ROBUST CYBERSECURITY FRAMEWORK USING AI AND ML

Abstract

The invention provides a robust cybersecurity framework that utilizes artificial intelligence (AI) and machine learning (ML) for real-time threat detection, prediction, and automated response across digital networks and systems. By analyzing network traffic, user behavior, and data patterns, the system autonomously identifies and mitigates cyber threats such as malware, ransomware, and zero-day vulnerabilities. The framework incorporates adaptive learning, allowing it to continuously improve its detection and response capabilities. Through predictive analytics, the system anticipates emerging threats, while its automated response mechanism minimizes the impact of cyber incidents by executing predefined actions. This invention enhances cybersecurity resilience, reduces human intervention, and offers proactive protection against evolving cyber risks.

Specification

Description:The present invention pertains to the field of cybersecurity, specifically to a robust and adaptive cybersecurity framework that leverages artificial intelligence (AI) and machine learning (ML) technologies for automated, real-time threat detection, prediction, and mitigation. This invention focuses on enhancing the security of digital networks and systems by identifying, analyzing, and responding to cybersecurity threats autonomously, thus providing comprehensive protection against various forms of cyber attacks, including zero-day threats, malware, phishing, and other advanced cyber intrusions.
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 disclos , Claims:1. A cybersecurity framework using artificial intelligence and machine learning, comprising:
• a threat detection module that applies deep learning algorithms to network data for identifying anomalies indicating potential cyber threats;
• a threat prediction engine that employs machine learning algorithms to predict future security risks based on detected behavioral patterns; and
• an automated response system configured to initiate predefined responses to detected threats autonomously.

2. A method for adaptive cybersecurity in a digital network, comprising:
• receiving real-time network data and analyzing it using an AI-driven threat detection module;
• predicting potential cyber threats based on patterns identified by the threat detection module; and
• executing automated mitigation actions, wherein a reinforcement learning model continuously updates the threat detection and response algorithms.

3. The framework of claim 1, wherein the threat detection module utilizes a combination of supervised and unsup

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