LAUSR

Security event correlation approaches are necessary to detect and predict incremental threats such as multi-step or targeted attacks (advanced persistent threats) and other causal sequences of abnormal events. The use of security event correlation techniques also makes it possible to reduce the volume of the original data stream by grouping the events and eliminating their redundancy. The variety of event correlation methods, in turn, requires choosing the most appropriate way to handle security events, depending on the purpose and available resources. This paper presents a systematization of security event correlation methods into several categories, such as publication year, applied correlation methods, knowledge extraction methods, used data sources, architectural solutions, and quality evaluation of correlation methods. The research method is a systematic literature review, which includes the formulation of research questions, the choice of keywords and criteria for inclusion and exclusion. The review corpus is formed by using search queries in Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, and selection criteria. The final review corpus includes 127 publications from the existing literature for 2010–2021 and reflects the current state of research in the security event correlation field. The results of the analysis include the main directions of research in the field of event correlation and methods used for correlation both single events and their sequences in attack scenarios. The review also describes the datasets and metrics used to evaluate security event correlation approaches. In conclusion, the existing problems and possible ways to overcome them are identified. The main contribution of the review is the most complete classification and comparison of existing approaches to the security event correlation, considered not only from the point of view of the algorithm, but also the possibility of unknown attack detection, architectural solutions and the use of event initial data.