3 citations · 6 across the 6 of their papers we have counts for
6 papers
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems
Van-Hau Pham, Hien Do Hoang, Phan Thanh Trung +3
In order to assess the risks of a network system, it is important to investigate the behaviors of attackers after successful exploitation, which is called post-exploitation. Althou…
XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection
Vu Le Anh Quan, Chau Thuan Phat, Kiet Van Nguyen +2
With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works l…
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors
Trong-Nghia To, Danh Le Kim, Do Thi Thu Hien +4
Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several res…
Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection
Tran Duc Luong, Vuong Minh Tien, Nguyen Huu Quyen +3
The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy br…
XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDN
Huynh Thai Thi, Ngo Duc Hoang Son, Phan The Duy +3
Advanced Persistent Threat (APT) attacks are highly sophisticated and employ a multitude of advanced methods and techniques to target organizations and steal sensitive and confiden…
VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model
Phan The Duy, Nghi Hoang Khoa, Nguyen Huu Quyen +4
This paper presents VulnSense framework, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning approach on graph-ba…