Titlebook: Computer Security – ESORICS 2024; 29th European Sympos Joaquin Garcia-Alfaro,Rafał Kozik,Sokratis Katsika Conference proceedings 2024 The E

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Predicting Code Vulnerability Types via Heterogeneous GNN Learningdiverse relationships within ICCPGs, thereby enabling the classification of vulnerabilities into specific types. The proposed approach achieved an accuracy of 92.8% in the C language, covering up to 108 different vulnerability types, and the top-2 classification further improved this score to 95.5%.

Confidential

WASMixer: Binary Obfuscation for WebAssembly(de)cryption method to minimize the performance impact on memory data, and code splitting/reconstructing algorithms to handle Wasm highly-structured control flow. Extensive experiments demonstrate the correctness, effectiveness and efficiency of .. Our research has shed light on the promising direct

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TGRop: Top Gun of Return-Oriented Programming Automationt . outperforms all existing approaches by more than 1.62–3.11 times. Additionally, we validate the rationale behind its design via analytical experiments. When running . against the newest ROP mitigations, we discovered their weaknesses and reported to vendors.

overreach

SerdeSniffer: Enhancing Java Deserialization Vulnerability Detection with Function Summariesd treat uninitialized variables as taint sources. We also merge the summary information of called functions to facilitate polymorphic analysis. Furthermore, . includes a vulnerability detection algorithm that starts from dangerous functions and uses summary information and propagation rules in a bot

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