Modeling User Search Behavior for Masquerade Detection

Masquerade attacks are a common security problem that is a consequence of identity theft. This paper extends prior work by modeling user search behavior to detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user's desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We identify actions linked to search and information access activities, and use them to build user models. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 1.1%, far better than prior published results. The limited set of features used for search behavior modeling also results in large performance gains over the same modeling techniques that use larger sets of features

Modeling User Search-Behavior for Masquerade Detection

Masquerade attacks are a common security problem that is a consequence of identity theft. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. This paper extends prior work by modeling user search behavior to detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user's desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We extend prior research by devising taxonomies of UNIX commands and Windows applications that are used to abstract sequences of user commands and actions. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 0.13%, far better than prior published results. The limited set of features used for search behavior modeling also results in large performance gains over the same modeling techniques that use larger sets of features

Towards Effective Masquerade Attack Detection

Data theft has been the main goal of the cybercrime community for many years, and more and more so as the cybercrime community gets more motivated by financial gain establishing a thriving underground economy. Masquerade attacks are a common security problem that is a consequence of identity theft and that is generally motivated by data theft. Such attacks are characterized by a system user illegitimately posing as another legitimate user. Prevention-focused solutions such as access control solutions and Data Loss Prevention tools have failed in preventing these attacks, making detection not a mere desideratum, but rather a necessity. Detecting masqueraders, however, is very hard. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. These approaches suffered from high miss and false positive rates. None of these approaches could be packaged into an easily-deployable, privacy-preserving, and effective masquerade attack detector. In this thesis, I present a machine learning-based technique using a set of novel features that aim to reveal user intent. I hypothesize that each individual user knows his or her own file system well enough to search in a limited, targeted, and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, are not likely to know the file system and layout of another user's desktop, and would likely search more extensively and broadly in a manner that is different from that of the victim user being impersonated. Based on this assumption, I model a user's search behavior and monitor deviations from it that could indicate fraudulent behavior. I identify user search events using a taxonomy of Windows applications, DLLs, and user commands. The taxonomy abstracts the user commands and actions and enriches them with contextual information. Experimental results show that modeling search behavior reliably detects all simulated masquerade activity with a very low false positive rate of 1.12%, far better than any previously published results. The limited set of features used for search behavior modeling also results in considerable performance gains over the same modeling techniques that use larger sets of features, both during sensor training and deployment. While an anomaly- or profiling-based detection approach, such as the one used in the user search profiling sensor, has the advantage of detecting unknown attacks and fraudulent masquerade behaviors, it suffers from a relatively high number of false positives and remains potentially vulnerable to mimicry attacks. To further improve the accuracy of the user search profiling approach, I supplement it with a trap-based detection approach. I monitor user actions directed at decoy documents embedded in the user's local file system. The decoy documents, which contain enticing information to the attacker, are known to the legitimate user of the system, and therefore should not be touched by him or her. Access to these decoy files, therefore, should highly suggest the presence of a masquerader. A decoy document access sensor detects any action that requires loading the decoy document into memory such as reading the document, copying it, or zipping it. I conducted human subject studies to investigate the deployment-related properties of decoy documents and to determine how decoys should be strategically deployed in a file system in order to maximize their masquerade detection ability. Our user study results show that effective deployment of decoys allows for the detection of all masquerade activity within ten minutes of its onset at most. I use the decoy access sensor as an oracle for the user search profiling sensor. If abnormal search behavior is detected, I hypothesize that suspicious activity is taking place and validate the hypothesis by checking for accesses to decoy documents. Combining the two sensors and detection techniques reduces the false positive rate to 0.77%, and hardens the sensor against mimicry attacks. The overall sensor has very limited resource requirements (40 KB) and does not introduce any noticeable delay to the user when performing its monitoring actions. Finally, I seek to expand the search behavior profiling technique to detect, not only malicious masqueraders, but any other system users. I propose a diversified and personalized user behavior profiling approach to improve the accuracy of user behavior models. The ultimate goal is to augment existing computer security features such as passwords with user behavior models, as behavior information is not readily available to be stolen and its use could substantially raise the bar for malefactors seeking to perpetrate masquerade attacks

Combining a Baiting and a User Search Profiling Techniques for Masquerade Detection

