SentinelNet-AI-Powered Network Intrusion Detection System (NIDS)

Overview

SentinelNet-AI is an AI-powered Network Intrusion Detection System developed to detect malicious network traffic using machine learning techniques. This project uses the CICIDS 2017 Wednesday dataset.

Milestone 1: Project Initialization, Dataset Acquisition, and Preprocessing

1. Project Goals and Expected Outcomes

   -Develop an AI-powered NIDS to detect malicious network traffic.

   -Understand network traffic data and attack types.

   -Apply ML models to detect intrusions.

   -Perform feature engineering and select important features.

   -Generate alerts for suspicious activity and prepare a report.

2. Dataset Acquisition and Exploration

   -Dataset: CICIDS2017 (Wednesday subset) from CIC Dataset.

   -Explored dataset structure, feature types, and unique attack labels.

   -Performed basic statistics and data validation to understand data distribution.

3. Data Cleaning

   -Identified and visualized missing values using heatmaps.

   -Dropped rows with null values and duplicate rows.

   -Removed irrelevant features and cleaned column names.

4. Data Preprocessing

   -Encoded categorical target column Label using LabelEncoder.

   -Standardized numerical features (mean ~0, std ~1) for uniform scaling.

   -Visualized distributions of numerical features before and after scaling.

5. Dataset Splitting

   -Split dataset into training (80%) and testing (20%) sets for ML model development.