AI-BASED PREDICTIVE SECURITY SYSTEM FOR EARLY THREAT DETECTION AND ANOMALY IDENTIFICATION IN CYBER NETWORKS (AI-PSS-ETD-AICN)

Abstract

The AI-Based Predictive Security System for Early Threat Detection and Anomaly Identification in Cyber Networks (AI-PSS-ETD-AICN) presents a novel approach to enhancing cybersecurity through advanced artificial intelligence (AI) and machine learning (ML) techniques. This system leverages predictive algorithms to proactively identify potential cyber threats and network anomalies before they can impact critical infrastructure. The AI-PSS-ETD-AICN integrates a multi-layered detection mechanism, incorporating real-time data analysis, pattern recognition, and anomaly detection to continuously monitor network traffic. By utilizing both supervised and unsupervised learning models, the system can adapt to evolving threat landscapes, autonomously detecting zero-day vulnerabilities and sophisticated attacks such as advanced persistent threats (APTs). The system further employs dynamic threat scoring and prioritization to provide actionable intelligence, enabling rapid and effective incident response. It combines historical threat data with real-time telemetry from networked environments to create a predictive model capable of forecasting potential attack vectors. The architecture supports seamless integration into existing security infrastructures and is designed to optimize resource usage while minimizing false positives. The AI-PSS-ETD-AICN significantly enhances an organization’s ability to preemptively defend against cyber-attacks, ensuring robust protection in dynamic and complex cyber environments.

Specification

Description:FIELD OF THE INVENTION

The present invention relates to the field of cybersecurity, specifically to the development of artificial intelligence (AI) and machine learning (ML)-based systems for predictive threat detection and anomaly identification in cyber networks. The invention addresses the need for advanced, autonomous cyber defense mechanisms that can proactively detect, analyze, and respond to evolving cybersecurity threats. It encompasses methods and systems that utilize AI and ML techniques to enhance threat intelligence, incident response, and overall network security, providing organizations with enhanced protection against both known and unknown cyber threats. This invention is particularly applicable in critical infrastructure, financial services, healthcare, and other industries where data integrity and security are paramount.

BACKGROUND OF THE INVENTION

As digital networks expand and cyber threats become more complex, organizations face growing challenges in securing their systems against a wide range of attacks, including malware, phishing, advanced persistent threats (APTs), and zero-day vulnerabilities. Traditional cybersecurity solutions, which rely primarily on rule-based detection, often struggle to keep up with the rapidly evolving tactics employed by cybercriminals. These conventional methods tend to be reactive, addressing threats only after they occur, leading to delayed responses and increased potential for harm to critical systems.
In response to these challenges, artificial intelligence (AI) and machine learning (ML) have emerged as promising solutions in the cybersecurity domain. By enabling systems to analyze vast amounts of data, learn from threat patterns, and detect anomalies in real-time, AI and ML can significantly improve the accuracy and efficiency of threat detection and incident response. However, existing AI-based solutions face limitations, such as high false positive rates and difficulty in adapting to new, unseen threats without extensive human intervention.
This invention addresses these gaps by introducing an AI-based predictive security system designed for early threat detection and anomaly identification within cyber networks. It integrates supervised and unsupervised learning algorithms, leveraging real-time data streams, historical threat intelligence, and adaptive models to autonomously identify and respond to both known and unknown threats. This approach provides a proactive layer of cybersecurity, reducing reliance on manual oversight, minimizing false positives, and enhancing the overall resilience of an organization's cyber defense. The invention is particularly suited for high-stakes industries where data protection and network integrity are critical.

