Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

With the high level of proliferation of connected mobile devices,the risk of intrusion becomes higher.Artificial Intelligence(AI)and Machine Learning(ML)algorithms started to feature in protection software and showed effective results.These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’malware,especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly.In this article,we present a framework for mobile malware detection based on a new dataset containing new categories of mobile malware.We focus on categories of malware that were not tested before by Machine Learning algorithms proven effective in malware detection.We carefully select an optimal number of features,do necessary preprocessing,and then apply Machine Learning algorithms to discover malicious code effectively.From our experiments,we have found that the Random Forest algorithm is the best-performing algorithm with such mobile malware with detection rates of around 99%.We compared our results from this work and found that they are aligned well with our previous work.We also compared our work with State-of-the-Art works of others and found that the results are very close and competitive.

A genetic algorithm based lookup table approach for optimal stepping sequence of open-loop stepper motor systems

Stepper motor driven systems are widely used in industrial applications. They are mainly used for their low cost open-loop high performance. However, as dynamic systems need to be increasingly faster and their motion more precise, it is important to have an open-loop system which is accurate and reliable. In this paper, we present a novel technique in which a genetic algorithm （GA） based lookup table approach is used to find the optimal stepping sequence of an open-loop stepper motor system. The optimal sequence objective is to minimize residual vibration and to accurately follow trajectory. A genetic algorithm is used to find the best stepping sequence which minimizes the error and improves the system performance. Numerical simulation has showed the effectiveness of our approach to improve the system performance for both position and velocity. The optimized system reduced the residual vibration and was able to follow the trajectory with minimal error.