Information systems security
Secrets & Lies: Digital Security in a Networked World
Secrets & Lies: Digital Security in a Networked World
Incentive-based modeling and inference of attacker intent, objectives, and strategies
Proceedings of the 10th ACM conference on Computer and communications security
Learning attack strategies from intrusion alerts
Proceedings of the 10th ACM conference on Computer and communications security
Attack Plan Recognition and Prediction Using Causal Networks
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Using HMM for Intent Recognition in Cyber Security Situation Awareness
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 02
Risks and Benefits of Signaling Information System Characteristics to Strategic Attackers
Journal of Management Information Systems
Boosting performance in attack intention recognition by integrating multiple techniques
Frontiers of Computer Science in China
Cyber security exercises and competitions as a platform for cyber security experiments
NordSec'12 Proceedings of the 17th Nordic conference on Secure IT Systems
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Even as reliance on information and communication technology networks continues to grow, and their potential security vulnerabilities become a greater threat, very little is known about the humans who perpetrate cyber attacks--what are their strategies, resources, and motivations? We present a new framework for modeling such cyber attackers. Utilizing observable information (i.e., network alerts, security implementations, systems logs), we can characterize attackers based on the risk they are willing to incur and delineate them based on skill level. These classifications can facilitate decision-making and resource allocation to counteract cybersecurity incidents. We look at two specific models of attacker risk and discuss empirical results from a prototype implementation of this modeling framework using real-world network data.