Master Projects & Major PapersMaster Projects & Major Papershttps://hdl.handle.net/10155/772024-03-29T05:06:45Z2024-03-29T05:06:45ZDevelopment and validation of scaled electric combat vehicle virtual modelVaz, Glenn Xavierhttps://hdl.handle.net/10155/17612024-02-27T21:23:38Z2023-11-01T00:00:00ZDevelopment 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 grammarTanniru, Alekhyahttps://hdl.handle.net/10155/17602024-02-27T21:15:06Z2023-10-01T00:00:00ZFiltering 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.
2023-10-01T00:00:00ZEnhancing password security: a quest for optimal honeywordsNety, Meher Viswanathhttps://hdl.handle.net/10155/17332024-01-23T21:43:41Z2023-10-01T00:00:00ZEnhancing password security: a quest for optimal honeywords
Nety, Meher Viswanath
In this capstone report, our primary focus is on harnessing the capabilities of the GPT4 model to enhance password security through the generation of honeywords. Honeywords are decoy passwords designed to strengthen the security of sensitive systems by confusing potential attackers. The utilization of GPT4, a powerful language model developed by OpenAI, offers a n innovative approach to this challenge. By directly generating honeywords without relying on password segmentation, GPT4 introduces a unique dimension to password security. This approach is particularly valuable in thwarting targeted attacks, as honeywords generated by GPT4 are designed to deceive potential attackers effectively. In addition to the exploration of GPT4, this report also delves into the realm of Chunk-GPT3. Chunk-GPT3, as detailed in previous research, employs advanced language models to generate honeywords through the segmentation of passwords into discrete chunks. These chunks are ingeniously recombined to form decoy passwords. The re-engineered Chunk-GPT3 approach incorporates enhancements to the password segmentation process, including ”mapping digits to alphabets” and ”removal of digits” functions. These modifications aim to produce more potent and effective honeywords, ultimately elevating password security. The report includes a comprehensive comparative analysis of honeywords generated by the original Chunk-GPT3 approach and the re-engineered Chunk GPT3 approach, as well as honeywords created by GPT4. By assessing the effectiveness of these honeyword generation methods using the HWSimilarity metric, the report provides valuable insights into the strengths and weaknesses of each approach. Examining the capabilities of both GPT4 and Chunk-GPT3 in the context of honeyword generation, this report aims to provide a holistic perspective on cutting-edge strategies for safeguarding sensitive data in the ever-evolving digital landscape.
2023-10-01T00:00:00ZGuarding the gate: using honeywords to enhance authentication securityKoppada, Gowthamhttps://hdl.handle.net/10155/17322024-01-23T21:36:27Z2023-10-01T00:00:00ZGuarding the gate: using honeywords to enhance authentication security
Koppada, Gowtham
A honeyword (false password) can be defined as a duplicate password (rearranged) resembling the same characteristics of the original password. It is very challenging for any cyberpunk to distinguish between a real password and honeyword (containing PI). Using HGT’s (honeyword generation technique), these honeywords are generated in lump sum and the hashed honeywords are placed in an organization database with triggers to identify breach before it’s too late. In accordance with the previous research, the concept of HGT’s might fail if the generated honeywords does not contain the personal information of the user, making it easy for the attacker to perform targeted attack. It is a good practice to include the chucks containing PI or part of the original password of that particular user in generated honeywords to make it look natural. In order to generate such honeywords with chunks, the concept of prompt engineering in LLM (Large Learning Models) is used. In this report, we tried to improve the existing prompt, making it easy for the LLM to get deep understanding and to produce better throughput. In addition to that, we compared the base GPT Learning model (existing) with the newly upgraded GPT models like GPT-3.5-turbo and GPT-4. Considering the ‘strength of password‘ as a base factor, we came up with results and statements stating which model outperformed the others.
2023-10-01T00:00:00Z