Organisations continuously face cyberattacks that generate large volumes of security alerts from intrusion detection systems, SIEM platforms, and other security monitoring tools. Many of these alerts may be false positives, low priority, or related to different stages of the same attack. Analysing individual alerts in isolation can therefore overwhelm security analysts and make it difficult to identify the threats that require immediate attention.

Alert prioritisation aims to rank security alerts according to their importance so that analysts can focus on the most critical threats first. However, the importance of an alert may depend not only on its individual attributes, such as severity or alert type, but also on its relationships with other alerts and the broader context in which it occurs.

Alert correlation complements prioritisation by identifying relationships among alerts, such as alerts involving related hosts, activities, attack stages, or temporal sequences. Correlating fragmented alerts can help reconstruct multi-stage attacks, reduce redundant alerts, and provide additional context for determining their priority.

Recent research has explored a range of approaches for alert prioritisation and correlation, including machine learning, graph-based techniques, and hybrid methods. These approaches provide opportunities to combine information from individual alerts with contextual and relational information to improve how security threats are identified and investigated.