no code implementations • 27 Sep 2023 • Van-Hau Pham, Hien Do Hoang, Phan Thanh Trung, Van Dinh Quoc, Trong-Nghia To, Phan The Duy
The agents automatically select actions and launch attacks on the environments and achieve over 84\% of successful attacks with under 55 attack steps given.
no code implementations • 26 Sep 2023 • Vu Le Anh Quan, Chau Thuan Phat, Kiet Van Nguyen, Phan The Duy, Van-Hau Pham
Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities.
no code implementations • 25 Sep 2023 • Trong-Nghia To, Danh Le Kim, Do Thi Thu Hien, Nghi Hoang Khoa, Hien Do Hoang, Phan The Duy, Van-Hau Pham
Our proposed FeaGAN model is built based on MalGAN by incorporating an RL model called the Deep Q-network anti-malware Engines Attacking Framework (DQEAF).
no code implementations • 15 Sep 2023 • Huynh Thai Thi, Ngo Duc Hoang Son, Phan The Duy, Nghi Hoang Khoa, Khoa Ngo-Khanh, Van-Hau Pham
To effectively protect against APTs, detecting and predicting APT indicators with an explanation from Machine Learning (ML) prediction is crucial to reveal the characteristics of attackers lurking in the network system.
no code implementations • 15 Sep 2023 • Phan The Duy, Nghi Hoang Khoa, Nguyen Huu Quyen, Le Cong Trinh, Vu Trung Kien, Trinh Minh Hoang, Van-Hau Pham
This paper presents VulnSense framework, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning approach on graph-based and natural language processing (NLP) models.