Data · dataset · 2026
AI-powered-Log-Anomaly-Detection-System
Listed in Teesside University Research Data Repository
Log files are the primary source of runtime telemetry for software applications, but their volume, speed, and unstructured nature make manual inspection or static rule-based monitoring (such as regular expressions or grep-based error filtering) highly impractical.
Description
Traditional methods cannot capture context exceptions such as gradual performance degradation or exception errors. It has patterns and silent security holes and requires constant manual rule maintenance.
This paper presents a real-time log anomaly detection system that bridges the gap between traditional monitoring and autonomous detection. The system integrates a Java Spring Boot backend for log processing, parsing, normalization, and persistence with a lightweight Python Flask microservice that serves an unsupervised Isolation forest model. Seven engineered features are extracted from each log entry: severity level, message length, number of error keywords, number of warning keywords, presence of stack traces, time of day, and weekend activities.
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In addition to marking outliers, the system normalizes raw Isolation Forest decision scores into a [0,1] probability range and generates textual reasoning for each anomaly to address the black-box explain ability gap of both deep learning based and LLM-based anomaly detectors. When tested on real-world workloads, the system enables low latency detection, online retraining, and provides real time alerts displayed on an interactive dashboard
Links
Where it is published
- DOI doi.org/10.17632/3v5bsvxdfz.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Machine learning · Social Science
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/3v5bsvxdfz.1 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |