A microservices-based endpoint monitoring platform with predictive NLP models for real-time security and hate-speech risk alerting

• URL: http://arxiv.org/abs/2605.11997v2
• Date: Wed, 13 May 2026 13:12:57 GMT
• Title: A microservices-based endpoint monitoring platform with predictive NLP models for real-time security and hate-speech risk alerting
• Abstract summary: This work proposes a unified, endpoint-based platform that collects telemetry and applies predictive natural language processing models to support real-time security and compliance.<n>For text classification, transformer-based models such as BERT are evaluated for hate-speech risk detection, achieving an average accuracy of 87%.<n> Experimental results indicate that the proposed platform can promptly surface indicators of data exfiltration and policy violations while centralizing alert management.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organizations increasingly depend on endpoint devices and corporate communication channels, yet they still face critical risks such as sensitive data leakage, suspicious user behavior, and the circulation of hateful or harmful language in workplace contexts. Current solutions frequently address these issues in isolation (e.g., productivity tracking, data loss prevention, or hate-speech detection), limiting correlation across signals and delaying incident response. This work proposes a unified, microservices-based platform that collects endpoint telemetry and applies predictive natural language processing models to support real-time security and compliance alerting. The architecture is modular and scalable, relying on RabbitMQ for event ingestion and routing and Redis for low-latency data access and alert delivery. For text classification, transformer-based models such as BERT are evaluated for hate-speech risk detection, achieving an average accuracy of 87\%. Experimental results indicate that the proposed platform can promptly surface indicators of data exfiltration and policy violations while centralizing alert management, providing an integrated framework that combines monitoring, security analytics, and predictive capabilities.

Related papers

• AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
  "AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
  arXiv  Detail & Related papers  (2026-08-26T11:48:38Z)
• LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications [76.41731220830714]
  Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tools, and iterative prediction.<n>We examine applications in finance, weather, health, energy, and operations, and we summarize the benchmarks and datasets used for evaluation.
  arXiv  Detail & Related papers  (2026-08-24T10:02:39Z)
• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• LROO Rug Pull Detector: A Leakage-Resistant Framework Based on On-Chain and OSINT Signals [1.0382948833881696]
  rug pull is one of the most prevalent attacks in smart contract-based ecosystems.<n>This paper proposes a leakage-aware framework for early rug-pull detection.<n>It integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals.
  arXiv  Detail & Related papers  (2026-03-11T21:35:53Z)
• Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
  Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
  arXiv  Detail & Related papers  (2026-02-13T14:57:15Z)
• Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts [51.37000123503367]
  We propose prediction-powered risk monitoring (PPRM), a semi-supervised risk-monitoring approach based on prediction-powered inference (PPI)<n>PPRM constructs anytime-valid lower bounds on the running risk by combining synthetic labels with a small set of true labels.<n>We demonstrate the effectiveness of PPRM through extensive experiments on image classification, large language model (LLM) and telecommunications monitoring tasks.
  arXiv  Detail & Related papers  (2026-02-02T15:32:14Z)
• Robust Federated Learning for Malicious Clients using Loss Trend Deviation Detection [0.0]
  Federated Learning (FL) facilitates collaborative model training among distributed clients while ensuring that raw data remains on local devices.<n>Such clients can interfere with the training process by sending misleading updates, which can negatively affect the performance and reliability of the global model.<n>We propose the Federated Learning with Loss Trend Detection (FL-LTD), a lightweight and privacy-preserving defense framework that detects and mitigates malicious behavior by monitoring temporal loss dynamics rather than model gradients.
  arXiv  Detail & Related papers  (2026-01-28T18:09:53Z)
• Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection [0.3266916057202441]
  This paper presents "Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection"<n>A machine learning-based detection engine, trained on labelled datasets of benign and malicious activity, enables accurate threat identification with minimal false positives.<n>The system includes a centralized interface for alerting and forensic analysis.
  arXiv  Detail & Related papers  (2025-11-11T15:28:54Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operations [5.95097350945477]
  Connected and automated factories face growing cybersecurity risks that can potentially cause interruptions and damages to physical operations.<n>Data-integrity attacks often involve sophisticated exploitation of vulnerabilities that enable an attacker to access and manipulate the operational data.<n>This paper develops an online detection framework, ViSTR-GP, that cross-checks encoder-reported measurements against a vision-based estimate from an overhead camera outside the controller's authority.
  arXiv  Detail & Related papers  (2025-09-13T19:10:35Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment [6.166295570030645]
  We propose an introspection approach to performance monitoring during deployment. We do so by predicting when the per-frame mean average precision drops below a critical threshold. We quantitatively evaluate and demonstrate our method's ability to reduce risk by trading off making an incorrect decision.
  arXiv  Detail & Related papers  (2020-09-18T06:37:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.