@inproceedings{fc6c3440937342d984ea9a921a40aada,
title = "Beyond data: Contextual information fusion for cyber security analytics",
abstract = "A major challenge of the existing attack detection approaches is the identification of relevant information to a particular situation, and the use of such information to perform multi-evidence intrusion detection. Addressing such a limitation requires integrating several aspects of context to better predict, avoid and respond to impending attacks. The quality and adequacy of contextual information is important to decrease uncertainty and correctly identify potential cyber-attacks. In this paper, a systematic methodology has been used to identify contextual dimensions that improve the effectiveness of detecting cyber-attacks. This methodology combines graph, probability, and information theories to create several context-based attack prediction models that analyze data at a high- and low-level. An extensive validation of our approach has been performed using a prototype system and several benchmark intrusion detection datasets yielding very promising results.",
keywords = "Context, Information fusion, Intrusion detection, Security",
author = "{Al Eroud}, Ahmed and George Karabatis",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 31st Annual ACM Symposium on Applied Computing, SAC 2016 ; Conference date: 04-04-2016 Through 08-04-2016",
year = "2016",
month = apr,
day = "4",
doi = "10.1145/2851613.2851636",
language = "English (US)",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery",
pages = "73--79",
booktitle = "2016 Symposium on Applied Computing, SAC 2016",
}