Log Anomaly Detection

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
3. Analysis and Report
4. Contact
5. Acknowledgments

About The Project

In the realm of computing, logging involves the process of creating a record detailing events that transpire within a computer system. These events encompass issues, errors, or even informative updates about ongoing operations. These occurrences might manifest within the operating system or other software components. For every such event, a message or entry is documented.

The project, ‘Log Anomaly Detection’, centres on identifying irregularities within logs produced by software by utilising Machine Learning techniques.

The principal objective of this project is to leverage the logs generated by the machine to identify anomalous log entries. This capability could be integrated as a software feature, a preventive measure against potential catastrophic machine failures. An "anomaly" signifies anything that deviates from the established norm, standing as an exception to the rule. Within the realm of software engineering, an anomaly can be characterized as an infrequent or unexpected event that diverges from regular patterns, thus becoming a subject of suspicion.

Examples could be:

• Unexpected failure of a service
• Sudden increase/decrease of user activity in a customer-facing system. Reasons for these anomalies could be different, like the Entry of unperceived inputs to the system.
• The system running out of memory.

The data containing the logs was provided. The dataset was in JSON format where each key is a single software log, and the corresponding value is the label for that log.

The labels for logs are "abnormal" and "normal"

Project Objectives

1. To train a machine learning model that can predict whether a given log is an anomaly or normal

Dataset

about the dataset pic of samples of data

Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

In

# Clone this repository
$ git clone 

# Go into the repository
$ cd 

# Install dependencies
$ make setup

Runing the program

train.py different models evalute.py

Analysis and Report

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Contact

If you have questions or need assistance, feel free to reach out to:

Name: Ipadeola Ezekiel Ladipo
Email: [email protected]
GitHub: @rileydrizzy
Linkdeln: Ipadeola Ladipo

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