Modeling and Optimization of Signals Using Machine Learning Techniques

3A Survey on Malware Detection Using Machine Learning

Devika S. P.^*, Pooja M. R. and Arpitha M. S.

Vidyavardhaka College of Engineering, Mysore, India

Abstract

With an emphasis on the Android platform and related technologies, the exponential rise in cybercrimes has resulted in a notable increase in cyberattack occurrences, many of which have catastrophic outcomes. An important component of Android framework security is malware identification. To overcome this risk, the machine learning algorithm which has a high degree of accuracy in malware detection was used. An important component of the Android application’s security is its ability to distinguish between benign and malicious apps. Malware has become a big computer threat in recent times due to the web’s rapid expansion and advancement. Ultimately, friendly programs falling under a particular class will typically have a similar set of features. Despite what is generally expected, pernicious applications will, in general, have unusual highlights, which are extraordinary for the classification that they have a place with. Machine learning structures have been tried, investigated, and created to portray and order malware into their malware families, utilizing attributes separated and obtained from static and live examination of the noxious programming.

Keywords: Cybercriminals, machine learning, malware detection

3.1 Background

Genuinely, Android is the primary focus on stage by malware creators trying to assume the responsibility ...