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.· IEEE Transactions on Network...· 0 citations
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.· IEEE Transactions on Machine...· 0 citations
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.· IEEE Transactions on Network...· 0 citations
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.· ACM Transactions on Modeling...· 0 citations
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