| Title | An Experimental Analysis on Malware Detection in Executable Files using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sharma, Anurag, Mohanty, Suman, Islam, Md. Ruhul |
| Conference Name | 2021 8th International Conference on Smart Computing and Communications (ICSCC) |
| Keywords | Adware, composability, computer viruses, Executable Files, Internet, machine learning, machine learning algorithms, Malware, pubcrawl, resilience, Resiliency, spyware, Support vector machines, Tools, trojan horse, trojan horse detection, Trojan horses, Virus |
| Abstract | In the recent time due to advancement of technology, Malware and its clan have continued to advance and become more diverse. Malware otherwise Malicious Software consists of Virus, Trojan horse, Adware, Spyware etc. This said software leads to extrusion of data (Spyware), continuously flow of Ads (Adware), modifying or damaging the system files (Virus), or access of personal information (Trojan horse). Some of the major factors driving the growth of these attacks are due to poorly secured devices and the ease of availability of tools in the Internet with which anyone can attack any system. The attackers or the developers of Malware usually lean towards blending of malware into the executable file, which makes it hard to detect the presence of malware in executable files. In this paper we have done experimental study on various algorithms of Machine Learning for detecting the presence of Malware in executable files. After testing Naive Bayes, KNN and SVM, we found out that SVM was the most suited algorithm and had the accuracy of 94%. We then created a web application where the user could upload executable file and test the authenticity of the said executable file if it is a Malware file or a benign file. |
| DOI | 10.1109/ICSCC51209.2021.9528122 |
| Citation Key | sharma_experimental_2021 |
An Experimental Analysis on Malware Detection in Executable Files using Machine Learning