Description:AI-POWERED INTRUSION DETECTION SYSTEM
TECHNICAL FIELD
[0001] The present innovation relates to an AI-powered intrusion detection system (AI-IDS) for cybersecurity, leveraging machine learning, deep learning, and threat intelligence for real-time network security monitoring and threat mitigation.

BACKGROUND

[0002] With the increasing digital transformation across industries, cybersecurity threats have evolved in complexity, making traditional security measures inadequate. Cyberattacks such as zero-day exploits, advanced persistent threats (APTs), ransomware, and insider threats pose severe risks to critical systems, cloud environments, and enterprise networks. Traditional Intrusion Detection Systems (IDS) are designed to monitor network traffic and system activities for malicious behavior, but they face significant limitations.

[0003] Currently, users have Signature-Based Intrusion Detection Systems (SIDS) and Anomaly-Based Intrusion Detection Systems (AIDS). SIDS relies on predefined signatures of known threats, making it effective against previously identified attacks but ineffective against novel or evolving threats. On the other hand, AIDS identifies anomalies by detecting deviations from normal network behavior, allowing it to detect unknown threats. However, traditional AIDS system from high false positive rates, computational inefficiencies, and limited adaptability, making them unreliable for real-world deployment.

[0004] The present invention introduces an AI-powered Intrusion Detection System (AI-IDS) that overcomes these limitations by integrating machine learning, deep learning, and threat intelligence for real-time, adaptive threat detection. Unlike conventional IDS system, the AI-IDS continuously learns from network activity, refines its detection logic, and adapts to emerging cyber threats without requiring constant manual updates. The system utilizes deep learning models (CNNs, RNNs, Transformers), federated learning, and hybrid detection techniques to enhance accuracy while reducing false alarms.

[0005] A key novelty of the invention is its ability to predict and mitigate zero-day attacks, leverage real-time packet inspection, and execute automated threat response strategies without human intervention. Additionally, the AI-IDS seamlessly integrates with cloud environments, IoT networks, and enterprise infrastructures, making it scalable and efficient for modern cybersecurity needs. By combining behavioral analysis, explainable AI, and threat intelligence correlation, the invention provides a proactive, automated, and intelligent cybersecurity system that surpasses traditional IDS in accuracy, adaptability, and mitigation efficiency.

OBJECTS OF THE INVENTION

[0006] The primary object of the invention is to enhance cybersecurity by providing an AI-powered Intrusion Detection System (AI-IDS) that detects, analyzes, and mitigates security threats in real-time.

[0007] Another object of the invention is to improve threat detection accuracy by leveraging deep learning models such as CNNs, RNNs, and Transformers for identifying complex attack patterns and anomalies.

[0008] Another object of the invention is to reduce false positive rates by utilizing adaptive machine learning algorithms that intelligently differentiate between legitimate network activities and potential threats.

[0009] Another object of the invention is to enable zero-day attack detection through AI-driven predictive analytics, allowing the system to identify and mitigate unknown cyber threats before they can be exploited.

[00010] Another object of the invention is to offer a hybrid detection approach by combining signature-based intrusion detection with AI-driven behavioral analysis for a more comprehensive security framework.

[00011] Another object of the invention is to provide automated incident response and mitigation, allowing the system to take predefined actions such as blocking malicious IPs, isolating infected devices, and notifying administrators.

[00012] Another object of the invention is to integrate federated learning and adaptive AI models, ensuring continuous improvement in threat detection without compromising data privacy or requiring extensive retraining.

[00013] Another object of the invention is to support multi-environment deployment, making the system scalable for cloud-based infrastructures, IoT networks, industrial control systems, and enterprise security frameworks.

[00014] Another object of the invention is to incorporate explainable AI (XAI) for transparent and interpretable threat analysis, helping security teams understand and validate AI-driven decisions.

[00015] Another object of the invention is to enhance cybersecurity resilience through blockchain-based security logging, ensuring the integrity and immutability of intrusion detection logs and threat intelligence records.

SUMMARY OF THE INVENTION

[00016] In accordance with the different aspects of the present invention, an AI powered intrusion detection system is presented. It enhances cybersecurity through machine learning, deep learning, and threat intelligence for real-time threat detection and mitigation. The system leverages adaptive AI models, federated learning, and deep learning algorithms (CNNs, RNNs, Transformers) to detect zero-day attacks, reduce false positives, and improve response efficiency. It integrates hybrid detection mechanisms, combining signature-based and behavior-based analysis for comprehensive threat identification. The AI-IDS features automated incident response, real-time packet inspection, and cloud-native scalability for multi-environment deployment. By leveraging explainable AI (XAI) and blockchain-based security logging, the invention ensures transparency, accuracy, and robust cybersecurity defense.

