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Found 5 papers, 0 papers with code
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems
The agents automatically select actions and launch attacks on the environments and achieve over 84\% of successful attacks with under 55 attack steps given.
XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection
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.
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors
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).
VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model
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.
XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDN
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.
