01275nam a2200097Ia 4500008004100000041001200041100001600053245006900069260004400138510099500182231221s9999    xx            000 0 und d  aEnglish  aSanjith, S. 0aRuntime Malware Detection Using Hardware FeaturesbSivaraman, E.  cJan-May 2019bPES UniversityaBengaluru  aThe low-level micro architectural malware prediction methods have shown good results in finding the viruses than the earlier software-based techniques. But these methods have failed to make an impact on the false detection rates which affect the precision of the detection scheme largely. When a person installs an application from a third-party website like filehippo.com or download.com, these methods tend to ignore significant attributes of an application like checksum, signature of the app creator that in turn makes an impact on the precision of the prediction methods due to more false prediction rates. In order to achieve a lesser false alarm rates, the impact made on the hardware features by the applications are tracked and the basic ML classifiers are trained and their efficiency is boosted using ensemble methods and a clear performance optimization of the ML classifiers using minimal number of HPCs is represented, thereby, minimizing the required number of runs for which