Jun 2026· 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0· pp. 1-6· 0 citations· 15 references
Abstract
Modern Software-as-a-Service (SaaS) environments generate voluminous, heterogeneous logs that exhibit rapid schema drift and semantic sparsity, rendering traditional template-based detection methods ineffective. This paper presents LLM-EP, a unified framework that utilizes Large Language Models (LLMs) for proactive error prediction via semantic log analysis. Unlike existing heuristics, LLM-EP employs a drift resilient semantic parser and an embedding driven normalization engine to maintain accuracy amidst evolving log formats. We propose a hybrid offline-online architecture that balances deep semantic understanding with real-time inference requirements, incorporating a robust outof-vocabulary (OOV) mechanism for zero-shot generalization to unseen log patterns. Evaluation on production SaaS datasets indicates that LLM-EP achieves a 25% gain in parsing accuracy and a 12% improvement in F1-score over state-of-the-art baselines. Most notably, LLM-EP reduces Mean Time to Detect (MTTD) by up to 60%, demonstrating its efficacy as a scalable foundation for automated site reliability engineering.
This paper presents a cloud-native, transformer-based multimodal intelligence framework that integrates Large Language Model semantic encoding with deep neural learning to enable automated defect prediction and proactive maintenance of large-scale digital infrastructure.
Mst Masuma Akter Semi, Md Masud Karim Rabbi, K. Ahmed et al.· Multimedia· 0 citations
BERM is introduced, a lightweight framework that performs in-situ detection by modeling a host LLM’s internal representations extracted during prefill, adding negligible overhead and reducing incremental inference overhead to near-zero.
Maihao Guo, Chaoyang Zhao, Jin-Qiao Wang· Proceedings of the Thirty-Fi...· 0 citations
With the rapid development of computer and network technology, network and software logs generated by a multitude of devices contain a wealth of knowledge and serve as a critical resource for intelligent fault diagnosis and efficient system operations. In recent years, various deep learning methods and the pre-training...
Lanlan Rui, Yuan-Rui Yang, Peng Yu et al.· IEEE Transactions on Network...· 0 citations
This research proposes the first end-to-end multi-task architecture that jointly detects Python syntax errors and localizes their exact line position by adaptively fusing CodeBERT’s semantic embeddings with handcrafted syntactic features via a lightweight gating mechanism, without relying on Abstract Syntax Trees.
Rozali Ilham, Bahtiar Imran, Hasan Basri et al.· 0 citations
Large language models (LLMs) embedded in enterprise workflows cannot structurally distinguish legitimate instructions from adversarial ones in the same token stream, making prompt injection OWASP's top LLM risk for two consecutive editions a persistent threat across direct and indirect vectors. This paper presents Prom...
Fatimah Alhamzawi· Al-Noor Journal of Engineeri...· 0 citations
LLM-Detector is proposed, a framework that utilizes the in-context learning capacity of LLMs for structured, prompt-conditioned scoring synthesis, enabling LLMs to derive anomaly detection logic from structured normal-state knowledge.
Tu Nguyen, Dang Nguyen, T. D. Le et al.· 0 citations
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