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Real-time fusion and projection of n...

Rochester Institute of Technology., Computer Engineering.

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Real-time fusion and projection of network intrusion activity.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Real-time fusion and projection of network intrusion activity./
作者:	Byers, Stephen Reed.
面頁冊數:	80 p.
附註:	Adviser: Shanchieh J. Yang.
Contained By:	Masters Abstracts International46-06.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1453882
ISBN:	9780549594864

Real-time fusion and projection of network intrusion activity.
Byers, Stephen Reed.

Real-time fusion and projection of network intrusion activity. - 80 p.

Adviser: Shanchieh J. Yang.

Thesis (M.S.)--Rochester Institute of Technology, 2008.

Intrusion Detection Systems (IDS) warn of suspicious or malicious network activity and are a fundamental, yet passive, defense-in-depth layer for modern networks. Prior research has applied information fusion techniques to correlate the alerts of multiple IDSs and group those belonging to the same multi-stage attack into attack tracks. Projecting the next likely step in these tracks potentially enhances an analyst's situational awareness; however, the reliance on attack plans, complicated algorithms, or expert knowledge of the respective network is prohibitive and prone to obsolescence with the continual deployment of new technology and evolution of hacker tradecraft.

ISBN: 9780549594864Subjects--Topical Terms:

626642
Computer Science.

Real-time fusion and projection of network intrusion activity.
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502 $a Thesis (M.S.)--Rochester Institute of Technology, 2008.
520 $a Intrusion Detection Systems (IDS) warn of suspicious or malicious network activity and are a fundamental, yet passive, defense-in-depth layer for modern networks. Prior research has applied information fusion techniques to correlate the alerts of multiple IDSs and group those belonging to the same multi-stage attack into attack tracks. Projecting the next likely step in these tracks potentially enhances an analyst's situational awareness; however, the reliance on attack plans, complicated algorithms, or expert knowledge of the respective network is prohibitive and prone to obsolescence with the continual deployment of new technology and evolution of hacker tradecraft.
520 $a This thesis presents a real-time continually learning system capable of projecting attack tracks that does not require a priori knowledge about network architecture or rely on static attack templates. Prediction correctness over time and other metrics are used to assess the system's performance. The system demonstrates the successful real-time adaptation of the model, including enhancements such as the prediction that a never before observed event is about to occur. The intrusion projection system is framed as part of a larger information fusion and impact assessment architecture for cyber security.
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