NDLI: Nexat: a history-based approach to predict attacker actions

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Nexat: a history-based approach to predict attacker actions

Content Provider	ACM Digital Library
Author	Zand, Ali Kruegel, Christopher Cipriano, Casey Vigna, Giovanni Houmansadr, Amir
Abstract	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.
Starting Page	383
Ending Page	392
Page Count	10
File Format	PDF
ISBN	9781450306720
DOI	10.1145/2076732.2076787
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-12-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Situation awareness Machine learning Attack prediction
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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