An Effective Attack Scenario Construction Model based on Attack Steps and Stages Identification
This work addresses the challenge of analyzing alerts for security analysts, but it appears incremental as it builds on existing alert correlation methods.
The paper tackles the problem of constructing attack scenarios from low-level alerts in Network Intrusion Detection Systems, which often leads to false or incomplete correlations. It proposes an effective model that improves correlation effectiveness, as measured by Completeness and Soundness metrics on DARPA 2000 and ISCX2012 datasets.
A Network Intrusion Detection System (NIDS) is a network security technology for detecting intruder attacks. However, it produces a great amount of low-level alerts which makes the analysis difficult, especially to construct the attack scenarios. Attack scenario construction (ASC) via Alert Correlation (AC) is important to reveal the strategy of attack in terms of steps and stages that need to be launched to make the attack successful. In most of the existing works, alerts are correlated by classifying the alerts based on the cause-effect relationship. However, the drawback of these works is the identification of false and incomplete correlations due to the infiltration of raw alerts. To address this problem, this work proposes an effective ASC model to discover the complete relationship among alerts. The model is successfully experimented using two types of datasets, which are DARPA 2000, and ISCX2012. The Completeness and Soundness of the proposed model are measured to evaluate the overall correlation effectiveness.