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ItemLeveraging Forensic Tools for Virtual Machine Introspection(Georgia Institute of Technology, 2011) Dolan-Gavitt, Brendan ; Payne, Bryan ; Lee, WenkeVirtual machine introspection (VMI) has formed the basis of a number of novel approaches to security in recent years. Although the isolation provided by a virtualized environment provides improved security, software that makes use of VMI must overcome the semantic gap, reconstructing high-level state information from low-level data sources such as physical memory. The digital forensics community has likewise grappled with semantic gap problems in the field of forensic memory analysis (FMA), which seeks to extract forensically relevant information from dumps of physical memory. In this paper, we will show that work done by the forensic community is directly applicable to the VMI problem, and that by providing an interface between the two worlds, the difficulty of developing new virtualization security solutions can be significantly reduced.
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ItemI Own, I Provide, I Decide: Generalized User-Centric Access Control Framework for Web Applications(Georgia Institute of Technology, 2010) Singh, Kapil ; Erete, Ikpeme ; Lee, WenkeWith the rapid growth of Web 2.0 technologies, users are contributing more and more content on the Internet, in the form of user profiles, blogs, reviews, etc. With this increased sharing comes a pressing need for access control policies and mechanisms to protect the users’ privacy. Access control has remained largely centralized and under the control of the web applications hosted on their servers. Moreover, most web applications either provide no or very primitive and limited access control. We argue that the owner of any piece of data on the web should be able to decide how to control access to this data. This argument should hold not only for the web applications contributing data, but also for the contributing users. In other words, users should be able to choose their own access control models to control the sharing of their data independent of the underlying applications of their data. In this work, we present a novel framework, called xAccess, for providing generic access control that empowers users to control how they want their data to be accessed. Such a control could be in the form of user-defined access categories, or in the form of new access control models built on top of our framework. On one hand, xAccess enables individual users to use a single unified access control across multiple web applications; and on the other hand, it allows an application to support different access control models deployed by its users with a single model abstraction. We demonstrate the viability of our design by means of a platform prototype. The usability of the platform is further evaluated by developing sample applications using the xAccess APIs. Our results show that our model incurs minimum overhead in enforcing the generic access control and requires negligible changes to the application code for deployment.
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ItemEvaluating Bluetooth as a Medium for Botnet Command and Control(Georgia Institute of Technology, 2009) Jain, Nehil ; Lee, Wenke ; Sangal, Samrit ; Singh, Kapil ; Traynor, PatrickMalware targeting mobile phones is being studied with increasing interest by the research community. While such attention has previously focused on viruses and worms, many of which use near-field communications in order to propagate, none have investigated whether more complex malware such as botnets can effectively operate in this environment. In this paper, we investigate the challenges of constructing and maintaining mobile phone-based botnets communicating nearly exclusively via Bluetooth. Through extensive large-scale simulation based on publicly available Bluetooth traces, we demonstrate that such a malicious infrastructure is possible in many areas due to the largely repetitive nature of human daily routines. In particular, we demonstrate that command and control messages can propagate to approximately 2/3 of infected nodes within 24 hours of being issued by the botmaster. We then explore how traditional defense mechanisms can be modified to take advantage of the same information to more effectively mitigate such systems. In so doing, we demonstrate that mobile phone-based botnets are a realistic threat and that defensive strategies should be modified to consider them.
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ItemRotalumè: A Tool for Automatic Reverse Engineering of Malware Emulators(Georgia Institute of Technology, 2009) Sharif, Monirul I. ; Lanzi, Andrea ; Giffin, Jonathon ; Lee, WenkeMalware authors have recently begun using emulation technology to obfuscate their code. They convert native malware binaries into bytecode programs written in a randomly generated instruction set and paired with a native binary emulator that interprets the bytecode. No existing malware analysis can reliably reverse this obfuscation technique. In this paper, we present the first work in automatic reverse engineering of malware emulators. Our algorithms are based on dynamic analysis. We execute the emulated malware in a protected environment and record the entire x86 instruction trace generated by the emulator. We then use dynamic data-flow and taint analysis over the trace to identify data buffers containing the bytecode program and extract the syntactic and semantic information about the bytecode instruction set. With these analysis outputs, we are able to generate data structures, such as control-flow graphs, that provide the foundation for subsequent malware analysis. We implemented a proof-of-concept system called Rotalumè and evaluated it using both legitimate programs and malware emulated by VMProtect and Code Virtualizer. The results show that Rotalumè accurately reveals the syntax and semantics of emulated instruction sets and reconstructs execution paths of original programs from their bytecode representations.
