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@article{155843,
        author = {Dr. M. Chinna Rao and Hasmitha Dasari and Balarka Pradhyumna Danduboina},
        title = {Malware Prediction Classifier Using Random Forest Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {108-113},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155843},
        abstract = {Windows devices are also becoming more popular and are more defenseless to malware attacks. Malware is computer code that is designed to harm the operating system and has various names, including adware, spyware, viruses, worms, trojans, backdoors, ransomware and command and control (C&C) bots, depending on its function. Malware attacks on systems are increasing as a result of increased internet use. The detection of unknown malware has been attempted using several strategies, but none of them have been successful. To deal with these threats, proposed research utilized dynamic malware research based on machine learning. Many malicious software-scanning tools are available for Windows PCs, but they perform static analysis, which consumes a lot of time and resources.  To address a solution to this problem, an imaging technique to detect malware effectively by converting malware binaries into .exe files and applying machine learning to those .exe files. A comparison of different Windows PC malware detection techniques with different machine learning classifiers is undertaken to detect reliably.},
        keywords = {Malware attacks, Windows, Machine Learning, Random Forest Algorithm, vulnerable.},
        month = {},
        }