Abstract
Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.
Highlights
In this age, the cyberspace is growing faster as a primary source for a node to node information transfer with all its charms and challenges
Machine learning techniques are being applied on both sides, i.e., attacker side and defender side
This paper reviews a comparative analysis of machine learning techniques applied to detect cybersecurity threats
Summary
The cyberspace is growing faster as a primary source for a node to node information transfer with all its charms and challenges. Apruzzese et al in [39] presented an analysis of machine learning techniques in cybersecurity to detect the spams, malware and intrusions It asserted that the machine learning techniques are vulnerable to cyber threats and all the methods are still struggling to overcome all the limitations and obstacles. Torres et al in [43] discussed the utilization of machine learning classification techniques applied in cybersecurity They provided a review of different alternatives to using machine learning models to reduce the error rate in intrusion and attack detection. This review paper is organized as follows: Section 2 describes an overview of cybersecurity threats, commonly used security datasets, basics of machine learning, and evaluation criteria to evaluate the performance of any classifier.
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