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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response

arXiv Security Archived 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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    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 Focus to learn more Submission history From: Joseph Walusimbi [view email] [v1] Sat, 6 Jun 2026 17:33:31 UTC (11 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.AI cs.ET References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    Article Info
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 09, 2026
    Archived
    Jun 09, 2026
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