1. An Artificial Intelligence–Based Cybersecurity System for Real-Time Intrusion Detection and Threat Mitigation claims that a system comprising a data acquisition module configured to continuously collect data from network traffic, user activities, system logs, and connected devices.

2. The system as claimed in claim 1, wherein the collected data is processed through a preprocessing module configured to perform data cleaning, normalization, and feature extraction for accurate analysis.

3. The system as claimed in claim 1, wherein an artificial intelligence-based detection engine is employed to analyze processed data using machine learning algorithms to identify malicious patterns and anomalies.

4. The system as claimed in claim 3, wherein the machine learning algorithms include supervised and unsupervised learning models for detecting both known and unknown cyber threats.

5. The system as claimed in claim 1, wherein an anomaly detection module is configured to compare real-time system behavior with predefined baseline patterns to identify deviations indicative of potential intrusions.

6. The system as claimed in claim 1, wherein a threat classification unit is configured to categorize detected anomalies based on severity levels for appropriate response actions.

7. The system as claimed in claim 1, wherein a real-time threat mitigation module is configured to automatically execute response actions including blocking malicious entities, isolating compromised systems, terminating suspicious sessions, and restricting unauthorized access.

8. The system as claimed in claim 1, wherein an alert and reporting module is configured to generate notifications and detailed reports for system administrators regarding detected threats and mitigation actions.

9. The system as claimed in claim 1, wherein an adaptive learning mechanism is incorporated to update the artificial intelligence models based on feedback from previous threat detection and mitigation outcomes.

10. The system as claimed in claim 1, wherein the architecture is scalable and deployable across cloud environments, enterprise networks, Internet of Things systems, and cyber-physical systems while maintaining real-time performance and security.

Description:FIELD OF INVENTION
The invention relates to cybersecurity systems, specifically artificial intelligence-based intrusion detection and automated threat mitigation for real-time protection of networked and cyber-physical environments.
BACKGROUND OF INVENTION
With the rapid expansion of interconnected systems, including cloud computing, Internet of Things (IoT), and cyber-physical systems, the frequency and complexity of cyberattacks have increased significantly. Traditional security mechanisms such as firewalls and signature-based intrusion detection systems are often insufficient in identifying sophisticated and zero-day attacks. These conventional approaches rely heavily on predefined rules and known threat signatures, making them ineffective against evolving attack patterns. Furthermore, manual monitoring and delayed response mechanisms lead to increased vulnerability and potential system compromise. There is a growing need for intelligent systems capable of analyzing large volumes of real-time data and adapting to dynamic threat landscapes. Artificial intelligence and machine learning offer promising solutions by enabling systems to learn patterns, detect anomalies, and respond autonomously. However, existing AI-based systems often lack integration with real-time mitigation mechanisms, highlighting the need for a comprehensive solution that combines detection and response within a unified framework.
The patent application number 202431030344 discloses a multimode fiber-based intrusion detection system. A multimode fiber-based intrusion detection system uses optical fibers to sense disturbances via light signal variations, enabling real-time monitoring, localization, and detection of physical intrusions along secured perimeters.
The patent application number 202441036235 discloses a system and method for detection and mitigation of cyber threats in social networking platforms. An intelligent system that monitors social networking platforms using ai to detect cyber threats, analyze malicious behavior, and automatically implement mitigation strategies to enhance user security, privacy, and platform integrity in real time.
The patent application number 202511045288 discloses a cyber-physical system for real-time patients health monitoring and intrusion detection. A cyber-physical system integrating wearable sensors, iot, and security algorithms to monitor patients’ vital signs in real time while detecting intrusions, ensuring data integrity, privacy, and timely medical intervention.
The patent application number 202441036235 discloses a system and method for detection and mitigation of cyber threats in social networking platforms. A system that monitors social media activity using ai to detect cyber threats like phishing, malware, and fake accounts, and automatically mitigates risks through alerts, filtering, and adaptive security responses.
The patent application number 202511045288 discloses a cyber-physical system for real-time patients health monitoring and intrusion detection. A cyber-physical system integrating wearable sensors, iot, and ai to continuously monitor patient health in real time while detecting anomalies and cyber intrusions, ensuring secure, reliable, and timely medical intervention.
OBJECTIVE OF THE INVENTION
The primary objective of the invention is to develop an AI-driven cybersecurity system capable of detecting intrusions in real time and automatically mitigating threats, thereby enhancing system security, reducing response time, and ensuring continuous protection of critical digital infrastructure against evolving cyberattacks.

SUMMARY
The invention proposes a comprehensive cybersecurity system that utilizes artificial intelligence for real-time intrusion detection and automated threat mitigation. The system comprises data acquisition modules, preprocessing units, machine learning models, anomaly detection engines, and response mechanisms. It collects data from multiple sources such as network traffic, system logs, and user behavior patterns. The collected data is processed and analyzed using advanced machine learning techniques including supervised and unsupervised learning models. The system identifies deviations from normal behavior to detect potential intrusions. Upon detection, it triggers automated mitigation actions such as blocking suspicious IP addresses, isolating compromised nodes, and generating alerts for administrators. The system is designed to operate in real time with minimal latency and supports scalable deployment across various environments. This integrated approach improves detection accuracy, reduces human intervention, and enhances the overall security posture of digital systems.
DETAILED DESCRIPTION OF INVENTION
The present invention relates to an artificial intelligence–based cybersecurity system designed to provide real-time intrusion detection and automated threat mitigation. The system is structured as a multi-layered architecture that integrates data acquisition, preprocessing, intelligent analysis, and response execution modules into a unified framework. The architecture is designed to ensure seamless communication between components while maintaining high efficiency, scalability, and adaptability.

