Merge pull request #137 from dessertlab/publications-conference-1777469465425

Add 3 entries to Conferences

Author: francespi

bib/conference/10.1145/3722041.3723097.bib

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+@inproceedings{10.1145/3722041.3723097,
+author = {Cotroneo, Domenico and Grasso, Francesco C. and Natella, Roberto and Orbinato, Vittorio},
+title = {Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?},
+year = {2025},
+isbn = {9798400715631},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3722041.3723097},
+doi = {10.1145/3722041.3723097},
+abstract = {Vulnerability prediction is valuable in identifying security issues efficiently, even though it requires the source code of the target software system, which is a restrictive hypothesis. This paper presents an experimental study to predict vulnerabilities in binary code without source code or complex representations of the binary, leveraging the pivotal idea of decompiling the binary file through neural decompilation and predicting vulnerabilities through deep learning on the decompiled source code. The results outperform the state-of-the-art in both neural decompilation and vulnerability prediction, showing that it is possible to identify vulnerable programs with this approach concerning bi-class (vulnerable/non-vulnerable) and multi-class (type of vulnerability) analysis.},
+booktitle = {Proceedings of the 18th European Workshop on Systems Security},
+pages = {26–32},
+numpages = {7},
+keywords = {Binary Analysis, Deep Learning, Neural Decompilation, Security, Vulnerability Prediction},
+location = {Rotterdam, Netherlands},
+series = {EuroSec'25}
+}

bib/conference/Dellapenna2025CTI-HAL.bib

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+@INPROCEEDINGS{Dellapenna2025CTI-HAL,
+  author={Della Penna, Sofia and Natella, Roberto and Orbinato, Vittorio and Parracino, Lorenzo and Pianese, Luciano},
+  booktitle={2025 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)}, 
+  title={CTI-HAL: A Human-Annotated Dataset for Cyber Threat Intelligence Analysis}, 
+  year={2025},
+  volume={},
+  number={},
+  pages={69-78},
+  keywords={Performance evaluation;Accuracy;Large language models;Natural languages;Blogs;Organizations;Cyber threat intelligence;Reliability;Data mining;Computer security;Cyber Threat Intelligence;MITRE ATT&CK;Advanced Persistent Threats;Large Language Models;Cybersecurity},
+  doi={10.1109/EuroSPW67616.2025.00014}}

bib/conference/cti-hal.bib

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+@inproceedings{cti-hal,
+  author={Della Penna, Sofia and Natella, Roberto and Orbinato, Vittorio and Parracino, Lorenzo and Pianese, Luciano},
+  booktitle={2025 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)}, 
+  title={CTI-HAL: A Human-Annotated Dataset for Cyber Threat Intelligence Analysis}, 
+  year={2025},
+  volume={},
+  number={},
+  pages={69-78},
+  keywords={Performance evaluation;Accuracy;Large language models;Natural languages;Blogs;Organizations;Cyber threat intelligence;Reliability;Data mining;Computer security;Cyber Threat Intelligence;MITRE ATT&CK;Advanced Persistent Threats;Large Language Models;Cybersecurity},
+  doi={10.1109/EuroSPW67616.2025.00014}}