Anomaly-Based Intrusion Detection System #684
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Pull Request for PyVerse 💡
Requesting to submit a pull request to the PyVerse repository.
Issue Title
Anomaly-Based Intrusion Detection System
Info about the Related Issue
The primary objective of this project is to develop an anomaly-based Intrusion Detection System (IDS) that identifies deviations from normal network behavior, potentially signaling an intrusion. This system uses machine learning techniques to classify network traffic as normal or anomalous, thus improving network security by detecting unusual or malicious activities.
Name
Sharayu Anuse
GitHub ID
114616759
Email ID
[email protected]
Identify Yourself
Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).
GSSOC-Ext, Hacktoberfest
Closes
Enter the issue number that will be closed through this PR.
*Closes: #654 *
Describe the Add-ons or Changes You've Made
In this project, I have implemented an anomaly detection system for network intrusion detection. The system uses machine learning algorithms to classify network traffic and detect abnormal behavior indicative of a potential attack.
The project is divided into the following stages:
Data Preprocessing: Cleaning and preparing the dataset for training and testing.
Feature Engineering: Selecting and transforming relevant features for model training.
Model Training: Using classification algorithms such as Random Forest, Support Vector Machines (SVM), and others to train the IDS.
Evaluation: Evaluating the model's performance using metrics like accuracy, precision, recall, and F1-score.
Anomaly Detection: Detecting anomalies in the network traffic and classifying them as potential intrusions.
I have described my changes.
Type of Change
Select the type of change:
How Has This Been Tested?
The models were evaluated using key metrics, such as accuracy, precision, recall, and F1-score, to ensure they could accurately classify network traffic and detect anomalies.
I have described my testing process.
Checklist
Please confirm the following: