Assessing the Efficacy of Machine Learning and Deep Learning in the Field of Cyber Security

The use of machine learning has become widespread across various fields because of its superior performance compared to conventional rule-based algorithms. As a result, these models have also been integrated into cyber security systems, Machine learning is being utilized to aid or possibly even supplant the role of human security analysts. However, it's important to evaluate the effectiveness of machine learning in cyber security with careful consideration, especially if complete automation of detection and analysis is being considered. This study provides an in-depth research focuses on machine learning techniques applied in intrusion, malware, and spam detection that are tailored towards security professionals. The primary objective of our study is to evaluate the degree of advancement or maturity of these techniques of ML-based cybersecurity solutions and to identify any limitations that could impede their effectiveness as detection mechanisms. To achieve this, we conducted a thorough literature review and performed experiments on enterprise systems and network traffic in real-world settings. Our goal is to gain understanding of the capabilities and limitations of ML solutions and provide actionable insights for their improvement.