Exploring Phishing Attacks in the AI Age: A Comprehensive Literature Review

Abstract

Over the years, phishing attacks have also evolved to more difficult types of phishing such as spear-phishing and clone-phishing. By nature, these attacks target not only human but also technical vulnerability resulting in massive financial losses and data exposure. So the more complex these methods become – such as phishing, cyberbullying or Packet Sniffing — offers in need new cybersecurity protocols to get rid of those threats. We have done this study by using Kitchenham Systematic Literature Review (SLR) framework, which consists of three phases: planning; conducting and reporting. These programs were reviewed because of the increased use in AI oriented operations such as financial phishing attacks, with new difficulties for detection systems. Furthermore, the study undertook extensive database searches on computers routines like IEEE Access, ResearchGate and Google Scholar rich in recent scientific studies. Methods: A two-phase screening process rigorously identified 20 high quality articles out of an initial pool of 250 studies for further analysis. The results are a good demonstration that AI-driven phishing campaigns continue to evolve in complexity, and in many cases can be more difficult to spot. Moreover, the current AI-based detection systems are mainly not claimed fully secured as they can easily tricked through adversarial attacks which may needs to updated or refine time after one another. The research indicates that combining contextual and behavioral investigation may improve the ability to detect threats as they take place. In addition, it is advisable to deploy a multi-layered security approach that combines traditional AI methodologies with human oversight through machine learning for more effective threat detection and prevention. The research highlights the need for preventative security strategies and continued detection innovations against ever more sophisticated phishing campaigns.