Since the rapid development of the internet, the emergence of network intrusion has become the focus of studies for scholars and security enterprises. As an important device for detecting and analyzing malicious behaviors in networks, IDS (Intrusion Detection Systems) is widely deployed in enterprises, organizations and plays a very important role in cyberspace security. The massive log data produced by IDS not only contains information about intrusion behaviors but also contains potential intrusion patterns. Through normalizing, correlating, and modeling data, we can obtain the patterns of different intrusion scenarios. Based on the previous works in the area of alert correlation and analyzing, this paper proposed a framework named IACF (Intrusion Action Based Correlation Framework), which improved the process of alert aggregating, action extraction, and scenario discovery, and applied a novel method for extracting intrusion sessions based on temporal metrics. The proposed framework utilized a new grouping method for raw alerts based on the concept of intrinsic strong correlations, rather than the conventional time windows and hyper alerts. For discovering high stable correlations between actions, redundant actions and action link modes are removed from sessions by a pruning algorithm to reduce the impact of false positives, finally, a correlation graph is constructed by fusing the pruned sessions, based on the correlation graph, a prediction method for the future attack is proposed. The experiment result shows that the framework is efficient in alert correlation and intrusion scenario construction.

The intrusion detection system (IDS) which is used widely in enterprises, has produced a large number of logs named alerts, from which the intrusion patterns can be mined. These patterns can be used to construct the intrusion scenarios or discover the final objectives of the malicious actors, and even assist the forensic works of network crimes. In this paper, a novel algorithm for the intrusion pattern mining is proposed which aimsto solve the difficult problems of the intrusion action sequence such as the loss of important intrusion actions, the disorder of the action sequence and the random noise actions. These common problems often occur in the real production environment which cause serious performance decrease in the analyzing system. The proposed algorithm is based on the online analysis of the intrusion action sequences extracted from IDS alerts, through calculating the influences of a particular action on the subsequent actions, the real intrusion patterns are discovered. The experimental results show that the method is effective in discovering pattern from the complex intrusion action sequences.

With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work.