Advancing cybersecurity: a comprehensive review of AI-driven detection techniques

As the number and cleverness of cyber-attacks keep increasing rapidly, it’s more
important than ever to have good ways to detect and prevent them. Recognizing
cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use
artifcial intelligence (AI), including machine learning (ML) and deep learning (DL),
alongside metaheuristic algorithms to detect cyber-attacks better. We’ve thoroughly
examined over sixty recent studies to measure how efective these AI tools are at identifying and fghting a wide range of cyber threats. Our research includes a diverse array
of cyberattacks such as malware attacks, network intrusions, spam, and others, showing
that ML and DL methods, together with metaheuristic algorithms, signifcantly improve
how well we can fnd and respond to cyber threats. We compare these AI methods
to fnd out what they’re good at and where they could improve, especially as we face
new and changing cyber-attacks. This paper presents a straightforward framework
for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection
is critical. We evaluate the efectiveness and the limitations of current ML and DL
proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We’re pushing for smart and fexible
solutions that can adapt to new challenges. The fndings from our research suggest
that the future of protecting against cyber-attacks will rely on continuously updating AI
methods to stay ahead of hackers’ latest tricks.