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School of Computer Science

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    Robust clustering algorithms
    (Georgia Institute of Technology, 2011-04-05) Gupta, Pramod
    One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across any different fields ranging from computational biology to social sciences to computer vision in part because they are simple and their output is easy to interpret. However, many of these algorithms lack any performance guarantees when the data is noisy, incomplete or has outliers, which is the case for most real world data. It is well known that standard linkage algorithms perform extremely poorly in presence of noise. In this work we propose two new robust algorithms for bottom-up agglomerative clustering and give formal theoretical guarantees for their robustness. We show that our algorithms can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also extend our algorithms to an inductive setting with similar guarantees, in which we randomly choose a small subset of points from a much larger instance space and generate a hierarchy over this sample and then insert the rest of the points to it to generate a hierarchy over the entire instance space. We then do a systematic experimental analysis of various linkage algorithms and compare their performance on a variety of real world data sets and show that our algorithms do much better at handling various forms of noise as compared to other hierarchical algorithms in the presence of noise.
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    Flexible access control for campus and enterprise networks
    (Georgia Institute of Technology, 2010-04-07) Nayak, Ankur Kumar
    We consider the problem of designing enterprise network security systems which are easy to manage, robust and flexible. This problem is challenging. Today, most approaches rely on host security, middleboxes, and complex interactions between many protocols. To solve this problem, we explore how new programmable networking paradigms can facilitate fine-grained network control. We present Resonance, a system for securing enterprise networks , where the network elements themselves en- force dynamic access control policies through state changes based on both flow-level information and real-time alerts. Resonance uses programmable switches to manipulate traffic at lower layers; these switches take actions (e.g., dropping or redirecting traffic) to enforce high-level security policies based on input from both higher-level security boxes and distributed monitoring and inference systems. Using our approach, administrators can create security applications by first identifying a state machine to represent different policy changes and then, translating these states into actual network policies. Earlier approaches in this direction (e.g., Ethane, Sane) have remained low-level requiring policies to be written in languages which are too detailed and are difficult for regular users and administrators to comprehend. As a result, significant effort is needed to package policies, events and network devices into a high-level application. Resonance abstracts out all the details through its state-machine based policy specification framework and presents security functions which are close to the end system and hence, more tractable. To demonstrate how well Resonance can be applied to existing systems, we consider two use cases. First relates to "Network Admission Control" problem. Georgia Tech dormitories currently use a system called START (Scanning Technology for Automated Registration, Repair, and Response Tasks) to authenticate and secure new hosts entering the network [23]. START uses a VLAN-based approach to isolate new hosts from authenticated hosts, along with a series of network device interactions. VLANs are notoriously difficult to use, requiring much hand-holding and manual configuration. Our interactions with the dorm network administrators have revealed that this existing system is not only difficult to manage and scale but also inflexible, allowing only coarse-grained access control. We implemented START by expressing its functions in the Resonance framework. The current system is deployed across three buildings in Georgia Tech with both wired as well as wireless connectivities. We present an evaluation of our system's scalability and performance. We consider dynamic rate limiting as the second use case for Resonance. We show how a network policy that relies on rate limiting and traffic shaping can easily be implemented using only a few state transitions. We plan to expand our deployment to more users and buildings and support more complex policies as an extension to our ongoing work. Main contributions of this thesis include design and implementation of a flexible access control model, evaluation studies of our system's scalability and performance, and a campus-wide testbed setup with a working version of Resonance running. Our preliminary evaluations suggest that Resonance is scalable and can be potentially deployed in production networks. Our work can provide a good platform for more advanced and powerful security techniques for enterprise networks.