An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
arXiv SecurityArchived Jun 09, 2026✓ Full text saved
arXiv:2606.08270v1 Announce Type: new Abstract: University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations. This paper presents an AI-based security a
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 6 Jun 2026]
An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response
Joseph Walusimbi, Joshua Benjamin Ssentongo
University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations. This paper presents an AI-based security agent for ACMIS that combines supervised anomaly detection, behavioural analytics, and a natural language processing chatbot for secure password recovery. The agent monitors five operational layers: authentication, authorisation, financial transactions, user behaviour, and system health, and responds through a four-tier risk escalation framework. A modular architecture allows the core engine to be extended to other institutional systems. Experiments on a simulated ACMIS event log dataset demonstrate a threat detection macro-average F1 of 0.91, compared to 0.49 for a rule-based baseline, with critical-tier automated response latency under 300 ms at the 95th percentile.
Comments: 5 pages, 1 figure, 3 tables,
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
Cite as: arXiv:2606.08270 [cs.CR]
(or arXiv:2606.08270v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.08270
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Submission history
From: Joseph Walusimbi [view email]
[v1] Sat, 6 Jun 2026 17:33:31 UTC (11 KB)
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