arXiv subjects

Tri Gia Nguyen

Publications and source records attributed to Tri Gia Nguyen.

4 recordsLinked to original sources

arXiv2026

DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense

Advanced Persistent Threats (APTs) are difficult to detect and interpret due to their multi-stage and stealthy nature. While recent autonomous defense systems leverage provenance graphs and learning-based models for detection and mitigation, their outputs remain largely machine-oriented and difficult for analysts to interpret. Large language models (LLMs) offer a promising interface for report generation, but often produce hallucinated or weakly grounded content. In this paper, we propose DeepFaith, an evidence-grounded framework for faithful incident reporting in multi-stage APT defense. DeepFaith transforms structured outputs from autonomous defense and explainability modules into natural-language reports that are explicitly aligned with underlying system evidence. The framework integrates a unified evidence representation, evidence-grounded prompting, faithfulness-aware generation, and post-generation verification to ensure that all generated statements are supported. Experiments in a realistic enterprise testbed demonstrate that DeepFaith improves faithfulness from 0.68 to 0.92, reduces unsupported claims from 0.32 to 0.08, and increases temporal consistency from 0.6 to 0.88, while maintaining concise reports and lower error rates than existing template-based and LLM-based solutions. These results show that evidence-grounded generation enables reliable, interpretable, and actionable reporting for security operations centers.

cs.CR↗

arXiv2026

DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns

This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is formulated as a partially observable Markov decision process (POMDP), in which host provenance and network telemetry are fused into unified provenance graphs. Building on our prior work (StageFinder), DeepStage employs a graph neural network encoder and an LSTM-based stage estimator to infer probabilistic attacker stages aligned with the MITRE ATT&CK framework. The resulting stage beliefs, together with graph embeddings, are used to guide a hierarchical Proximal Policy Optimization (PPO) agent that selects defense actions across monitoring, access control, containment, and remediation. Experiments in a realistic enterprise testbed with CALDERA-driven APT playbooks show that DeepStage achieves an average F1-score of 0.887 and a mitigation success rate of 84.7%, outperforming a risk-aware DRL baseline by 21.8% in F1-score and 16.2% in mitigation success. The results demonstrate effective stage-aware and cost-efficient autonomous cyber defense.

cs.CR↗

arXiv2022

Quality-Driven Energy-Efficient Big Data Aggregation in WBANs

In the Internet-of-Things (IoT) era, the development of Wireless Body Area Networks (WBANs) and their applications in big data infrastructure has gotten a lot of attention from the medical research community. Since sensor nodes are low-powered devices that require heterogeneous Quality-of-Service (QoS), managing large amounts of medical data is critical in WBANs. Therefore, effectively aggregating a large volume of medical data is important. In this context, we propose a quality-driven and energy-efficient big data aggregation approach for cloud-assisted WBANs. For both intra-BAN (Phase I) and inter-BAN (Phase II) communications, the aggregation approach is cost-effective. Extensive simulation results show that the proposed approach DEBA greatly improves network efficiency in terms of aggregation delay and cost as compared to existing schemes.

cs.NI↗

arXiv2019

Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning

Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it is challenging to choose a proper degree of traffic flow handling granularity while proactively protecting forwarding devices from getting overloaded. In this paper, we propose a novel traffic flow matching control framework called Q-DATA that applies reinforcement learning in order to enhance the traffic flow monitoring performance in SDN based networks and prevent traffic forwarding performance degradation. We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity. It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm. Next, we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require. Our novel approach is realized as a REST SDN application and evaluated in an SDN environment. Through comprehensive experiments, the results show that---compared to the default behavior of common SDN controllers and to our previous DATA mechanism---the new Q-DATA framework yields a remarkable improvement in terms of traffic forwarding performance degradation protection of SDN switches while still providing the most detailed traffic flow information on demand.

cs.NI↗