| Title | Malware Detection Amp; Classification Using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Choudhary, Sunita, Sharma, Anand |
| Conference Name | 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) |
| Date Published | feb |
| Keywords | Human Behavior, KNN, machine learning, malware classication, malware detection, Metrics, privacy, pubcrawl, resilience, Resiliency, SVM |
| Abstract | With fast turn of events and development of the web, malware is one of major digital dangers nowadays. Henceforth, malware detection is an important factor in the security of computer systems. Nowadays, attackers generally design polymeric malware [1], it is usually a type of malware [2] that continuously changes its recognizable feature to fool detection techniques that uses typical signature based methods [3]. That is why the need for Machine Learning based detection arises. In this work, we are going to obtain behavioral-pattern that may be achieved through static or dynamic analysis, afterward we can apply dissimilar ML techniques to identify whether it's malware or not. Behavioral based Detection methods [4] will be discussed to take advantage from ML algorithms so as to frame social-based malware recognition and classification model. |
| DOI | 10.1109/ICONC345789.2020.9117547 |
| Citation Key | choudhary_malware_2020 |
Malware Detection Amp; Classification Using Machine Learning