Systematic Review on Frameworks for Intrusion Detection using Machine Learning and Deep Learning Algorithms

These days, information technology-driven businesses and companies are primarily concerned with cybersecurity challenges and their increasing complexity. Computer systems are still quite susceptible to several kinds of assaults, even with the introduction of multiple intrusion detection systems to combat zero-day cybersecurity threats. This sophisticated cyberattack has led to severe revenue losses in previous years due to several system breakdowns and service disruptions and irreversible harm to one’s reputation. This research paper provides an extensive, methodical analysis of cyberattacks. In addition to making thorough comparisons between the papers published by reputable venues in this field from a variety of angles. To greatly increase business sustainability, private corporations, governments, and enterprises can greatly benefit from implementing the survey paper’s results and major conclusions in their local or worldwide operations. By conducting a literature review, providing preliminary details on the various intrusion detection algorithms, and evaluating their performance while considering various parameters, this study strives at investigating the machine learning and deep learning-based strategies for intrusion detection systems.