Masquerade attacks are characterized by an adversary stealing a legitimate user's credentials and using them to impersonate the victim and perform malicious activities, such as stealing information. Prior work on masquerade attack detection has focused on profiling legitimate user behavior and detecting abnormal behavior indicative of a masquerade attack. Like any anomaly-detection based techniques, detecting masquerade attacks by profiling user behavior suffers from a significant number of false positives. We extend prior work and provide a novel integrated detection approach in this paper. We combine a user behavior profiling technique with a baiting technique in order to more accurately detect masquerade activity. We show that using this integrated approach reduces the false positives by 36% when compared to user behavior profiling alone, while achieving almost perfect detection results. We also show how this combined detection approach serves as a mechanism for hardening the masquerade attack detector against mimicry attacks

Masquerade Attack Detection Using a Search-Behavior Modeling Approach

Masquerade attacks are unfortunately a familiar security problem that is a consequence of identity theft. Detecting masqueraders is very hard. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. This paper extends prior work by presenting one-class Hellinger distance-based and one-class SVM modeling techniques that use a set of novel features to reveal user intent. The specific objective is to model user search profiles and detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user's desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We extend prior research that uses UNIX command sequences issued by users as the audit source by relying upon an abstraction of commands. We devise taxonomies of UNIX commands and Windows applications that are used to abstract sequences of user commands and actions. We also gathered our own normal and masquerader data sets captured in a Windows environment for evaluation. The datasets are publicly available for other researchers who wish to study masquerade attack rather than author identification as in much of the prior reported work. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 0.1%, far better than prior published results. The limited set of features used for search behavior modeling also results in huge performance gains over the same modeling techniques that use larger sets of features

Masquerade Detection Using a Taxonomy-Based Multinomial Modeling Approach in UNIX Systems

This paper presents one-class Hellinger distance-based and one-class SVM modeling techniques that use a set of features to reveal user intent. The specific objective is to model user command profiles and detect deviations indicating a masquerade attack. The approach aims to model user intent, rather than only modeling sequences of user issued commands. We hypothesize that each individual user will search in a targeted and limited fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user's desktop, and would likely search more extensively and broadly. Hence, modeling a user search behavior to detect deviations may more accurately detect masqueraders. To that end, we extend prior research that uses UNIX command sequences issued by users as the audit source by relying upon an abstraction of commands. We devised a taxonomy of UNIX commands that is used to abstract command sequences. The experimental results show that the approach does not lose information and performs comparably to or slightly better than the modeling approach based on simple UNIX command frequencies

On the Design and Execution of Cyber-Security User Studies: Methodology, Challenges, and Lessons Learned

Real-world data collection poses an important challenge in the security field. Insider and masquerader attack data collection poses even a greater challenge. Very few organizations acknowledge such breaches because of liability concerns and potential implications on their market value. This caused the scarcity of real-world data sets that could be used to study insider and masquerader attacks. Moreover, user studies conducted to collect such data lack rigor in their design and execution. In this paper, we present the methodology followed to conduct a user study and build a data set for evaluating masquerade attack detection techniques. We discuss the design, technical, and procedural challenges encountered during our own masquerade data gathering project, and share some of the lessons learned from this several-year project

Designing Host and Network Sensors to Mitigate the Insider Threat

We propose a design for insider threat detection that combines an array of complementary techniques that aims to detect evasive adversaries. We are motivated by real world incidents and our experience with building isolated detectors: such standalone mechanisms are often easily identified and avoided by malefactors. Our work-in-progress combines host-based user-event monitoring sensors with trap-based decoys and remote network detectors to track and correlate insider activity. We identify several challenges in scaling up, deploying, and validating our architecture in real environments

On the Design and Execution of Cyber-Security User Studies: Methodology, Challenges, and Lessons Learned

Real-world data collection poses an important challenge in the security field. Insider and masquerader attack data collection poses even a greater challenge. Very few organizations acknowledge such breaches because of liability concerns and potential implications on their market value. This caused the scarcity of real-world data sets that could be used to study insider and masquerader attacks. Moreover, user studies conducted to collect such data lack rigor in their design and execution. In this paper, we present the methodology followed to conduct a user study and build a data set for evaluating masquerade attack detection techniques. We discuss the design, technical, and procedural challenges encountered during our own masquerade data gathering project, and share some of the lessons learned from this several-year project