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Full-Text Articles in Engineering
Cyber Risks In Ontario Online Elections, James D. Brunet
Cyber Risks In Ontario Online Elections, James D. Brunet
Electronic Thesis and Dissertation Repository
Online voting is increasingly prevalent in Ontario's municipalities, despite a lack of regulated technological and procedural safeguards. Individual municipalities, lacking deep knowledge of online voting technologies, are responsible for procuring technology from private vendors which make security and privacy claims that are difficult to verify. These reasons, among others, have contributed to an anomalous environment where election technology, security, and procedures diverge greatly from other robust democracies that use electronic voting. This thesis demonstrates this divergence by first presenting a novel security vulnerability in a popular online voting system used in Ontario, as well as the difficulty communicating this risk …
Behavioral Biometrics-Based Continuous User Authentication, Sanket Vilas Salunke
Behavioral Biometrics-Based Continuous User Authentication, Sanket Vilas Salunke
Electronic Thesis and Dissertation Repository
The field of cybersecurity is exploring new ways to defend against cyber-attacks, including a technique called continuous user authentication. This method uses keystroke (typing) data to continuously match the user's typing pattern with patterns previously recorded using artificial intelligence (AI) to identify the user. While this approach has the potential to improve security, it also has some challenges, including the time it takes to register a user, the performance of machine learning algorithms on real-world data, and latency within the system. In this study, the researchers proposed solutions to these issues by using transfer learning to reduce user registration time, …
Anonymization & Generation Of Network Packet Datasets Using Deep Learning, Spencer K. Vecile
Anonymization & Generation Of Network Packet Datasets Using Deep Learning, Spencer K. Vecile
Electronic Thesis and Dissertation Repository
Corporate networks are constantly bombarded by malicious actors trying to gain access. The current state of the art in protecting networks is deep learning-based intrusion detection systems (IDS). However, for an IDS to be effective it needs to be trained on a good dataset. The best datasets for training an IDS are real data captured from large corporate networks. Unfortunately, companies cannot release their network data due to privacy concerns creating a lack of public cybersecurity data. In this thesis I take a novel approach to network dataset anonymization using character-level LSTM models to learn the characteristics of a dataset; …
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Electronic Thesis and Dissertation Repository
This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect …