Advanced Persistent Threat Detection: Leveraging Behavioural Analysis and Threat Intelligence for Enhanced Cybersecurity

Abstract

APTs are sophisticated and persistent attacks that threaten the confidentiality, availability, and integrity of corporate data and services. As a result, they provide serious security issues to companies. This paper systematically reviews the literature on APT detection techniques by thoroughly reviewing the field's research, finding any gaps in the pertinent literature, and suggesting future research areas. The authors critically analyzed the current techniques of APT detection based on multi-stage attack-related behaviors. We conducted an extensive search on many databases that adhered to the PRISMA standards for systematic reviews and meta-analyses. For the final study, we included 45 studies in total. These studies include both academic and commercial sources. The results indicate that by exploiting the existing systemic vulnerabilities, APTs can horizontally propagate and successfully complete their operations. We recommend that their multi-stage attack-related behaviors combine with the appraisal of the availability of network weaknesses and their weakness to exploitation as we found loopholes in various popular APT detection techniques. This new methodology visualizes how APT attacks take place while combining ratings with vulnerability and the probability metrics together to identify possible sequences of attacking nodes. It makes it possible to execute proactive actions to stop future network compromise on the early identification of the most likely targets made possible by this enhanced detection approach.