An Exploration of Machine Learning Approaches in the Field of Cybersecurity

The extensive and growing utilization of the Internet and mobile apps has resulted in the enlargement of the online realm, rendering it more vulnerable to extended and automated cyber assaults. In response to this heightened vulnerability, cybersecurity techniques have been developed to strengthen security measures and improve the ability to detect and respond to cyberattacks. Due to the intelligence of cybercriminals in evading traditional security systems, the previously employed security measures have become inadequate. Conventional security systems struggle to effectively detect new and ever-changing security attacks that are previously unseen or have varying forms. ML methods are making substantial contributions to different aspects of cybersecurity, playing a pivotal role in numerous applications within the discipline. While ML systems have been successful so far, there are considerable obstacles in ensuring their trustworthiness. This paper’s main objective is to offer a thorough examination of the obstacles ML techniques encounter in safeguarding cyberspace from attacks. This is accomplished by examining the existing body of literature concerning ML techniques utilized in the field of cybersecurity. These techniques encompass areas such as intrusion detection, spam detection, and malware detection within computer and mobile networks. The document also provides succinct elucidations of each specific machine learning approach, indispensable machine learning tools, ML involvement in cybersecurity, and current state of ML for cybersecurity. Finally, the paper examines the barriers and challenges, as well as the anticipated path for the future of ML in the context of cybersecurity.