PhD Alumni
Dr. Aikaterini Kanta
University College Dublin
Aikaterini Kanta completed her PhD, Context-Based Password Cracking for Digital Investigation, under the supervision of Assoc. Prof. Mark Scanlon and Dr. Iwen Coisel.
Research Output
Publications
Context Based Password Cracking Dictionary Expansion Using Generative Pre-trained Transformers
2024 Cyber Research Conference - Ireland (Cyber-RCI)
This paper explores the effectiveness of combining a strategic contextual approach with large language models in password cracking. The authors create context-based password dictionaries through training PassGPT models with contextual information, demonstrating improved password cracking efficiency and accuracy.
A Comprehensive Evaluation on the Benefits of Context Based Password Cracking for Digital Forensics
Journal of Information Security and Applications
This paper evaluates the benefits of context-based password cracking for digital forensics, demonstrating that targeted approaches can increase the likelihood of success when contextual information is available. The study presents an experimental methodology and results section analyzing the approach's performance across ten datasets, proving the impact of context in password cracking.
Context-Based Password Cracking for Digital Investigation
School of Computer Science, University College Dublin
This thesis presents a context-based password cracking approach for digital investigation, introducing a methodology and framework for creating and assessing custom dictionary wordlists for dictionary-based password cracking attacks. The approach leverages contextual information to generate bespoke password candidate lists, achieving significant improvements over traditional approaches, with over 50% improvement in some instances.
Harder, Better, Faster, Stronger: Optimising the Performance of Context-Based Password Cracking Dictionaries
Forensic Science International: Digital Investigation Vol. 44S pp. 301507
This paper presents a methodology for optimising and ranking contextual wordlists for password cracking, tailored to the suspect in a digital forensic investigation. The approach is evaluated with data leaks from compromised online communities, demonstrating its effectiveness in finding passwords not recovered by traditional methods.
A Novel Dictionary Generation Methodology for Contextual-Based Password Cracking
IEEE Access Vol. 10 pp. 59178-59188
This paper introduces a novel dictionary generation methodology for contextual-based password cracking, enabling the creation of custom dictionary word lists for dictionary-based password cracking attacks. The approach leverages contextual information encountered during an investigation, such as user habits and personal information, to generate targeted password candidates. This methodology has the potential to expedite password cracking processes in law enforcement investigations.
PCWQ: A Framework for Evaluating Password Cracking Wordlist Quality
The 12th EAI International Conference on Digital Forensics and Cyber Crime
This paper presents PCWQ, a novel framework for evaluating the quality of password cracking wordlists. The framework assesses wordlists based on several interconnecting metrics, including final percentage of passwords cracked, number of guesses until target, progress over time, size of wordlist, and better performance with stronger passwords. The authors conduct a preliminary analysis to demonstrate the framework's evaluation process.
How Viable is Password Cracking in Digital Forensic Investigation? Analyzing the Guessability of over 3.9 Billion Real-World Accounts
Forensic Science International: Digital Investigation Vol. 37 pp. 301186
This study analyzed over 3.9 billion real-world passwords to assess their guessability and identify patterns in password construction. The analysis reveals that certain semantic classes are more common than others, indicating the importance of user context in password selection. The study also evaluates the effectiveness of password cracking tools and techniques, providing insights for digital investigators.
A Survey Exploring Open Source Intelligence for Smarter Password Cracking
Forensic Science International: Digital Investigation Vol. 35 pp. 301075
This paper explores the potential of Open Source Intelligence (OSINT) for more efficient password cracking in digital investigations. A comprehensive survey of password strength, cracking, and OSINT is presented, along with an analysis of password structure and demographic factors influencing password selection. The authors discuss the challenges of password cracking and the potential impact of OSINT on law enforcement.
Smarter Password Guessing Techniques Leveraging Contextual Information and OSINT
6th IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security)
This paper proposes smarter password guessing techniques that leverage contextual information and Open Source Intelligence (OSINT) to improve password recovery rates. The authors explore the use of OSINT to gather information about a suspect's online and offline life, which can be used to make educated guesses about their password. The research aims to create a bespoke, personalized dictionary list to feed into password cracking tools.
Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
The 8th International Workshop on Cyber Crime (IWCC), held at the 14th International Conference on Availability, Reliability and Security (ARES)
This paper presents an ensemble learning approach to improve facial age estimation for borderline adulthood cases. The authors develop a deep learning model (DS13K) and fine-tune it on the Deep Expectation (DEX) model to achieve an accuracy of 68% for the age group 16-17 years old, outperforming DEX by 4 times. The study also evaluates existing cloud-based facial age prediction services.
Improving the Accuracy of Automated Facial Age Estimation to Aid CSEM Investigations
Digital Investigation Vol. 28 pp. S142
This study evaluates existing age prediction services and introduces a deep learning model, DS13K, to improve the accuracy of underage facial age estimation in child sexual exploitation material (CSEM) investigations. The model outperforms existing services, particularly in the borderline adulthood age range (16-17 years old), with an accuracy rate of 68%.