published_or_final_version / Computer Science / Master / Master of Philosophy

Our behaviour is guided by the ability to predict future events. The predictive saccade paradigm has been shown to be a valuable tool that uses eye movements to measure the control of predictive behaviour. In this task, subjects follow a visual target that alternates or “steps” between two fixed locations at either predictable or unpredictable inter-stimulus time intervals (ISIs). Response times can be measured by subtracting the time of saccade initiation from the time of target appearance. When the ISI is predictable, saccadic reaction times (SRTs) become predictive (SRT <100ms) within 3-4 target steps, but when the ISI is unpredictable, the SRTs remain reactive to target appearance (SRT >100ms). The goal of our study was to investigate neural mechanisms controlling prediction by contrasting areas in the brain that were more active for predictive (PRED) versus reactive (REACT) saccades in young healthy adults using functional magnetic resonance imaging (fMRI). fMRI analysis revealed two distinct neural networks more recruited for REACT and PRED tasks. We observed greater activation for the REACT task compared to the PRED task in oculomotor network areas including the frontal, supplementary, parietal eye fields, dorsolateral prefrontal cortex, thalamus, and putamen. These structures are all involved with the control of saccades. We also observed greater activation for the PRED task compared to the REACT task in default network areas, including the medial prefrontal cortex, posterior cingulate cortex, inferior parietal lobule, and hippocampus. These structures are known to be involved with passive thinking when subjects are not focused on their external environments. We also observed greater activation for the PRED task in the cerebellum (crus I), which may serve as the internal clock that drives the regular rhythmic behaviour observed for predictive saccades. In summary, our findings suggest brain activation in the PRED task reflects automated and motor-timed responses, while that for the REACT task reflects externally-driven responses. Therefore, the predictive saccade task is an excellent tool for measuring prediction involving fast internally-guided responses. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2011-08-12 10:21:37.744

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This dissertation investigates modeling techniques and computing algorithms for detection of anomalous contents and traffic flows of ingress Internet traffic at an enterprise network gateway. Anomalous contents refer to a large volume of ingress packets whose contents are not wanted by enterprise users, such as unsolicited electronic messages (UNE). UNE are often sent by Botnet farms for network resource exploitation, information stealing, and they incur high costs in bandwidth waste. Many products have been designed to block UNE, but most of them rely on signature database(s) for matching, and they cannot recognize unknown attacks. To address this limitation, in this dissertation I propose a Progressive E-Message Classifier (PEC) to timely classify message patterns that are commonly associated with UNE. On the basis of a scoring and aging engine, a real-time scoreboard keeps track of detected feature instances of the detection features until they are considered either as UNE or normal messages. A mathematical model has been designed to precisely depict system behaviors and then set detection parameters. The PEC performance is widely studied using different parameters based on several experiments. The objective of anomalous traffic flow detection is to detect selfish Transmission Control Protocol, TCP, flows which do not conform to one of the handful of congestion control protocols in adjusting their packet transmission rates in the face of network congestion. Given that none of the operational parameters in congestion control are carried in the transmitted packets, a gateway can only use packet arrival times to recover states of end to end congestion control rules, if any. We develop new techniques to estimate round trip time (RTT) using EWMA Lomb-Scargle periodogram, detect change of congestion windows by the CUSUM algorithm, and then finally predict detected congestion flow states using a prioritized decision chain. A high level finite state machine (FSM) takes the predictions as inputs to determine if a TCP flow follows a particular congestion control protocol. Multiple experiments show promising outcomes of classifying flows of different protocols based on the ratio of the aberrant transition count to normal transition count generated by FSM.

Thesis (M.S.)--Ohio University, March, 1982. / Title from PDF t.p.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 1670168). Also available via the Internet.

Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 112-115).

Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1993. / Vita. Abstract. Includes bibliographical references (leaves 87-88). Also available via the Internet.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1976. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 143-148).

As a recently emerging network paradigm, Software-Defined Networking (SDN) has attracted considerable attention from both industry and academia. The most significant advantage of SDN is that the paradigm disassociates the control logic (i.e., control plane) from the forwarding process (i.e., data plane), which are usually integrated into traditional network devices. Thanks to the property of centralized control, SDN enables the flexibility of dispatching flow policies to simplify network management. However, this property also makes the SDN environment vulnerable, which will cause network paralysis when the sole SDN controller runs malfunction. Although several works have been done on deploying multiple controllers to address the failure of a centralized controller, their drawbacks are leading to inefficiency and balance loss of controller utilization, provoking resource idling as well as being incapable to suffice flow outburst. Additionally, the network operators often put a great deal of effort into discovering failure nodes to recover their networks, which can be mitigated by applying failure detection before the network deterioration occurs. Network traffic prediction can serve as a practical approach to evaluate the state of the OpenFlow-based switch and consequently detect SDN node failures in advance. As far as prediction solution is concerned, most researchers investigate either statistical modeling approaches, such as Seasonal Autoregressive Integrated Moving Average (SARIMA), or Artificial Neural Network (ANN) methods, like Long Short-Term Memory (LSTM) Neural Network. Nonetheless, few of them study the model merging these two mechanisms regarding multi-step prediction. This thesis proposes a novel system associated with Network Function Virtualization (NFV) technique to enhance the resilience of SDN network. A hybrid prediction model based on the combination of SARIMA and LSTM is introduced as part of the detection module of this system, where the potential node breakdown can be readily determined so that it can implement smart prevention and fast recovery without human interaction. The results show the proposed scheme improves the performance concerning time complexity compared with that of previous work, reaching up to 95% accuracy while shortening the detection and recovery time by the new combined prediction model.