TL;DR: An overview of the Bro system's design, which emphasizes high-speed (FDDI-rate) monitoring, real-time notification, clear separation between mechanism and policy, and extensibility, is given.
TL;DR: In this article, a log manager collects such log data using various protocols (e.g., Syslog, SNMP, SMTP, etc.) to determine events and transfer the events to an event manager.
Abstract: The present invention generally relates to log message processing such that events can be detected and alarms can be generated. For example, log messages are generated by a variety of network platforms (e.g., Windows servers, Linux servers, UNIX servers, databases, workstations, etc.). Often, relatively large numbers of logs are generated from these platforms in different formats. A log manager described herein collects such log data using various protocols (e.g., Syslog, SNMP, SMTP, etc.) to determine events. That is, the log manager may communicate with the network platforms using appropriate protocols to collect log messages therefrom. The log manager may then determine events (e.g., unauthorized access, logins, etc.) from the log data and transfer the events to an event manager. The event manager may analyze the events and determine whether alarms should be generated therefrom.
TL;DR: A new methodology of dynamic syslog mining is proposed in order to detect failure symptoms with higher confidence and to discover sequential alarm patterns among computer devices.
Abstract: Syslog monitoring technologies have recently received vast attentions in the areas of network management and network monitoring They are used to address a wide range of important issues including network failure symptom detection and event correlation discovery Syslogs are intrinsically dynamic in the sense that they form a time series and that their behavior may change over time This paper proposes a new methodology of dynamic syslog mining in order to detect failure symptoms with higher confidence and to discover sequential alarm patterns among computer devices The key ideas of dynamic syslog mining are 1) to represent syslog behavior using a mixture of Hidden Markov Models, 2) to adaptively learn the model using an on-line discounting learning algorithm in combination with dynamic selection of the optimal number of mixture components, and 3) to give anomaly scores using universal test statistics with a dynamically optimized threshold Using real syslog data we demonstrate the validity of our methodology in the scenarios of failure symptom detection, emerging pattern identification, and correlation discovery
TL;DR: This document describes the syslog protocol which is used to convey event notification messages and describes the basic message format and structured elements used to provide meta- information about the message.
Abstract: This document describes the syslog protocol which is used to convey
event notification messages. It describes a layered architecture for
an easily extensible syslog protocol. It also describes the basic
message format and structured elements used to provide meta-
information about the message.
TL;DR: This document describes the observed behavior of the syslog protocol, a protocol used for the transmission of event notification messages across networks for many years that has been ported to many other operating systems as well as being embedded into many other networked devices.
Abstract: This document describes the observed behavior of the syslog protocol. This protocol has been used for the transmission of event notification messages across networks for many years. While this protocol was originally developed on the University of California Berkeley Software Distribution (BSD) TCP/IP system implementations, its value to operations and management has led it to be ported to many other operating systems as well as being embedded into many other networked devices.