Techniques for network security analysis have historically focused on the actions of the network hosts. Outside of forensic analysis, little has been done to detect or predict malicious or infected nodes strictly based on their association with other known malicious nodes. This methodology is highly prevalent in the graph analytics world, however, and is referred to as community detection. In this paper, we present a method for detecting malicious and infected nodes on both monitored networks and the external Internet. We leverage prior community detection and graphical modeling work by propagating threat probabilities across network nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints that remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of probabilistic threat propagation on the tasks of detecting botnets and malicious web destinations.

In this paper, we present a method for detecting malicious activity within networks of interest. We leverage prior community detection work by propagating threat probabilities across graph nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints which remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of Probabilistic Threat Propagation on the task of detecting malicious web destinations.

To go beyond what current provenance systems can capture for natural language text documents, we propose the Lincoln Laboratory Plagiarism for Provenance System (LLPla) as an approach for capturing linguistic provenance. Linguistic provenance infers the origin of text based on its linguistic structure. We take a plagiarism detection approach to this task as identifying similar sections of text is fundamental to linguistic provenance and central to LLPla Ì's performance. Thus, to determine the most viable plagiarism detection algorithm for use in LLPla Ì, we evaluate three state-of-the-art plagiarism detection algorithms. Moreover, we propose extensions to the best-performing algorithm that improve its precision with negligible effects on recall.

The attack graph is an abstraction that reveals the ways an attacker can leverage vulnerabilities in a network to violate a security policy. When used with attack graph-based security metrics, the attack graph may be used to quantitatively assess security-relevant aspects of a network. The Shortest Path metric, the Number of Paths metric, and the Mean of Path Lengths metric are three attack graph-based security metrics that can extract security-relevant information. However, one's usage of these metrics can lead to misleading results. The Shortest Path metric and the Mean of Path Lengths metric fail to adequately account for the number of ways an attacker may violate a security policy. The Number of Paths metric fails to adequately account for the attack effort associated with the attack paths. To overcome these shortcomings, we propose a complimentary suite of attack graph-based security metrics and specify an algorithm for combining the usage of these metrics. We present simulated results that suggest that our approach reaches a conclusion about which of two attack graphs correspond to a network that is most secure in many instances.

Abstract In this paper, we discuss security problems, with a focus on collaborative attacks, in the Worldwide Interoperability for Microwave Access (WiMAX) scenario. The WiMAX protocol suite, which includes but is not limited to DOCSIS, DES, and AES, consists of a large number of protocols. We present briefly the WiMAX standard and its vulnerabilities. We pinpoint the problems with individual protocols in the WiMAX protocol suite, and discuss collaborative attacks on WiMAX systems. We present several typical WiMAX attack scenarios, including: bringing a large number of attackers to increase their computation power and break WiMAX protocols; assembling a sufficient number of attackers to influence the decision‐making of core machines, which includes routing attacks and Sybil attacks; and exploiting implementations that do not conform to the WiMAX specification completely, causing interoperability problems among various protocols, including the ones in typical WiMAX/WiFi/LAN deployment scenarios. We present theoretical models and practical solutions to profile, model, and analyze collaborative attacks in WiMAX. We employ attack graphs to do vulnerability analysis. Experimental results verify our models and validate our analysis. Copyright © 2009 John Wiley & Sons, Ltd.