Impact of PCA Feature Extraction Method used in Malware Detection for Security Enhancement
Venkat P. Patil1, Hrushikesh Shukla2, Sanket Sawant3, Zuzer Sakarwala4
1Venkat P. Patil*, Electronics and Communication Engineering Department, Smt. Indira Gandhi College of Engineering, Navi Mumbai.
2Hrushikesh Shukla, Computer Engineering Department, Smt. Indira Gandhi College of Engineering, Navi Mumbai.
3Sankat Sawant, Computer Engineering Department, Smt. Indira Gandhi College of Engineering, Navi Mumbai.
4Zuzer Sakarwala, Computer Engineering Department, Smt. Indira Gandhi College of Engineering, Navi Mumbai.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1802-1807 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8790049420/2020©BEIESP | DOI: 10.35940/ijeat.D8790.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Malware is one of the all told the foremost security threats on the net now a days. Some of the Internet problems like denial of service attacks and spam e-mails have malware threat cause. Computers involved with malware are however networked together for making botnets, and major of threats or attacks are basically launched with the help of these types of malicious and attacker-controlled networks. Downloading files like Executable files like .exe, .bat, .msi etc from sources of untrusted internet probably having an opportunity of getting maliciousness. Further it is seen that these executables are smartly obfuscated with the help of some of the anomalous user for bypassing antivirus stuffs. In this research work , we have proposed an enhanced approach for detecting some of the malicious executables files with the help of analysing the traced Portable Executable (PE) files which are extracted from executable files and use of PCA feature extraction method. The method used in this paper consists of training a supervised binary classifier with the help of these extracted features from the portable executables files from the normal and malicious executables. Considering this approach experimentation has been done on an outsized publicly available dataset and it is seen that over 95% of classification accuracy can be obtained.
Keywords: Malware Analysis ,Machine Learning, , Feature Extraction, PCA feature extraction.