Abstract-The insider threat remains one of the most vexing problems in computer security. A number of approaches have been proposed to detect nefarious insider actions including user modeling and profiling techniques, policy and access enforcement techniques, and misuse detection. In this work we propose trap-based defense mechanisms and a deployment platform for addressing the problem of insiders attempting to exfiltrate and use sensitive information. The goal is to confuse and confound an adversary requiring more effort to identify real information from bogus information and provide a means of detecting when an attempt to exploit sensitive information has occurred. "Decoy Documents" are automatically generated and stored on a file system by the D 3 System with the aim of enticing a malicious user. We introduce and formalize a number of properties of decoys as a guide to design trap-based defenses to increase the likelihood of detecting an insider attack. The decoy documents contain several different types of bogus credentials that when used, trigger an alert. We also embed "stealthy beacons" inside the documents that cause a signal to be emitted to a server indicating when and where the particular decoy was opened. We evaluate decoy documents on honeypots penetrated by attackers demonstrating the feasibility of the method.
This paper provides a novel algorithm for automatically extracting social hierarchy data from electronic communication behavior. The algorithm is based on data mining user behaviors to automatically analyze and catalog patterns of communications between entities in a email collection to extract social standing. The advantage to such automatic methods is that they extract relevancy between hierarchy levels and are dynamic over time.We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players.
The Email Mining Toolkit (EMT) is a data mining system that computes behavior profiles or models of user email accounts. These models may be used for a multitude of tasks including forensic analyses and detection tasks of value to law enforcement and intelligence agencies, as well for as other typical tasks such as virus and spam detection. To demonstrate the power of the methods, we focus on the application of these models to detect the early onset of a viral propagation without "content-based" (or signature-based) analysis in common use in virus scanners. We present several experiments using real email from 15 users with injected simulated viral emails and describe how the combination of different behavior models improves overall detection rates. The performance results vary depending upon parameter settings, approaching 99% true positive (TP) (percentage of viral emails caught) in general cases and with 0.38% false positive (FP) (percentage of emails with attachments that are mislabeled as viral). The models used for this study are based upon volume and velocity statistics of a user's email rate and an analysis of the user's (social) cliques revealed in the person's email behavior. We show by way of simulation that virus propagations are detectable since viruses may emit emails at rates different than human behavior suggests is normal, and email is directed to groups of recipients in ways that violate the users' typical communications with their social groups.
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