AI-Based Intrusion Detection System for Networks

Abstract

The invention relates to an AI-based intrusion detection system (IDS) for networks, designed to enhance cybersecurity by utilizing machine learning (ML) and deep learning (DL) techniques to detect and mitigate both known and unknown cyber threats in real-time. The system continuously monitors network traffic, aggregates data from multiple sources, preprocesses and analyzes it to identify anomalous behavior, and classifies potential intrusions. By integrating adaptive learning capabilities, the IDS improves over time, reducing false positives and enhancing detection accuracy. The system can be deployed in diverse network environments, including on-premises, cloud, and hybrid infrastructures, providing comprehensive protection against evolving security threats.

Specification

Description:The present invention pertains to network security, specifically to an advanced intrusion detection system (IDS) that leverages artificial intelligence (AI) and machine learning (ML) algorithms to detect and mitigate cyber threats in real-time. It aims to provide a comprehensive and adaptive solution capable of identifying known and unknown intrusions by analyzing network traffic patterns, thereby enhancing the security of both on-premises and cloud-based network environments.

BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

The increasing reliance on digital infrastructure and cloud computing has made , Claims:1. An AI-based intrusion detection system for networks, comprising:
• A data collection module configured to aggregate data from various network sources;
• A preprocessing module designed to clean, normalize, and format the collected data;
• A feature extraction module for extracting relevant features from network traffic data;
• An AI model module employing machine learning and deep learning algorithms for anomaly detection;
• A real-time threat detection module for monitoring and analyzing network traffic using the trained AI models;
• An alerting and response module to generate alerts and trigger automated responses upon detecting anomalies.

2. The system of Claim 1, wherein the AI model module includes a supervised learning model trained on labeled datasets to classify network traffic as normal or malicious.
3. The system of Claim 1, wherein the AI model module utilizes an unsupervised learning approach, employing clustering algorithms or autoencoders to detect previously unknown anomalies.
4. The system

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