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 systems. 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 s
Design and develop a Universal Log Pre-processing Framework (ULPF) that can ingesting, parsing, normalizing, and standardizing logs and events generated by any hardware or software system.
Modern enterprises generate massive volumes of logs from a wide range of sources, including network devices, servers, operating systems, applications, databases, cloud services, containers,... These logs are produced in diverse formats such as Syslog, JSON, XML, CSV, CEF, LEEF, proprietary vendor formats, and application-specific schemas
Design and develop a Universal Log Pre-processing Framework (ULPF) that can 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. •