ML-Based Enterprise Log Monitoring & Threat Detection Platform
Abstract
With the rapid growth of digital infrastructure, enterprises face increasing risks from sophisticated cyber threats hidden within massive volumes of system and application logs. Traditional rule-based monitoring systems struggle to detect evolving attack patterns and anomalous behaviors in real time. To address this challenge, this project proposes SecureSight – an ML-Based Enterprise Log Monitoring and Threat Detection Platform, designed to provide intelligent, scalable, and automated security analytics.
SecureSight integrates centralized log collection, streaming data processing, and machine learning–driven threat analysis to identify malicious activities across enterprise environments. The system ingests logs from multiple sources, performs ETL operations to normalize and extract meaningful features, and applies anomaly detection and supervised classification models to detect intrusion attempts such as brute-force attacks, abnormal user behavior, and traffic anomalies. Detected threats are assigned risk scores and severity levels, enabling prioritized incident response.
A web-based security dashboard visualizes real-time alerts, system metrics, and attack timelines, while automated response mechanisms support rapid mitigation actions such as IP blocking and account isolation. The platform also supports behavioral profiling and explainable AI techniques to improve transparency in threat detection decisions.
SecureSight demonstrates how machine learning and big data technologies can be combined to build a proactive security monitoring solution, reducing detection time and improving organizational resilience against cyber attacks. The proposed system is suitable for small to medium enterprises and can be extended to large-scale environments, offering a foundation for next-generation intelligent SIEM platforms.