Institution profile

New Mexico State University

Academic institutionnorthamerica · us
Official website
Research library29linked papers
Opportunities0open roles
Selected work

Representative Papers

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

Jul 14, 2026

This study addresses the high false positive rates in high-traffic network intrusion detection caused by class imbalance. To this end, the authors propose BARS, a two-stage feature selection method that replaces the conventional global mean with the mean of benign traffic as an anchor point and incorporates an order-preserving decorrelation mechanism to more accurately model the benign baseline under imbalanced conditions. BARS is the first approach to explicitly align feature selection anchors with the benign distribution, effectively mitigating anchor shift while maintaining linear time complexity and low memory overhead. Experimental results on the UNSW-NB15 and CICDDoS2019 datasets show that BARS reduces false positive rates by 15.4%–23% compared to the CMD method, achieves comparable recall and macro-F1 scores, and incurs significantly lower memory usage than mutual information–based approaches.

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When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

Jul 06, 2026

This work addresses a critical security vulnerability in personal AI agents: their long-term memory mechanisms, when integrating dialogue and action planning, are susceptible to memory poisoning attacks from untrusted sources due to inadequate safety governance, potentially leading to information leakage or behavioral manipulation. The study systematically uncovers this threat for the first time and introduces GhostWriter, a two-stage memory poisoning attack method, alongside Agentic Memory Sentry (AM-Sentry), a defense framework that combines secure memory storage strategies with retrieval filtering mechanisms. Experimental results demonstrate that GhostWriter achieves approximately 98% injection success and 60% average activation rates across mainstream agent platforms, while AM-Sentry effectively mitigates these attacks with minimal impact on the agent’s core task performance.

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Recent publications

Latest Papers

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

Jul 14, 2026

This study addresses the high false positive rates in high-traffic network intrusion detection caused by class imbalance. To this end, the authors propose BARS, a two-stage feature selection method that replaces the conventional global mean with the mean of benign traffic as an anchor point and incorporates an order-preserving decorrelation mechanism to more accurately model the benign baseline under imbalanced conditions. BARS is the first approach to explicitly align feature selection anchors with the benign distribution, effectively mitigating anchor shift while maintaining linear time complexity and low memory overhead. Experimental results on the UNSW-NB15 and CICDDoS2019 datasets show that BARS reduces false positive rates by 15.4%–23% compared to the CMD method, achieves comparable recall and macro-F1 scores, and incurs significantly lower memory usage than mutual information–based approaches.

0 citationsRead paper

When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

Jul 06, 2026

This work addresses a critical security vulnerability in personal AI agents: their long-term memory mechanisms, when integrating dialogue and action planning, are susceptible to memory poisoning attacks from untrusted sources due to inadequate safety governance, potentially leading to information leakage or behavioral manipulation. The study systematically uncovers this threat for the first time and introduces GhostWriter, a two-stage memory poisoning attack method, alongside Agentic Memory Sentry (AM-Sentry), a defense framework that combines secure memory storage strategies with retrieval filtering mechanisms. Experimental results demonstrate that GhostWriter achieves approximately 98% injection success and 60% average activation rates across mainstream agent platforms, while AM-Sentry effectively mitigates these attacks with minimal impact on the agent’s core task performance.

0 citationsRead paper