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Sat 3 May 2025 11:20 - 11:40 at 206 - Paper session 1 - continued Chair(s): Ita Ryan

Code vulnerabilities pose risks to software security. This paper combines data-driven (deep learning) and rulebased methods to enhance vulnerability detection, leveraging unsupervised learning to prune the hybrid model. A neural network encodes source code into vectors preserving semantic syntactic properties, which are classified using a Random Forest ensemble. A novel pruning algorithm, utilizing clustering techniques, removes low-impact trees to prevent overfitting. Evaluation on Java and C datasets shows that the proposed approach outperforms state-of-the-art rule-based, neural network, and vulnerability detection methods in accurately classifying methodlevel vulnerabilities

Sat 3 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
Paper session 1 - continuedEnCyCriS at 206
Chair(s): Ita Ryan University College Cork
11:00
20m
Paper
Cyberspace Vigilante or Security Sleuth: Understanding Who Threat Hunters Are
EnCyCriS
Samantha Hill University of Victoria, Alessandra Maciel Paz Milani University of Victoria, Callum Curtis University of Victoria, Arty Starr University of Victoria, Enrique Larios Vargas University of Victoria, Marcus Dunn University of Victoria, Margaret-Anne Storey University of Victoria
11:20
20m
Paper
Enhanced Detection of Code Vulnerability with Synergy between Data-Driven, Rule-Based and Unsupervised Learnings
EnCyCriS
Hibah Mohammed Ghouse Hubspot, Samiha Shimmi Northern Illinois University, Mona Rahimi Northern Illinois University
11:40
20m
Paper
Evaluating the Integration of Aurora zkSNARK in the Zupply Framework
EnCyCriS
Mohammadtaghi Badakhshan University of Waterloo, Guang Gong University of Waterloo
12:00
30m
Panel
Panel based discussions and open questions - morning session
EnCyCriS

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