SUMMARY OF THE INVENTION

The present invention, an AI-Based Predictive Security System for Early Threat Detection and Anomaly Identification in Cyber Networks (AI-PSS-ETD-AICN), provides an innovative cybersecurity solution utilizing advanced artificial intelligence (AI) and machine learning (ML) techniques for proactive defense against cyber threats. Unlike conventional security systems that rely on static rules or manual configurations, this invention incorporates adaptive, predictive algorithms capable of autonomously detecting and mitigating threats in real time.
The AI-PSS-ETD-AICN operates by analyzing both real-time network data and historical threat intelligence to identify anomalies and potential threats before they impact critical infrastructure. The system employs a combination of supervised learning for known threat patterns and unsupervised learning for detecting novel, unknown attack vectors. Additionally, the system includes a dynamic threat scoring module that prioritizes threats based on severity, enabling more efficient allocation of cybersecurity resources.
A key feature of this invention is its multi-layered defense strategy, which integrates network monitoring, anomaly detection, and automatic incident response. The architecture seamlessly integrates with existing cybersecurity infrastructure and is designed to minimize false positives, ensuring that alerts are both actionable and accurate. Furthermore, the system's adaptive learning capability enables it to evolve with the changing cyber threat landscape, continuously refining its detection models based on new data and attack trends.
In summary, the AI-PSS-ETD-AICN enhances cybersecurity by providing organizations with an intelligent, predictive, and autonomous defense system, improving both the speed and accuracy of threat detection and minimizing reliance on manual oversight. This invention is particularly valuable for industries that handle sensitive data or require high levels of network security, such as finance, healthcare, and critical infrastructure.

BRIEF DESCRIPTION OF FIGURES

The comprehension of these features, aspects, and benefits of the current innovation will be enhanced upon reviewing the following detailed description, accompanied by corresponding illustrations. Similar symbols denote analogous components consistently across all illustrations
Figure 1 illustrates the Block Diagram of the Invention. This diagram offers a comprehensive visualization of the primary components employed in the innovation. It presents a detailed portrayal of their functions and how they interact within the system, providing extensive insights into the innovation's operational framework.;
Figure 2 depicts the Process Flow of the Invention. This diagram delineates the sequential steps and progression inherent in the innovation. It clarifies the methodical sequence of operations crucial to understanding the functionality of the invention.;
Figure 3 presents an Architect of Actual Design in the invention. This illustration provides insight into the various components incorporated, elucidating their roles and contributions within the innovation's operational framework;
Figure 4 presents the Prototype of the Invention. This visual depiction offers an illustration of the physical manifestation of the innovation, showcasing a tangible model or an initial version. It emphasizes essential design elements and notable features, providing a visual representation of the innovation's early-stage development.
The illustrations within the drawings are simplified for clarity and might not adhere to exact proportions. For instance, the flow charts emphasize the essential steps to enhance comprehension of the invention's aspects. Additionally, certain components in the device might be symbolically represented, and the drawings might focus solely on pertinent details. This approach prevents unnecessary complexity in the drawings, ensuring that skilled individuals in the field can readily grasp the embodiments detailed in the description provided.

DETAILED DESCRIPTION:
For Figure 1: Block Diagram of the AI-Based Predictive Security System for Early Threat Detection and Anomaly Identification in Cyber Networks (AI-PSS-ETD-AICN), the illustration would ideally include the following components, represented as distinct blocks with directional arrows to show the data flow between them:
101. Data Collection Module
Collects real-time network data from various endpoints (e.g., servers, devices, firewalls).
102. Feature Extraction Engine
Processes raw data to extract key characteristics such as packet metadata, user behavior patterns, and threat signatures.
103. Threat Detection Module
Contains models trained with supervised learning for detecting known threats.
Outputs identified threats and sends them to the Threat Scoring and Prioritization Engine (105).
104. Anomaly Identification Module
Uses unsupervised learning to identify novel or unknown threats.
105. Threat Scoring and Prioritization Engine
Assigns severity scores and ranks threats based on threat type, potential impact, and propagation risk.
106. Incident Response Module
Executes automated actions based on threat severity, such as quarantine, isolation, or escalation to security teams.
107. Adaptive Learning Loop
A feedback loop that retrains models in the Threat Detection Module and Anomaly Identification Module based on incident outcomes, ensuring the system improves over time. Connects back to Threat Detection Module and Anomaly Identification Module from Incident Response Module..

