SCALABLE ANALYSIS OF HETEROGENEOUS LOG STREAMS: A DISTRIBUTED ENSEMBLE APPROACH FOR ROBUST SYSTEM OBSERVABILITY

Abstract

Manual log checking is no longer feasible due to the development of high-velocity data streams in contemporary distributed systems. Using cutting-edge ensemble learning techniques, this study suggests a scalable, distributed architecture for the real-time analysis of heterogeneous log streams, particularly firewall, web server, and system logs. Our method incorporates an ensemble of varied base learners to produce a more robust "collective decision," in contrast to conventional single-classifier models that frequently suffer from excessive bias or overfitting in the face of complex, dynamic threats. A hybrid ensemble architecture is used by the framework. To lower variation in high-speed packet classification for firewall logs, we employ a Bagging technique using Random Forests; for web logs, we use a Boosting-based mechanism. (such as XGBoost) is used to iteratively learn from injection patterns that have been incorrectly classified. We implement a Stacking layer that combines predictions from Transformer-based log parsers with conventional statistical models to handle the various server log formats, greatly improving anomaly detection accuracy. These methods are frequently combined into a single design in contemporary log pipelines. For example, Apache Kafka is widely utilized for data input; logs are concurrently archived into Hadoop for long-term historical reporting and fed into Flink for instantaneous anomaly detection. These designs can process up to 500,000 log events per second with typical latencies as low as 300 milliseconds, according to recent benchmarks. Log analysis is essential for understanding system activity, identifying issues, and improving performance. The increasing volume and complexity of log data makes traditional analysis methods inadequate. The benefits and drawbacks of employing big data technologies like Hadoop, Spark, and Flink for log analysis are examined in this paper. Batch processing and large-scale data storage are ideal uses for Hadoop, a distributed processing platform. However, it could be show for real-time analysis. Because of its high performance and real-time capabilities, Spark is an in-memory processing engine that is perfect for machine learning and iterative workloads. Flink is real time analytics stream processing engine with high throughput and low latency. The main motivation for the use of ensemble techniques in log analysis is to improve predictive performance and robustness beyond what a single model might do. By combining the results of multiple models trained on different data subsets or using diverse learning techniques, the system becomes more flexible and more adept at generalizing new and unseen data. Ensemble learning is a machine learning technique that combines several "weak" learning models to create a single, ultra-reliable, and accurate "strong" model. Ensemble approaches offer a powerful solution to the inherent problems of large-scale log