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ItemAdvanced Polymorphic Worms: Evading IDS by Blending in with Normal Traffic(Georgia Institute of Technology, 2005) Kolesnikov, Oleg ; Lee, WenkeNormal traffic can provide worms with a very good source of information to camouflage themselves. In this paper, we explore the concept of polymorphic worms that mutate based on normal traffic. We assume that a worm has already penetrated a system and is trying to hide its presence and propagation attempts from an IDS.We focus on stealthy worms that cannot be reliably detected by increases in traffic because of their low propagation factor.We first give an example of a simple polymorphic worm. Such worms can evade a signature-based IDS but not necessarily an anomaly-based IDS. We then show that it is feasible for an advanced polymorphic worm to gather a normal traffic profile and use it to evade an anomaly-based IDS.We tested the advanced worm implementation with three anomaly IDS approaches: NETAD, PAYL and Service-specific IDS. None of the three IDS approaches were able to detect the worm reliably. We found that the mutated worm can also evade other detection methods, such as the Abstract Payload Execution. The goal of this paper is to advance the science of IDS by analyzing techniques polymorphic worms can use to hide themselves. While future work is needed to present a complete solution, our analysis can be used in designing possible defenses. By showing that polymorphic worms are a practical threat, we hope to stimulate further research to improve existing IDS.
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ItemAn Information-Theoretic Measure of Intrusion Detection Capability(Georgia Institute of Technology, 2005) Gu, Guofei ; Fogla, Prahlad ; Dagon, David ; Lee, Wenke ; Skoric, BorisA fundamental problem in intrusion detection is what metric(s) can be used to objectively evaluate an intrusion detection system (IDS) in terms of its ability to correctly classify events as normal or intrusion. In this paper, we provide an in-depth analysis of existing metrics. We argue that the lack of a single unified metric makes it difficult to fine tune and evaluate an IDS. The intrusion detection process can be examined from an information-theoretic point of view. Intuitively, we should have less uncertainty about the input (event data) given the IDS output (alarm data). We thus propose a new metric called Intrusion Detection Capability, C[subscript ID], which is simply the ratio of the mutual information between IDS input and output, and the entropy of the input. C[subscript ID] has the desired property that: (1) it takes into account all the important aspects of detection capability naturally, i.e., true positive rate, false positive rate, positive predictive value, negative predictive value, and base rate; (2) it objectively provide an intrinsic measure of intrusion detection capability; (3) it is sensitive to IDS operation parameters. We propose that C[subscript ID] is the appropriate performance measure to maximize when fine tuning an IDS. The thus obtained operation point is the best that can be achieved by the IDS in terms of its intrinsic ability to classify input data. We use numerical examples as well as experiments of actual IDSs on various datasets to show that using C[subscript ID], we can choose the best (optimal) operating point for an IDS, and can objectively compare different IDSs.
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ItemWorm Detection Using Local Networks(Georgia Institute of Technology, 2004) Qin, Xinzhou ; Dagon, David ; Gu, Guofei ; Lee, WenkeThe need for a global monitoring system for Internet worm detection is clear. Likewise, the need for local detection and response is also obvious. In this study, we used a large data set to review some of the worm monitoring and detection strategies proposed for large networks, and found them difficult to apply to local networks. In particular, the Kalman filter and victim number-based approaches proved unsuitable for smaller networks. They are of course appropriate for large systems, but what work well for local networks? We propose two algorithms tailored for local network monitoring needs. First, the Destination Source Correlation (DSC) algorithm focuses on the infection relation, and tracks real infected hosts (and not merely scans) to provide an accurate response. Second, the HoneyStat system provides a way to track the short-term infection behavior used by worms. Potentially, this provides a basis for statistical inference about a worm’s behavior on a network.