This figure illustrates the sequential flow of processes within the AI-PSS-ETD-AICN system, starting from data collection and ending with adaptive learning for system improvement. Each stage is numbered starting from 201 to reflect the chronological sequence, with directional arrows to indicate data flow and progression through the stages:
201. Start
The process begins with network monitoring, where the system actively observes data sources for potential security events.
202. Data Collection
System gathers real-time data from network endpoints, including logs, telemetry data, and user activity metrics.
203. Data Pre-processing
Cleans and standardizes the raw data to ensure effective feature extraction and analysis.
204. Feature Extraction
Extracts relevant features such as network patterns, user behavior, and packet data for further analysis.
205. Threat Detection (Supervised Learning)
Analyzes data using pre-trained models to identify known threats based on supervised learning methods.
206. Anomaly Identification (Unsupervised Learning)
Uses unsupervised models to detect unknown anomalies or unusual network behaviors that may indicate novel threats.
207. Threat Scoring and Prioritization
Assigns severity scores and prioritizes each detected threat or anomaly based on its impact, urgency, and propagation risk.
208. Incident Response
Executes automated or manual response actions based on threat priority, such as isolation, quarantine, or alert escalation to security personnel.
209. Adaptive Learning and Model Retraining
Uses incident data and outcomes to continuously update the Threat Detection (205) and Anomaly Identification (206) models, enhancing future detection accuracy. The process resets and continues network monitoring, providing a continuous and adaptive security cycle to protect the network environment.
Figure 3 would illustrate the overall architecture, detailing the system's layers and infrastructure, including the interconnections among various functional components. The architectural design might include the following key elements organized into distinct layers and blocks:. Here's a description for Figure 3:
Architecture Components:
301. Data Ingestion Layer
Endpoints and Sources: Represents servers, devices, IoT nodes, and firewalls generating network and user activity data.
Data Aggregators: Modules that centralize and aggregate data from multiple endpoints for analysis.
Threat Intelligence Feeds: External databases or sources that provide real-time threat indicators and signatures to enhance detection capabilities.
302. Data Processing and Analysis Layer
Data Pre-processing Unit: Cleans and standardizes incoming data, performing noise reduction, normalization, and formatting to prepare for analysis.
Feature Extraction Engine: Derives relevant attributes such as behavioral metrics, network activity logs, and packet metadata for further processing.
303. Core AI/ML Analytics Layer
Threat Detection Engine (Supervised ML): Utilizes models trained on historical threat data to detect known threats such as malware, ransomware, DDoS attacks, and phishing.
Anomaly Detection Engine (Unsupervised ML): Employs models designed for detecting unknown threats through clustering, outlier detection, and behavior deviation analysis.
Adaptive Learning Mechanism: Continuously retrains models based on feedback and results from past incidents to improve detection accuracy.
304. Threat Scoring and Response Layer
Threat Scoring and Prioritization Module: Assigns risk levels to detected threats and prioritizes them based on impact, urgency, and likelihood of propagation.
Automated Response Controller: Triggers real-time countermeasures, including network isolation, device quarantine, and escalation alerts to security personnel.
Incident Logging and Escalation: Records all details of incidents and escalates high-severity threats to security teams for further analysis and action.
305. System Management and Interface Layer
Security Dashboard: Displays live status updates, prioritized threats, and ongoing incident responses, accessible to security analysts for monitoring.
Configuration Manager: Allows system administrators to update configurations, set response rules, and adjust parameters for machine learning models.
Audit and Compliance Module: Ensures all system actions are logged and meet compliance with relevant regulatory frameworks.
306. External Integrations
API Layer: Provides secure access points for integration with other enterprise security solutions, such as Security Information and Event Management (SIEM) systems, firewalls, and endpoint protection solutions.
Reporting Tools: Exports data for analytics, generating audit reports, and ensuring compliance documentation.
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This architectural diagram should visually connect each layer and component, showing the data flow from the imaging devices to preprocessing, analysis, alert generation, and UI interaction. The design highlights the interconnected infrastructure enabling real-time, adaptive threat detection across varied environments.
Figure 400: Complete System Architecture
This figure presents an overview of the complete architecture of the AI-Based Predictive Security System for Early Threat Detection and Anomaly Identification in Cyber Networks (AI-PSS-ETD-AICN). It illustrates the integration of various modules and subsystems, showcasing the system's capacity to seamlessly acquire, process, and analyze data from diverse network endpoints to detect cyber threats in real-time. The architecture encapsulates essential components such as data ingestion, preprocessing, feature extraction, threat detection, anomaly identification, and incident response. This cohesive framework optimizes threat detection and response across different cybersecurity environments, ensuring robust protection against emerging threats.
