Abstract
An ever increasing number of critical missions rely today on complex Information Technology infrastructures, making such missions vulnerable to a wide range of potentially devastating cyber-attacks. Attackers can exploit network configurations and vulnerabilities to incrementally penetrate a network and compromise critical systems, thus rendering security monitoring and intrusion detection much more challenging. It is also evident from the ever growing number of high-profile cyber-attacks reported in the news that not only are cyber-attacks growing in sophistication but also in numbers. For these reasons, cyber-security analysts need to continuously monitor large amounts of alerts and data from a multitude of sensors in order to detect attacks in a timely manner and mitigate their impact. However—given the inherent complexity of the problem—manual analysis is labor-intensive and error-prone, and distracts the analyst from getting the “big picture” of the cyber situation.
The work of Sushil Jajodia and Massimiliano Albanese was supported in part by the Army Research Office under awards W911NF-13-1-0421, W911NF-09-1-0525, and W911NF-13-1-0317, and by the Office of Naval Research under awards N00014-12-1-0461 and N00014-13-1-0703.
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Albanese, M., Cam, H., Jajodia, S. (2014). Automated Cyber Situation Awareness Tools and Models for Improving Analyst Performance. In: Pino, R., Kott, A., Shevenell, M. (eds) Cybersecurity Systems for Human Cognition Augmentation. Advances in Information Security, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-10374-7_3
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DOI: https://doi.org/10.1007/978-3-319-10374-7_3
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