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@article{157797,
        author = {Buddhadev Pusti},
        title = {Malware Detection Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {8},
        pages = {45-50},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=157797},
        abstract = {Malware is a critical security risk on Internet today. Malware is a set of programs designed to damage Internet- connected devices such as servers, computer resources, networks. Criminals are using Malware to send spam and to steal personal, financial, business information. Malware detection is the primary tool to stop unauthorized access of sensitive information. These days Windows OS is the most commonly used Operating System worldwide(77% to 88.8%) also it is the most targeted OS by malware attackers. In this paper detection of malware is done by simple observation of Portable Executable(PE) headers. In this paper, I use four methodology: 1. collect the data- set from https://www.kaggle.com/c/malware-detection/data 2. use an ExtraTreesClassifier for feature importance 3. use a ”most frequent” strategy for baseline model 4. use Random Forest classification algorithm as a benchmark model.
My data-set contains 140849 benign samples and 75503 malware samples. In the data-set, the feature “legitimate” has values “0” and “1”, defines valid and malware files respectively. My experiments to detect malware by Portable Executable(PE) headers have a precision score of 98% and an f1-score of 98%. My experiments indicate that it is easy to detect malware files by observing Portable Executable(PE) headers.},
        keywords = {Malware, Malware detection, Portable Exe- cutable(PE) headers, Internet-connected devices, security},
        month = {},
        }