Advanced Network Intrusion Detection Using HMM for Feature Selection in Machine Learning Models

Abstract

This invention provides a network intrusion detection system that employs a Hidden Markov Model (HMM) for feature selection, enhancing the accuracy of machine learning classifiers in detecting network intrusions. The system incorporates an ensemble classifier, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Decision Tree, and Voting Classifier, to minimize false positives and increase detection precision. Additionally, it includes a real-time user interface for monitoring, allowing network administrators to respond promptly to potential intrusions. The system is designed for scalability and adaptability across diverse network environments, offering a robust solution to contemporary cybersecurity challenges.

Specification

Description:FIELD OF THE INVENTION:
This invention relates to the field of network security, specifically to network intrusion detection systems (IDS). More particularly, it involves a machine learning-based approach to detect and mitigate network threats using a Hidden Markov Model (HMM) for feature selection, enhancing the accuracy and efficiency of intrusion detection.
3. BACKGROUND OF THE INVENTION:
With the rise of cyber threats, network intrusion detection systems (IDS) have become critical for identifying and mitigating unauthorized access or attacks on network infrastructure. Traditional IDS solutions often struggle with high false positive rates and inadequate feature selection methods, which limit their accuracy and reliability in detecting new or complex threats. Existing machine learning-based IDS approaches provide improvements; however, many still face limitations in selecting relevant features, leading to noise and suboptimal detection performance. This invention addresses these challenges by incorporating Hidden Markov Models (HMM) for enhanced feature selection and an ensemble of machine learning classifiers for accurate and efficient network intrusion detection________________________________________
4. OBJECTIVES OF THE INVENTION:
The primary objectives of the present invention are:
1. To improve the accuracy of network intrusion detection by using Hidden Markov Models (HMM) for effective feature selection, reducing irrelevant data noise.
2. To employ an ensemble of machine learning models, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Decision Tree, and Voting Classifier, for robust classification of network traffic anomalies.
3. To provide a real-time user interface for network administrators to monitor and respond to intrusion alerts promptly.
4. To create a scalable and adaptable intrusion detection system capable of recalibrating its models to respond to evolving threats in diverse network environments
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5. SUMMARY OF THE INVENTION:
The present invention introduces an advanced network intrusion detection system that leverages Hidden Markov Models (HMM) for feature selection to improve the accuracy of machine learning classifiers in detecting network threats. The system incorporates a hybrid machine learning model comprising classifiers such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Decision Tree, and Voting Classifier, which together improve detection accuracy and reduce false positives. Additionally, the invention includes a real-time user interface, allowing network administrators to monitor network traffic and respond to potential intrusions swiftly. This system is designed to be both scalable and adaptable to various network environments, offering a robust solution for modern cybersecurity challenges
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6. DETAILED DESCRIPTION OF THE INVENTION:
 System Architecture: Describe the high-level architecture of the intrusion detection system, including modules for feature selection, classification, and user interface components.
 Feature Selection using HMM: Explain how the HMM module is applied to select relevant features from network traffic data, filtering out noise and improving classification accuracy. Detail the steps, algorithms, or parameters used in the HMM process.
 Ensemble Machine Learning Model: Describe each machine learning model (CNN, LSTM, Decision Tree, Voting Classifier) in the ensemble, explaining how each classifier contributes to detecting intrusions. Discuss the ensemble strategy that combines the results of these classifiers for a robust threat detection outcome.
 Real-Time Monitoring Interface: Detail the functionality of the user interface, which provides real-time monitoring capabilities for network administrators. Describe any dashboard components, alert notifications, and data visualization features that aid administrators in managing network security.
 Scalability and Adaptability: Explain the mechanisms by which the system adapts to various network environments, such as dynamic data updates or model recalibration processes that allow the system to respond effectively to new and evolving threats.
, Claims:1. I/ We Claim a feature selection module utilizing a Hidden Markov Model (HMM) to identify and select relevant features from network traffic data. This feature selection module filters irrelevant data using Hidden Markov Model (HMM), thereby enhancing the accuracy and efficiency of the machine learning classifiers in detecting network intrusions.

2. I/We Claim an ensemble machine learning module including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Decision Tree Classifier, K-Nearest Neighbors (KNN), and Voting Classifier to classify network intrusions based on selected features. The ensemble classifier comprises a Convolutional Neural Network (CNN) configured to perform feature extraction by identifying spatial patterns in network traffic, coupled with a Long Short-Term Memory (LSTM) network that captures temporal dependencies within the extracted features for enhanced intrusion detection accuracy. The ensemble classifier incorporates a Voting Classifier that applies a weighted voting mechanism, assigning greater influence to classifiers with historically higher accuracy on similar traffic patterns, to generate a final intrusion prediction with improved accuracy. Further comprising an adaptive alert module that dynamically adjusts intrusion detection thresholds based on real-time network traffic analysis and historical alert data to reduce false positives and alert fatigue among network administrators.

3. I/We Claim a prediction module configured to analyse selected features and output intrusion classifications and a user interface module built on a web-based platform enabling real-time monitoring and user interaction with the system. This user interface module provides real-time graphical representation of detected intrusions, displaying metrics such as source and destination IP addresses, attack type, severity, and timestamps, allowing users to monitor intrusion patterns and potential network vulnerabilities efficiently.

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