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Home/Authors/Ke Xu

Ke Xu

10 indexed papers

Recent (6 mo)
6
With code
0
Influential cites
0
Benchmarked
0

Publications per year

10
26

Top categories

Crypto×5AI×5Networking×2ML×2Complexity×1Algorithms×1Info Theory×1Logic×1

Frequent co-authors

Qi Li5×
Canyixing Cui2×
Tao Wu2×
Xingping Xian2×
Xiao-Ke Xu2×
Mao Wang2×

Research Timeline

2026
How Far Should We Need to Go : Evaluate Provenance-based Intrusion Detection Systems in Industrial Scenarios

This paper systematically evaluates Provenance-based Intrusion Detection Systems (PIDSes) in real industrial scenarios, revealing that existing systems struggle with data heterogeneity, advanced attacks, and high false positive rates.

Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs

This paper systematically identifies and demonstrates multiple session manipulation attacks against VPN connection tracking frameworks, revealing widespread vulnerabilities in popular VPN services.

AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents

The paper introduces AgentWard, a lifecycle-oriented, defense-in-depth architecture designed to systematically secure autonomous AI agents by protecting them across all stages of their operation.

AFL-ICP: Enhancing Industrial Control Protocol Reliability via Specification-Guided Fuzzing

AFL-ICP is a novel specification-driven fuzzing framework that significantly enhances the security testing of industrial control protocols by detecting subtle semantic and logic bugs missed by traditional fuzzers.

Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

The paper introduces MOV-Bench, a challenging benchmark for multi-hop audio-visual reasoning, and proposes AOP-Agent, an agentic framework that significantly improves open-source Omni-LLMs' ability to perform active cross-modal perception.

Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

The paper introduces MOV-Bench, a challenging benchmark for multi-hop audio-visual reasoning, and proposes AOP-Agent, an agentic framework that significantly improves open-source Omni-LLMs' ability to perform active cross-modal perception.

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

GJDNet proposes a joint disentanglement framework to enhance the robustness of Graph Neural Networks against adversarial attacks by simultaneously stabilizing node representations and decision boundaries across diverse graph connectivity types.

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

GJDNet proposes a joint disentanglement framework to enhance the robustness of Graph Neural Networks against adversarial attacks by simultaneously stabilizing node representations and decision boundaries across diverse graph connectivity types.

Understanding the Stealthy BGP Hijacking Risk in the ROV Era

This paper proposes a framework to assess the risk of stealthy BGP hijacking.

Self-Referential $K$-SAT and the Finite Analogue of Gödel's Incompleteness Theorem

This paper establishes a finite combinatorial version of Gödel's incompleteness theorems in Boolean $K$-SAT, resolving assignment correlations and constructing structurally irreducible SAT/UNSAT pairs.

Highlighted terms show continued research focus across papers

Papers

cs.CCcs.DScs.ITTheoreticalRecentJul 2, 2026

Self-Referential $K$-SAT and the Finite Analogue of Gödel's Incompleteness Theorem

Wen Fang, Xianxian Li, Jun Liu, Jie Luo +2 more

This paper establishes a finite combinatorial version of Gödel's incompleteness theorems in Boolean $K$-SAT, resolving assignment correlations and constructing structurally irreducible SAT/UNSAT pairs…

View →
cs.CRcs.NIEmpirical
Recent
Jun 22, 2026

Understanding the Stealthy BGP Hijacking Risk in the ROV Era

Yihao Chen, Qi Li, Ke Xu, Zhuotao Liu +1 more

This paper proposes a framework to assess the risk of stealthy BGP hijacking.

View →
cs.LGcs.AIRecentJun 1, 2026

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

Canyixing Cui, Tao Wu, Xingping Xian, Xiao-Ke Xu +2 more

GJDNet proposes a joint disentanglement framework to enhance the robustness of Graph Neural Networks against adversarial attacks by simultaneously stabilizing node representations and decision boundar…

View →
cs.LGcs.AIRecentJun 1, 2026

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

Canyixing Cui, Tao Wu, Xingping Xian, Xiao-Ke Xu +2 more

GJDNet proposes a joint disentanglement framework to enhance the robustness of Graph Neural Networks against adversarial attacks by simultaneously stabilizing node representations and decision boundar…

View →
cs.AIRecentMay 27, 2026

Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

Ke Xu, Yuhao Wang, Ziyang Cheng, Hongcheng Liu +2 more

The paper introduces MOV-Bench, a challenging benchmark for multi-hop audio-visual reasoning, and proposes AOP-Agent, an agentic framework that significantly improves open-source Omni-LLMs' ability to…

View →
cs.AIRecentMay 27, 2026

Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

Ke Xu, Yuhao Wang, Ziyang Cheng, Hongcheng Liu +2 more

The paper introduces MOV-Bench, a challenging benchmark for multi-hop audio-visual reasoning, and proposes AOP-Agent, an agentic framework that significantly improves open-source Omni-LLMs' ability to…

View →
cs.CRcs.NIcs.SEMay 6, 2026

AFL-ICP: Enhancing Industrial Control Protocol Reliability via Specification-Guided Fuzzing

Jiaying Meng, Xuewei Feng, Qi Li, Min Liu +1 more

AFL-ICP is a novel specification-driven fuzzing framework that significantly enhances the security testing of industrial control protocols by detecting subtle semantic and logic bugs missed by traditi…

View →
cs.CRcs.AIApr 27, 2026

AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents

Yixiang Zhang, Xinhao Deng, Jiaqing Wu, Yue Xiao +2 more

The paper introduces AgentWard, a lifecycle-oriented, defense-in-depth architecture designed to systematically secure autonomous AI agents by protecting them across all stages of their operation.

View →
cs.CRApr 5, 2026

Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs

Yuxiang Yang, Ao Wang, Xuewei Feng, Qi Li +1 more

This paper systematically identifies and demonstrates multiple session manipulation attacks against VPN connection tracking frameworks, revealing widespread vulnerabilities in popular VPN services.

View →
cs.CRMar 24, 2026

How Far Should We Need to Go : Evaluate Provenance-based Intrusion Detection Systems in Industrial Scenarios

Yue Xiao, Ling Jiang, Sen Nie, Ding Li +3 more

This paper systematically evaluates Provenance-based Intrusion Detection Systems (PIDSes) in real industrial scenarios, revealing that existing systems struggle with data heterogeneity, advanced attac…

View →