• Swapnil S. Chaudhari
• Dr. Satish N. Gujar
• Dr. Farhat Jummani

The region of Malware Detection is not new, Malware is a malicious code that is done to harm a computer or network. There are three main methods are in the market to sense malware: Signature-based, Behavioral based, and Heuristic ones methods, but to find the more accurate result in anomaly-based Malware detection Machine Learning algorithms are used. Therefore this study is based on choosing the best machine learning classification methods for the detection of malware. In this work, the implementation of such an efficient and versatile MDS is a profound learning-oriented manner. In This comparative Analysis of Malware Detection We took two different datasets of Malware detection online as dataset_malwares.csv, Data.csv of Rows:19243, Columns:79 & Rows:10539, Columns:57 also applied four different Machine Learning classification techniques as naive Bayes classification, the random forest classification, Decision Tree Classifier & Linear SVC Classifier for to find better accuracy. Through both, the test conducted on Classifiers Machine Learning is verified to be successful for MDS. The best classification method is Random forest classification which has classified the subjects with an average of 97.955% accuracy. This can prove to be a useful screening classification tool for detecting malware in systems (MDS) and networks (NMDS).

Accuracy in Classifiers, Malware Analysis, Malware Classification, Malware Detection, ML Classifiers, Random Forest Classifier

"COMPARATIVE ANALYSIS OF MALWARE DETECTION DATASETS USING DIFFERENT MACHINE LEARNING CLASSIFIERS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.d698-d703, January-2022, Available :http://www.jetir.org/papers/JETIR2201390.pdf

"COMPARATIVE ANALYSIS OF MALWARE DETECTION DATASETS USING DIFFERENT MACHINE LEARNING CLASSIFIERS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppd698-d703, January-2022, Available at : http://www.jetir.org/papers/JETIR2201390.pdf