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Ren-Hung Hwang

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2026

Reinforcement Learning Meets LLM Honeypots: A MITRE Engage-Aligned Approach

The growing sophistication of cyberattacks, accelerated by large language models (LLMs), highlights the limitations of traditional honeypots, which often lack realism, require heavy maintenance, and rely on static deception strategies. Recent LLM-based honeypots generate fluent, context-aware responses but cannot adapt...

Ren-Hung Hwang, Jiao-Chuan Huang, Yuan-Cheng Lai et al. · 0 citations
Open access 2026

LLM-Driven Alert Correlation and Lifecycle Prediction With Threat Intelligence for Attack Forensics

Security Operations Centers face overwhelming volumes of alerts from firewalls, intrusion detection systems, and other sources. These alerts often lack temporal and semantic coherence, hindering analysts from reconstructing full MITRE ATT&CK lifecycles or anticipating subsequent techniques. To address this, we propose...

Ying-Dar Lin, Chun-Yi Chang, Didik Sudyana et al. · 0 citations
2026

Bridging Training–Deployment Gap in Intrusion Detection With Source-Free Domain Adaptation

Machine learning (ML)–based intrusion detection systems (IDS) frequently degrade when deployed across heterogeneous networks due to domain shifts in traffic composition and monitoring configurations. Conventional domain adaptation (DA) methods mitigate this issue by aligning source and target distributions, but they of...

Didik Sudyana, Wong Yu Xuan, Laurens D’hooge et al. · 0 citations
Open access Aug 2026

Proximal Policy Optimization for Latency-Aware Service Function Chain Placement in Edge-Enabled Networks

This work proposes an enhanced Proximal Policy Optimization (PPO) framework for resource-aware and latency-sensitive SFC placement in edge-enabled networks, and demonstrates the applicability of the proposed framework in mission-critical and latency-sensitive service environments.

Nithin Melala Eshwarappa, Ching-Hsien Hsu, Hojjat Baghban et al. · 0 citations

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