Figure 1: System architecture.
The system comprises interconnected modules including data acquisition sources, preprocessing engines, machine learning-based detection units, anomaly detection layers, and mitigation components. These modules are interconnected through a secure communication interface, ensuring that data flows continuously and securely between layers.
The architecture is flexible and can be deployed across various environments, including enterprise networks, cloud platforms, Internet of Things ecosystems, and cyber-physical systems. The modular design allows for easy integration with existing infrastructure without requiring significant modifications.
Data Acquisition and Monitoring Layer
The data acquisition layer serves as the foundational component of the system, responsible for collecting real-time data from multiple sources. These sources include network traffic streams, server logs, user activity records, endpoint devices, and IoT sensors. The system is capable of capturing both structured and unstructured data, ensuring comprehensive monitoring of all potential entry points for cyber threats.
The module continuously monitors incoming and outgoing data packets, capturing parameters such as packet size, transmission frequency, protocol type, and source and destination addresses. Additionally, it records system-level events such as login attempts, file access activities, and application usage patterns.
To handle high volumes of data, the acquisition module employs streaming technologies that enable real-time data ingestion without introducing latency. The system ensures that no critical data is lost during transmission by implementing buffering and redundancy mechanisms.
Furthermore, the data acquisition layer includes mechanisms for filtering irrelevant data, ensuring that only meaningful information is forwarded to the preprocessing module. This selective data capture enhances system efficiency and reduces computational overhead.
Data Preprocessing and Feature Engineering
Once the data is collected, it is transmitted to the preprocessing module, where it undergoes cleaning, normalization, and transformation. Raw data often contains noise, inconsistencies, and missing values, which can affect the accuracy of the detection system. The preprocessing module addresses these issues by applying data cleaning techniques such as outlier removal, normalization, and encoding.
Feature engineering plays a crucial role in enhancing the performance of machine learning models. The system extracts relevant features from the processed data, including behavioral attributes, statistical measures, and temporal patterns. Examples of extracted features include connection duration, frequency of access requests, data transfer rates, and deviation from normal usage patterns.
The module also performs dimensionality reduction to eliminate redundant features and improve computational efficiency. Techniques such as principal component analysis and feature selection algorithms are employed to retain only the most significant attributes.
The processed and feature-rich data is then forwarded to the intelligent detection engine for further analysis.

Artificial Intelligence-Based Detection Engine
The detection engine forms the core of the invention, utilizing artificial intelligence techniques to identify potential cyber threats. The engine employs a hybrid approach that combines supervised and unsupervised learning models to achieve high detection accuracy.

Figure 2: Machine learning-based intrusion detection workflow.
The workflow begins with the training phase, where the system learns patterns of normal and abnormal behavior from historical data. Supervised learning models such as decision trees, support vector machines, and neural networks are used to classify known threats. Simultaneously, unsupervised models such as clustering algorithms and autoencoders are employed to detect unknown or zero-day attacks.
During the operational phase, real-time data is fed into the trained models, which analyze the data and identify deviations from established patterns. The system assigns a risk score to each activity based on its likelihood of being malicious.
The detection engine continuously updates its models using new data, ensuring that it adapts to evolving threat landscapes. This dynamic learning capability enhances the system’s ability to detect sophisticated attacks that may not have been previously encountered.

Anomaly Detection Mechanism
The anomaly detection mechanism is designed to identify unusual activities that deviate from normal system behavior. It operates in conjunction with the AI detection engine to provide an additional layer of security.
The mechanism compares real-time data with baseline behavior models established during the training phase. Any significant deviation from these models is flagged as a potential anomaly. The system uses statistical analysis, pattern recognition, and deep learning techniques to improve detection accuracy.
The anomaly detection module is capable of identifying various types of cyber threats, including unauthorized access attempts, data exfiltration, denial-of-service attacks, and malware activities. It also distinguishes between benign anomalies and malicious activities, reducing the occurrence of false positives.
Real-Time Threat Mitigation Module
Upon detecting a potential intrusion, the system activates the threat mitigation module, which is responsible for executing appropriate response actions. The response is automated and occurs in real time, minimizing the impact of the threat.

Figure 3: Real-time threat mitigation process flow.
The process begins with the identification of a threat, followed by classification and prioritization. Based on the severity of the threat, the system initiates actions such as blocking suspicious IP addresses, terminating compromised sessions, isolating affected nodes, and restricting access to sensitive resources.
The system also generates alerts and notifications for system administrators, providing detailed information about the detected threat and the actions taken. This allows administrators to perform further analysis and take additional measures if necessary.
The automated response mechanism reduces the reliance on manual intervention, ensuring rapid and effective threat mitigation.
Adaptive Learning and Feedback Mechanism
The invention incorporates an adaptive learning mechanism that enables the system to improve its performance over time. The system maintains a repository of detected threats, responses, and outcomes, which is used to refine its models and decision-making processes.

