Universal Log Pre-processing Framework

• Background Modern enterprises generate massive volumes of logs from a wide range of sources, including network devices, servers, operating systems, applications, databases, cloud services, containers, endpoint security tools, identity and access management systems, IoT devices, and other hardware and software platforms. These logs are produced in diverse formats such as Syslog, JSON, XML, CSV, CEF, LEEF, proprietary vendor formats, and application-specific schemas. The diversity of log structures creates significant challenges in centralized monitoring, security operations, compliance reporting, incident investigation, and threat analytics. Security teams often spend substantial effort developing source-specific parsers and normalization rules before the data can be effectively utilized by SIEM, data lake, or machine learning platforms. As organizations adopt hybrid, multi-cloud, and AI-driven environments, the need for a universal and extensible log standard that can accommodate both current and future data sources have become increasingly critical. • Detailed Description Design and develop a Universal Log Pre-processing Framework (ULPF) capable of ingesting, parsing, normalizing, and standardizing logs and events generated by any hardware or software system. The framework should support diverse event sources while preserving the original event data for forensic and compliance purposes. It should transform heterogeneous logs into a unified schema that enables consistent analytics, correlation, visualization, threat hunting, anomaly detection, and machine learning applications. The framework must be scalable, extensible, vendor-agnostic, and suitable for deployment in Big Data environments handling billions of events per day. • Expected Solutions This solution should cover universal event schema and processing framework that enables: a) Preserve complete raw event data without information loss. b) Extract and parse source-specific attributes. c) Normalize fields into a common event taxonomy. d) Maintain traceability between normalized and original events. e) Plug-and-play on boarding of new log sources. f) Unified visibility across enterprise environments. g) Efficient SIEM and Data Lake integration. h) AI/ML-ready security and operational analytics. i) Reduced parser development effort. j) The solution shall be deployable in an air-gapped network. k) Solution may be packaged in a container for making it platform independent. • Current Scope Build a framework that converts any perimeter network device-generated log or event-regardless of source, format, vendor, or technology into a standardized, lossless, analytics-ready representation for next-generation SIEM and cybersecurity platforms. • Expected Solution/Deliverables for Evaluation • Source Code Link (GitHub/Drive Link) • Readme with Setup Instructions • Architecture Document (Max 2 Pages) • Demo Video (Max 2 Minutes) • Technical Presentation (Max 5 Slides)