Figure 401: User Interface (UI)
Figure 401 displays the user interface of the AI-PSS-ETD-AICN model, specifically designed to provide security personnel with a streamlined, interactive dashboard for monitoring and responding to potential threats. The UI presents real-time alerts, threat scores, and analytical insights derived from processed data, facilitating efficient decision-making. Key features include graphical representations of identified threats, system status indicators, and customizable alert thresholds. This design enhances situational awareness and promotes intuitive user interaction, enabling security teams to swiftly address and mitigate threats.
Figure 402: System Infrastructure and Processing Platform
This figure represents the hardware and infrastructure necessary to support the operations of the AI-PSS-ETD-AICN system. The infrastructure includes high-performance computing resources, such as servers and edge devices, that manage extensive data processing and real-time analysis. This setup provides the computational power and scalability required for continuous network monitoring, threat detection, and automated alert generation. The design ensures the system's functionality and resilience, even under high data loads, validating its capability to effectively safeguard cyber networks against evolving threats.
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The displayed information facilitates informed decision-making, empowering users to observe, interpret, and respond to real-time health data. This aids in effective health monitoring, timely interventions, and informed actions to ensure optimal health management and security measures during the home quarantine period.
The illustrations and descriptions provided in the foregoing detail examples of embodiments. It's important to note that skilled individuals in this field can combine multiple described elements into a single functional element, or conversely, separate certain elements into multiple functional components. Additionally, elements from one embodiment may be incorporated into another embodiment. For instance, the sequence of processes outlined here can be altered and is not confined to the manner described. Furthermore, the actions depicted in flow diagrams need not strictly adhere to the shown order, and acts that are not reliant on each other can be performed simultaneously.
It's crucial to understand that the scope of embodiments is not confined to the specific examples provided. Numerous variations, whether explicitly stated or not, including differences in structure, dimensions, and materials used, are within the realm of possibility. The breadth of embodiments extends at least as broadly as outlined in the following claims.
While the previously described embodiments have highlighted specific benefits, advantages, and solutions to problems, it's important to note that these aspects, as well as any component contributing to such benefits, advantages, or solutions, are not necessarily mandatory or critical features in all claims.
, C , C , C , C , C , Claims:Claim 1: A system for proactive cybersecurity, comprising:a data collection module configured to collect real-time data from multiple network endpoints; a feature extraction engine to process the collected data into relevant features for analysis; a threat detection module leveraging supervised machine learning algorithms for identifying known threats; an anomaly identification module employing unsupervised learning techniques to detect unknown or novel threats.
Claim 2: The system of Claim 1, wherein the feature extraction engine identifies features based on user behavior patterns, network packet characteristics, and historical threat signatures to enhance model accuracy.
Claim 3: The system of Claim 1, wherein the threat detection module comprises machine learning models trained on historical data to detect a variety of cyber threats, including malware, phishing, and brute-force attacks.
Claim 4: The system of Claim 1, wherein the anomaly identification module utilizes clustering, outlier analysis, and other unsupervised learning techniques to detect deviations from normal network behavior, indicating potential new or unknown threats.
Claim 5: A method for dynamic threat scoring and prioritization in a cybersecurity system, comprising: assigning a threat score to each detected threat based on factors including threat type, potential impact, and likelihood of propagation; ranking threats according to the calculated threat score; and providing a prioritized list of threats for investigation by security personnel.
Claim 6: The system of Claim 1, further comprising a predictive threat detection component that combines supervised and unsupervised learning to detect both known and unknown threats within a network environment.
Claim 7: The system of Claim 1, wherein the incident response module initiates real-time containment of threats by isolating affected devices, quarantine actions, and escalates alerts to designated security personnel for high-severity incidents.
Claim 8: The system of Claim 1, further comprising an adaptive learning mechanism configured to continuously retrain the threat detection and anomaly identification models based on new threat data and feedback from prior incidents.
Claim 9: A cybersecurity system as claimed in Claim 1, wherein the system is scalable and capable of integration with existing security platforms, including firewalls, endpoint security solutions, and Security Information and Event Management (SIEM) systems.
Claim 10: The system of Claim 1, wherein the threat detection module autonomously updates based on new threat intelligence data, thereby allowing detection of newly developed threats without requiring extensive manual input.

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