A survey on Effective Machine Learning Techniques in the field of Cyber Security

Abstract

Machine learning techniques have many cybersecurity applications, and they have entered the mainstream in a variety of fields. Examples include threat analysis, anomaly-based intrusion detection of frequent attacks on important infrastructures, malware analysis, particularly for zero-day malware detection, and many others. Machine learning-based detection is being employed by researchers in many cybersecurity solutions as a result of the inefficiency of signature-based methods in identifying zero day attacks or even modest modifications of existing assaults. In this paper, we cover a number of cybersecurity applications for machine learning. We also give a few examples of adversarial assaults on machine learning algorithms that aim to corrupt classifiers' training and test data in order to render them useless.