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Wi-Fi Network Intrusion Detection: Enhanced with Feature Extraction and Machine Learning Algorithms

Category: Python Projects

Price: ₹ 3200 ₹ 10000 68% OFF

Wireless Fidelity (Wi-Fi) is one of the most widely used technologies for wireless internet access, enabling users to connect devices without the need for physical cables. With the growing number of devices connected to Wi-Fi networks, especially in environments like schools, offices, and smart homes, the need for secure and efficient network management has become critical. Wi-Fi plays a pivotal role in enabling the Internet of Things (IoT), which connects various smart devices to the internet, offering greater convenience and efficiency. However, the same connectivity that makes Wi-Fi so convenient also exposes it to a variety of vulnerabilities, making it susceptible to network attacks and unauthorized access.
The risks associated with Wi-Fi networks can range from reduced service quality due to unauthorized use, to serious security breaches such as personal data theft. These attacks can be executed in many ways, including through man-in-the-middle attacks, denial-of-service attacks, or by exploiting weak encryption methods. As such, safeguarding Wi-Fi networks against these threats is essential. This is where network intrusion detection systems (NIDS) come into play. NIDS are designed to monitor network traffic for signs of suspicious activity or potential breaches, alerting administrators to take necessary actions before a network compromise occurs.

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In addition to using machine learning algorithms, we utilized time-series data, which provides a temporal sequence of network traffic data, to better understand and detect attacks over time. Time-series data analysis is crucial in detecting patterns that may not be immediately obvious but become clear when viewed over a period of time. We enhanced this analysis by using feature extraction methods such as Convolutional Neural Networks (CNN) and statistical measures, which help capture critical features from the raw data and improve the accuracy of the machine learning models.

Software Requirements:
1. Front-end:
HTML
CSS
Bootsrap
JavaScript
2. Back-end:
Python
Flask
MYSQL
Datasets
3.Machine Learning:
Python idle3.8
Library
Numpy
Matplotlib
Scikit-learn
Hardware Requirement
Pc or laptop
Vs code

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