BOTNET Detection Using Machine Learning
Objective
The objective of this project is to develop an effective and efficient system for detecting botnet attacks on Android devices using advanced machine learning algorithms. By leveraging the "BotDroid Android Botnet Detection" dataset from Kaggle, the project aims to accurately identify and classify botnet attacks based on network traffic and device behavior data. The system will employ various machine learning techniques, including Random Forest, XGBoost, SVM, and Decision Tree, to achieve high precision and recall in detecting malicious activities. The ultimate goal is to provide a reliable, user-friendly tool that enhances cybersecurity measures for individuals and organizations, protecting Android devices from the significant threats posed by botnet attacks.
Abstract
The project aims to develop a robust and efficient system for detecting botnet attacks on Android devices using various machine learning algorithms. Utilizing the dataset from Kaggle, "BotDroid Android Botnet Detection," we seek to identify potential botnet attacks based on network traffic, device behavior data, app permissions, and system logs. Botnet attacks pose a significant threat to cybersecurity, exploiting large numbers of infected devices to perform malicious activities such as DDoS attacks, data theft, unauthorized access, spreading malware, and facilitating fraud. By accurately identifying such attacks, the system will help prevent extensive damage to networks, safeguard user privacy, and enhance device security.
Keywords: Botnet Detection, Android Security, Machine Learning, Network Traffic Analysis, Cyber security
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware Requirements
· Processor - I3/Intel Processor
· Hard Disk - 160GB
· Key Board - Standard Windows Keyboard
· Mouse - Two or Three Button Mouse
· Monitor - SVGA
· RAM - 8GB
Software Requirements
· Operating System : Windows 7/8/10
· Server side Script : HTML, CSS, Bootstrap & JS
· Programming Language : Python
· Libraries : Flask, Pandas, MySQL. Connector, Scikit-learn
· IDE/Workbench : VS Code
· Technology : Python 3.8+
· Server Deployment : Xampp Server
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