Constructing attack scenarios through correlation of intrusion alerts
Proceedings of the 9th ACM conference on Computer and communications security
Aggregation and Correlation of Intrusion-Detection Alerts
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Alert Correlation in a Cooperative Intrusion Detection Framework
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Intrusion Detection and Correlation: Challenges and Solutions
Intrusion Detection and Correlation: Challenges and Solutions
A Comprehensive Approach to Intrusion Detection Alert Correlation
IEEE Transactions on Dependable and Secure Computing
Attack Plan Recognition and Prediction Using Causal Networks
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
Understanding Complex Network Attack Graphs through Clustered Adjacency Matrices
ACSAC '05 Proceedings of the 21st Annual Computer Security Applications Conference
Bro: a system for detecting network intruders in real-time
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts
Computer Communications
Analyzing intensive intrusion alerts via correlation
RAID'02 Proceedings of the 5th international conference on Recent advances in intrusion detection
A mission-impact-based approach to INFOSEC alarm correlation
RAID'02 Proceedings of the 5th international conference on Recent advances in intrusion detection
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
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Computer networks are constantly being targeted by different attacks. Since not all attacks are created equal, it is of paramount importance for network administrators to be aware of the status of the network infrastructure, the relevance of each attack with respect to the goals of the organization under attack, and also the most likely next steps of the attackers. In particular, the last capability, attack prediction, is of the most importance and value to the network administrators, as it enables them to provision the required actions to stop the attack and/or minimize its damage to the network's assets. Unfortunately, the existing approaches to attack prediction either provide limited useful information or are too complex to scale to the real-world scenarios. In this paper, we present a novel approach to the prediction of the actions of the attackers. Our approach uses machine learning techniques to learn the historical behavior of attackers and then, at the run time, leverages this knowledge in order to produce an estimate of the likely future actions of the attackers. We implemented our approach in a prototype tool, called Nexat, and validated its accuracy leveraging a dataset from a hacking competition. The evaluations show that Nexat is able to predict the next steps of attackers with very high accuracy. In particular, Nexat achieves a 94% accuracy in predicting the next actions of the attackers in our prototype implementation. In addition, Nexat requires little computational resources and can be run in real-time for instant prediction of the attacks.