@article{MAKHILLJEAS2018131516633,
    title = {Malware Analysis and Detection Approaches: Drive to Deep Learning},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {15},
    pages = {6281-6292},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.6281.6292},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.6281.6292},
    author = {Toqeer,Salman,Shahrul and},
    keywords = {Trusted computing,neural networks,RNN,ESN,CNN,GAN,DCGAN,GPU},
    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.}
    }