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At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.

ABSTRACT

Intrusion detection systems (IDSs) are currently drawing a great amount of interest as a
key part of system defense. IDSs collect network traffic information from some point
on the network or computer system and then use this information to secure the network.
To distinguish the activities of the network traffic that the intrusion and normal is very
difficult and to need much time consuming. An analyst must review all the data that
large and wide to find the sequence of intrusion on the network connection. Therefore,
it needs a way that can detect network intrusion to reflect the current network traffics.
In this study, a novel method to find intrusion characteristic for IDS using genetic
algorithm machine learning of data mining technique was proposed. Method used to
generate of rules is classification by Genetic algorithm of decision tree. These rules can
determine of intrusion characteristics then to implement in the genetic algorithm as
prevention.so that besides detecting the existence of intrusion also can execute by doing
deny of intrusion as prevention.

Abstract:

This project focuses on the development of a Network Intrusion Detection System (NIDS) using Python and web technologies. With the increasing sophistication of cyber threats, an effective intrusion detection system is crucial for safeguarding network security. The proposed system integrates machine learning algorithms to analyze network traffic patterns, providing real-time detection and alerting for potential intrusions.

Existing System:

Traditional network intrusion detection systems often rely on rule-based methods and signature-based approaches. While effective to some extent, these systems may struggle to adapt to emerging threats and can generate false positives or negatives, leading to potential security vulnerabilities.

Proposed System:

The proposed system introduces an advanced Network Intrusion Detection System that leverages machine learning algorithms, particularly anomaly detection, for more adaptive and accurate intrusion detection. The system continuously analyzes network traffic, identifies patterns, and raises alerts in real-time when suspicious activities are detected. The integration of a web interface enhances user accessibility and facilitates efficient monitoring of network security.

Modules Explanation:

1. Packet Capture Module:

• Captures and preprocesses network packets for analysis.

1. Feature Extraction Module:

• Extracts relevant features from network packets for input into machine learning models.

1. Machine Learning Model:

• Implements anomaly detection algorithms for analyzing network traffic patterns and identifying potential intrusions.

1. Alerting Module:

• Raises alerts and notifications in real-time when suspicious activities are detected.

1. Web Interface:

• Provides a user-friendly dashboard for monitoring network activities, viewing alerts, and managing system configurations.

System Requirements:

• Hardware:
• Standard server-class hardware with network interface cards.
• Sufficient storage for storing network logs.
• Software:
• Python for implementing machine learning algorithms.
• Web development framework (e.g., Flask or Django).
• Wireshark or similar tools for packet capture.

Algorithms:

• Machine Learning (Anomaly Detection):
• Utilizes algorithms such as Isolation Forests, One-Class SVM, or Autoencoders for anomaly detection.

Hardware and Software Requirements:

• Hardware:
• Standard server-class hardware.
• Network interface cards for packet capture.
• Storage for storing network logs.
• Software:
• Python 3.x
• Web development framework (Flask or Django).
• Machine learning libraries (Scikit-learn, TensorFlow, or PyTorch).

Architecture:

• Packet Capture and Preprocessing:
• Captures and preprocesses network packets for feature extraction.
• Feature Extraction:
• Extracts relevant features from network packets for input into machine learning models.
• Machine Learning Model Integration:
• Implements and trains anomaly detection models on network traffic data.
• Alerting and Notification:
• Raises alerts and notifications in real-time when anomalies indicative of intrusions are detected.
• Web Interface:
• User-friendly interface for monitoring network activities, viewing alerts, and managing system configurations.

Technologies Used:

• Python, machine learning libraries for anomaly detection.
• Web development frameworks (Flask/Django) for creating the web interface.
• HTML, CSS, JavaScript for designing an interactive and user-friendly web interface.

Web User Interface:

The web interface provides a centralized platform for monitoring network activities, visualizing traffic patterns, and receiving real-time alerts. It offers functionalities such as historical data analysis, configurable alert thresholds, and system status updates. The user-friendly design ensures that network administrators can efficiently manage and respond to potential intrusions, enhancing overall network security.

UML DIAGRAMS

Collaboration Diagram

Architecture diagram

class diagram

sequence diagram

use case diagram

activity diagram

component diagram

Deployment Diagram

Flow chart Diagram