A Cybersecurity Perspective of Machine Learning Algorithms

Abstract

Cyber security ensures the real time protection of information, information systems, and networks from intruders. It is depicted from various prominent security and privacy reports that cyber security breaches have shown a rapid elevation in the last decade. Information security has been compromised by intruders at an intensified rate. The intrusion incidents are carried out by both the insiders as well as outsiders against the valuable data assets of the organizations. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address current cyber security issues and challenges. However, our concern is to address these issues in a proactive fashion. Henceforth, our goal is to explore the machine learning approaches with respect to cyber security perspective. As machine learning (ML) approaches provide a proactive security mechanism that will be serviceable to address cyber security issues and challenges, ML approaches can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. Machine learning as an emerging technology provides the ability to make programs learn from past data, then apply the learning behavior to make predictions for future activities with less human intervention and explicit programming. In this study, our primary concern is to discuss and analyze prominent machine learning algorithms and how effectively they can address present day cyber security issues. This study will help readers to obtain deep insights on prominent machine learning techniques that are effective to address cyber security concerns.