• Kum Chandani Patel
• Prof. Prashant Modi

Initial penetration is one of the first steps of an Advanced Persistent Threat (APT) attack, and it is considered one of the most significant means of initiating cyber-attacks aimed at organizations. In this paper, we first provide a brief overview on malware as well as the anti-malware industry, and present the industrial needs on malware detection. We then survey intelligent malware detection methods, the process of detection is usually divided into two stages: feature extraction and classification/clustering. The performance of such intelligent malware detection approaches critically depend on the extracted features and the methods for classification/clustering. We provide a comprehensive investigation on both the feature extraction and the classification/clustering techniques. We also discuss the additional issues and the challenges of malware detection using data mining techniques and finally forecast the trends of malware development

File Formats, Malicious Document, Malware Detection, Machine Learning

"Malicious Document Detection using Machine Learning: A Survey ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.546-560, June 2019, Available :http://www.jetir.org/papers/JETIR1907J68.pdf

"Malicious Document Detection using Machine Learning: A Survey ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp546-560, June 2019, Available at : http://www.jetir.org/papers/JETIR1907J68.pdf