Figure 4: Adaptive learning feedback loop of the system.
The feedback loop collects data from previous incidents and uses it to update the machine learning models. This continuous learning process allows the system to adapt to new attack patterns and improve its detection accuracy.
The feedback mechanism also helps in reducing false positives and false negatives by fine-tuning detection thresholds and model parameters. This ensures that the system remains reliable and efficient in dynamic environments.
Integration with Cyber-Physical Systems
The system is designed to integrate seamlessly with cyber-physical systems, where physical processes are controlled by digital components. Examples include healthcare monitoring systems, industrial automation, smart grids, and transportation systems.
In such environments, the system ensures secure communication between physical devices and digital networks. It prevents unauthorized access and protects critical infrastructure from cyber threats.
The integration is achieved through standardized communication protocols and secure interfaces, enabling the system to operate effectively in diverse applications.
Scalability and Deployment Flexibility
The invention is designed to be scalable and adaptable to different deployment scenarios. It can be implemented in centralized, distributed, or hybrid architectures, depending on the requirements of the environment.
The system supports deployment in cloud environments, where it can leverage high computational power for data analysis. It can also be deployed at the edge, enabling real-time processing with minimal latency.
Load balancing and parallel processing techniques are employed to ensure optimal performance, even in high-demand environments. The system can handle large volumes of data without compromising on speed or accuracy.
Security and Privacy Considerations
The system incorporates robust security measures to protect sensitive data and ensure privacy. Data encryption techniques are used to secure data during transmission and storage. Access control mechanisms are implemented to restrict unauthorized access to system components.
The system also complies with data protection regulations, ensuring that user privacy is maintained. It anonymizes sensitive data and uses secure authentication methods to prevent unauthorized access.
Performance Optimization
To achieve high performance, the system employs various optimization techniques. These include efficient data structures, parallel processing, and hardware acceleration. The system is capable of processing large datasets in real time without significant delays.
Performance metrics such as detection accuracy, response time, and system throughput are continuously monitored and optimized. This ensures that the system operates efficiently under different conditions.
Use Case Scenarios
The invention can be applied in various domains where cybersecurity is critical. In healthcare systems, it protects patient data and ensures secure communication between devices. In financial systems, it prevents fraud and unauthorized transactions. In industrial environments, it safeguards critical infrastructure from cyberattacks.
The system’s ability to detect and mitigate threats in real time makes it suitable for applications where security is of utmost importance.
The proposed artificial intelligence–based cybersecurity system provides a comprehensive solution for real-time intrusion detection and threat mitigation. By integrating advanced machine learning techniques with automated response mechanisms, the system enhances the security of digital and cyber-physical environments.
The modular architecture, adaptive learning capabilities, and scalability of the system make it a versatile solution for addressing modern cybersecurity challenges. The invention significantly improves detection accuracy, reduces response time, and ensures continuous protection against evolving cyber threats.

DETAILED DESCRIPTION OF DIAGRAM
Figure 1: System architecture.
Figure 2: Machine learning-based intrusion detection workflow.
Figure 3: Real-time threat mitigation process flow.
Figure 4: Adaptive learning feedback loop of the system. , Claims:1. An Artificial Intelligence–Based Cybersecurity System for Real-Time Intrusion Detection and Threat Mitigation claims that a system comprising a data acquisition module configured to continuously collect data from network traffic, user activities, system logs, and connected devices.
2. The system as claimed in claim 1, wherein the collected data is processed through a preprocessing module configured to perform data cleaning, normalization, and feature extraction for accurate analysis.
3. The system as claimed in claim 1, wherein an artificial intelligence-based detection engine is employed to analyze processed data using machine learning algorithms to identify malicious patterns and anomalies.
4. The system as claimed in claim 3, wherein the machine learning algorithms include supervised and unsupervised learning models for detecting both known and unknown cyber threats.
5. The system as claimed in claim 1, wherein an anomaly detection module is configured to compare real-time system behavior with predefined baseline patterns to identify deviations indicative of potential intrusions.
6. The system as claimed in claim 1, wherein a threat classification unit is configured to categorize detected anomalies based on severity levels for appropriate response actions.
7. The system as claimed in claim 1, wherein a real-time threat mitigation module is configured to automatically execute response actions including blocking malicious entities, isolating compromised systems, terminating suspicious sessions, and restricting unauthorized access.
8. The system as claimed in claim 1, wherein an alert and reporting module is configured to generate notifications and detailed reports for system administrators regarding detected threats and mitigation actions.
9. The system as claimed in claim 1, wherein an adaptive learning mechanism is incorporated to update the artificial intelligence models based on feedback from previous threat detection and mitigation outcomes.
10. The system as claimed in claim 1, wherein the architecture is scalable and deployable across cloud environments, enterprise networks, Internet of Things systems, and cyber-physical systems while maintaining real-time performance and security.