Graduate & Postdoctoral Studies
https://hdl.handle.net/10155/4
Graduate & Postdoctoral Studies2024-03-28T12:06:04ZDevelopment and validation of scaled electric combat vehicle virtual model
https://hdl.handle.net/10155/1761
Development and validation of scaled electric combat vehicle virtual model
Vaz, Glenn Xavier
This research focuses on an 8x8 scaled electric combat vehicle (SECV) and aims to create a virtual model made of the same vehicle on a vehicle dynamics simulation software using parameters from the actual vehicle. In the proposed vehicle, each wheel is independently driven and steered. MATLAB and Simulink software were used to design and implement the electric powertrain while TruckSim Modelling and Simulation software was used to simulate the on-road conditions tests. The simulation data was then compared with the experimental data obtained from the physical test scenarios.
2023-11-01T00:00:00ZFiltering honeywords using probabilistic context free grammar
https://hdl.handle.net/10155/1760
Filtering honeywords using probabilistic context free grammar
Tanniru, Alekhya
With the growing prevalence of cyber threats, effective password policies have become crucial for safeguarding sensitive information. Traditional password-based authentication techniques are open to a number of threats. The idea of honeywords, which was developed to improve password-based security, entails using dummy passwords with real ones to build a defence mechanism based on deceit. The importance of password policies is examined in the context of honeywords in this study, emphasizing how they might improve security and reduce password-related risks. We present the idea of using the existing passwords to extract a policy and using this policy to filter good and strong passwords. Through this capstone project, we aim to contribute to the broader understanding of honeywords and their role in improving password-based authentication systems. I have conducted experiments on Chunk-GPT3 and GPT 4 models, to see which one of the models produces more honeywords which are very similar to the real passwords.
2023-10-01T00:00:00ZDL-based defense against polymorphic network attacks
https://hdl.handle.net/10155/1759
DL-based defense against polymorphic network attacks
Sabeel, Ulya
Network security is of vital importance in our world dominated by internet systems. These systems are vulnerable to large-scale rapidly evolving attacks by sophisticated cyber attackers who can have an upper edge over the defensive systems. Artificial Intelligence (AI) based intrusion detection systems provide effective defense mechanisms against cyber attacks. However, these techniques often rely on the same dataset for training and validation as well as evaluation of AI models. Current research [1] also confirms that such trained models can accurately identify known/typical network attacks but perform poorly when faced with continuously evolving atypical/polymorphic cyberattacks. Therefore, it is crucial to develop and train an AI-based Intrusion Detection System (IDS) that proactively learns to resist infiltration by such dynamically changing attacks.
For this purpose, in this research work, we propose an AI-based IDS system that can monitor and detect polymorphic network attacks with high confidence levels. We propose a novel hybrid adversarial model that leverages the best characteristics of a Conditional Variational Autoencoder (CVAE) and a Generative Adversarial Network (GAN). Our system generates adversarial polymorphic attacks against the IDS to examine its performance and incrementally retrains it to strengthen its detection of new attacks, specifically for minority attack samples in the input data. The employed attack quality analysis ensures that the adversarial atypical/polymorphic attacks generated through our system resemble realistic network attacks. Our experiments showcase the exceptional performance of the proposed IDS by training it using the CICIDS2017 and CICIoT2023 benchmark datasets and evaluating its performance against several atypical/polymorphic attack flows. The results indicate that the proposed technique, through adaptive training, learns the pattern of dynamically changing atypical/polymorphic attacks and identifies such attacks with high IDS proficiency. Additionally, our IDS surpasses various state-of-the-art anomaly detection and class balancing techniques.
2024-01-01T00:00:00Z“Can we keep blogging?”: Analyzing blogging in a grade six classroom as a trauma-informed practice for students and educators
https://hdl.handle.net/10155/1758
“Can we keep blogging?”: Analyzing blogging in a grade six classroom as a trauma-informed practice for students and educators
Allum, Heidi M.
Trauma-informed practice is a high-profile term in education, with multiple definitions and implementation strategies for classroom practice. Through phenomenological and case study methods, this study examines how one teacher uses blogging as a trauma-informed practice micro-move. Through blogging, trauma-informed practices address student safety, choice, and empowerment. The teacher changed trauma-informed practices based on student feedback from students' blogs. Results showed that blogging could be a trauma-informed practice. The teacher made subtle, yet powerful, changes in practice based on student feedback through blogging. More research is suggested for implementing trauma-informed micro-moves in the classroom and their impact on student well-being.
2024-02-01T00:00:00Z