Browsing by Author "Liu, Haiyang"

Now showing 1 - 4 of 4
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    A Multi-Step Framework for Detecting Attack Scenarios
    (2006-02-21) Shaneck, Mark; Chandola, Varun; Liu, Haiyang; Choi, Changho; Simon, Gyorgy; Eilertson, Eric; Kim, Yongdae; Zhang, Zhi-Li; Srivastava, Jaideep; Kumar, Vipin
    With growing dependence upon interconnected networks, defending these networks against intrusions is becoming increasingly important. In the case of attacks that are composed of multiple steps, detecting the entire attack scenario is of vital importance. In this paper, we propose an analysis framework that is able to detect these scenarios with little predefined information. The core of the system is the decomposition of the analysis into two steps: first detecting a few events in the attack with high confidence, and second, expanding from these events to determine the remainder of the events in the scenario. Our experiments show that we can accurately identify the majority of the steps contained within the attack scenario with relatively few false positives. Our framework can handle sophisticated attacks that are highly distributed, try to avoid standard pre-defined attack patterns, use cover traffic or "noisy" attacks to distract analysts and draw attention away from the true attack, and attempt to avoid detection by signature-based schemes through the use of novel exploits or mutation engines.
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    dSENSE: Data-driven Stochastic Energy Management for Wireless Sensor Platforms
    (2005-05-09) Liu, Haiyang; Chandra, Abhishek; Srivastava, Jaideep
    Wireless sensor networks are being widely deployed for providing physical measurements to diverse applications. Energy is a precious resource in such networks as nodes in wireless sensor platforms are typically powered by batteries with limited power and high replacement cost. This paper presents dSENSE: a data-driven approach for energy management in sensor platforms. dSENSE is a node-level power management approach that utilizes knowledge of the underlying data streams as well as application data quality requirements to conserve energy on a sensor node. dSENSE employs sense-on-change---a sampling strategy that aggressively conserves power by reducing sensing activity on the sensor node. Unlike most existing energy management techniques, this strategy enables explicit control of the sensor along with the CPU and the radio. Our approach uses an efficient statistical data stream model to predict future sensor readings. These predictions are coupled with a stochastic scheduling algorithm to dynamically control the operating modes of the sensor node components. Using experimental results obtained on PowerTOSSIM with a real-world data trace, we demonstrate that our approach reduces energy consumption by 29-36% while providing strong statistical guarantees on data quality.
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    Probabilistic Stream Relational Algebra: A Data Model for Sensor Data Streams
    (2004-07-12) Liu, Haiyang; Hwang, San-Yih; Srivastava, Jaideep
    Sensor data streams exhibit special characteristics such as inherent information uncertainty and inherent data sample correlations, both within and across streams. We introduce a new data model, called Probabilistic Stream Relational Algebra (PSRA), that models a sensor data stream as a set of probabilistic data samples, along with prediction strategies for each attributes, capturing domain knowledge of inherent data correlations. We also explicitly associate every operation with schedule, specifying when next data sample should be produced, to facilitate resource management in sensor networks. We prove that operators in PSRA are non-blocking, thus making PSRA especially suitable for data stream processing. We also show that conventional relational model and existing deterministic data stream processing model can be modeled in PSRA.
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    PWave: Flexible Potential-based Routing Framework for Wireless Sensor Networks
    (2006-08-07) Liu, Haiyang; Zhang, Zhi-Li; Srivastava, Jaideep; Firoiu, Victor; Decleene, Brian
    Inspired by the potential theory in resistive electric networks, we present a routing framework called PWave that is fundamentally different from existing routing paradigms. PWave provides a systematic framework that guarantees proportional traffic allocation and supports global optimization of custom objectives. A fully distributed and highly scalable potential estimation algorithm and protocol were designed to ensure PWave to have low overhead and high resilience to network dynamics. Key properties of this framework are proved through theoretical analysis and verified through simulations.Using network lifetime maximization problem as one example, we illustrated the power of this framework by showing a 2.7 to 8 times lifetime extension over Directed Diffusion and up to 5 times lifetime extension over the exisitingenergy-aware multipath routing.