Students currently enrolled in the Computer Science graduate program here at Brock University will be required to submit an electronic copy of their final Major Research Paper to this repository as part of graduation requirements.

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Recent Submissions

  • A Novel DDoS Detection and Multi-Class Classification Method: A Graph Convolutional Network Approach

    Saunders, Braden
    Distributed Denial of Service (DDoS) is an attack that overwhelms the cyber critical infrastructure system with malicious packets causing it to become unresponsive, which precludes legitimate users from accessing the target system. This work leverages a deep learning method known as Graph Convolutional Network (GCN) to empower DDoS detection systems. The proposed GCN model consists of three hidden layers, each with 128 neurons. Considering the Canadian Institute for Cybersecurity CIC-IDS 2017 dataset, the proposed model achieves an overall accuracy of 99.95%, along with a value of 99.95% for each of the precision, recall, and F1-score metrics for the binary DDoS classification problem. For the multi-class DDoS classification problem, the model scores an overall accuracy of 98.94% and precision, recall, and F1-score values of over 93% for all classes. These results support the use of the proposed GCN DDoS detection method in practice.
  • ACS-IoT: A CNN-BiLSTM Model for Anomaly Classification in IoT Networks

    GUAN, YUE
    This work proposes an Anomaly Classification System for IoT (ACS-IoT). The proposed system contains a pipeline of machine learning and deep learning algorithms for the effective classification of anomalies and their sub-types. Machine learning algorithms are adopted to distinguish between normal data and anomaly data. The deep networks, on the other hand, are used to perform anomaly-type classification. We propose the use of the Synthetic Minority Oversampling Technique (SMOTE) to address the data imbalance problem and Particle Swarm Optimization (PSO) as a feature selection mechanism to improve accuracy as well as execution time. The proposed system proved to be accurate as well as precise when tested on a publicly available IoT dataset.
  • An Improved Sufficient Condition for Routing on the Hypercube with Blocking Nodes

    Wang, Wenjie
    We study the problem of routing between two nodes in a hypercube with blocking nodes using shortest path. This problem has been previously studied by other researchers, they have proposed a few algorithms to solve the problem. Among the work done, one has found several sufficient conditions for such a path to exist. One such condition states that a shortest path between node 0^n and 1^n exists if the number of blocking nodes is less than n in an n-dimensional hypercube. We improve this condition by proposing the condition that if the size of a SDR (system of distinct representatives) for the blocking nodes is less than n, then a shortest path between the two nodes 0^n and 1^n exists. Since the number of blocking nodes can be greater than or equal to n, while the size of SDR is less than n, thus this result improves the previous sufficient condition.