Applications of Machine Learning for Malware and Intrusions Detection

Abstract

Computers face numerous security threats like spoofing, jamming, DDoS attacks, malware, and botnet attacks. Various machine learning techniques and algorithms can be employed to enhance the security features of computing devices, making machine learning a valuable tool in this regard.

The primary focus of this research was to pinpoint security issues in electronic devices and the use of machine learning to address them. The objective is to enhance the security of computer devices to safeguard private data, ensure the availability and integrity of the devices, and prevent access to malicious activities.

Certain security experiments yielded improved outcomes, with a reduction in false detection rates from 20% to 5% and increased accuracy in detecting threats from 89% to 92%. Algorithms and experiments did not yet achieve 100% accuracy in detecting all threats; therefore, computing devices must be trained using advanced machine learning techniques to enhance security levels.