence of ransomware have proven that signature-based methods are error-prone and can be easily evaded by intelligent malware programs. This study reviews traditional and state-of-the-art models developed for malware analysis and detection. According to our observation the classification of malware and their behavior facilitates in provision of basic insights for the researchers working in the domain of malware analysis. At the end we present the conception of using Deep Convolutional Generated Adversarial Networks (DCGAN) in the area of malware detection as the DCGANs are the latest approach in deep learning that effectively deals adversarial examples.
 
How to cite this article:
Toqeer Ali, Salman Jan, Shahrul Niza Musa and Atiqur Rahman, 2018. Malware Analysis and Detection Approaches: Drive to Deep Learning. Journal of Engineering and Applied Sciences, 13: 6281-6292.
DOI: 10.36478/jeasci.2018.6281.6292
URL: http://medwelljournals.com/abstract/?doi=jeasci.2018.6281.6292