[00017] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00018] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00019] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00020] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00021] FIG. 1 is component wise drawing for AI-powered intrusion detection system.

[00022] FIG 2 is working methodology of AI-powered intrusion detection system.

DETAILED DESCRIPTION

[00023] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00024] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of AI-powered intrusion detection system and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00025] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00026] The terms “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00027] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00028] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00029] Referring to Fig. 1, AI - powered intrusion detection system 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data collection module 102, feature extraction and preprocessing unit 104, AI-driven detection engine 106, federated learning and adaptive AI module 108, threat correlation and intelligence module 110, automated response and mitigation system 112, user interface and visualization dashboard 114, real-time packet inspection and deep packet analysis module 116, AI-powered phishing and social engineering detection module 118, user behavior analytics (UBA) module 120, blockchain-enhanced security logging system 122, smart honeypots for attack diversion 124, multi-layered defense system 126, automated compliance monitoring and reporting system 128, AI-powered deception module 130, self-healing security module 132, explainable AI (XAI) module 134.

[00030] Referring to Fig. 1, the present disclosure provides details of AI - powered intrusion detection system 100. It is designed to enhance network security through machine learning, deep learning, and threat intelligence. It enables real-time threat detection, adaptive anomaly analysis, and automated incident response, ensuring proactive cybersecurity defense. In one of the embodiments, the AI-powered IDS may be provided with the following key components such as data collection module 102, feature extraction and preprocessing unit 104, and AI-driven detection engine 106, facilitating efficient data processing and threat identification. The system incorporates federated learning and adaptive AI module 108 and threat correlation and intelligence module 110 to enhance accuracy and predictive threat mitigation. It also features real-time packet inspection and deep packet analysis module 116 for encrypted traffic analysis and automated response and mitigation system 112 to execute security actions instantly. Additional components such as self-healing security module 132 and explainable AI (XAI) module 134 ensure continuous security adaptation and transparent threat analysis.

[00031] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with data collection module 102, which gathers real-time data from various sources. In one of the embodiments, it gathers data from network traffic, endpoint logs, system activities, and cloud environments. This module 102 plays a crucial role in providing raw security data for analysis and threat detection. It preprocesses the data to remove noise and normalizes it for structured input into feature extraction and preprocessing unit 104. The data collection module 102 continuously feeds security logs and metadata into AI-driven detection engine 106, ensuring timely anomaly detection. It also works closely with threat correlation and intelligence module 110, which enriches collected data with external threat intelligence for better threat identification.

[00032] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with feature extraction and preprocessing unit 104, which applies advanced data analytics to transform raw security data into structured inputs for machine learning models. It ensures efficient data normalization, feature selection, and noise reduction to enhance detection accuracy. This unit 104 collaborates with data collection module 102 to process incoming security logs before passing structured data to AI-driven detection engine 106. By optimizing data representation, it improves the accuracy of real-time packet inspection and deep packet analysis module 116, enabling more precise detection of malicious patterns. The feature extraction and preprocessing unit 104 also refines network behavior analytics used by user behavior analytics (UBA) module 120, allowing better anomaly classification.

[00033] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with AI-driven detection engine 106, which forms the core of the system by leveraging deep learning models such as CNNs, RNNs, and Transformers for real-time threat identification. It classifies activities based on historical attack data and detects anomalies in live network traffic. This engine 106 interacts with feature extraction and preprocessing unit 104 to analyze optimized security features and detect threats with higher accuracy. It also utilizes federated learning and adaptive AI module 108 to improve threat detection continuously without requiring centralized data storage. Additionally, it works in conjunction with threat correlation and intelligence module 110 to refine attack signatures and enhance predictive analytics for zero-day threats.

[00034] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with federated learning and adaptive AI module 108, which ensures continuous learning and adaptability of AI models across multiple instances of the intrusion detection system. It enables AI models to refine their detection capabilities without exposing sensitive network data to external systems. This module 108 interacts with AI-driven detection engine 106, providing real-time updates on emerging threats and improving classification accuracy. It collaborates with threat correlation and intelligence module 110 to integrate global threat intelligence while ensuring privacy through decentralized learning. The federated learning and adaptive AI module 108 enhances system resilience by dynamically adjusting detection thresholds in response to changing attack patterns.

[00035] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with threat correlation and intelligence module 110. In different embodiments, it may integrate with different sources. In one of the embodiments, threat correlation and intelligence module 110 integrates with external threat intelligence feeds, industry attack databases, and cybersecurity reports to enhance detection accuracy. This module 110 analyzes real-time threat intelligence and correlates it with detected anomalies from AI-driven detection engine 106 to identify sophisticated attack strategies. It works closely with data collection module 102 to enhance raw security logs with enriched threat indicators. Additionally, it collaborates with real-time packet inspection and deep packet analysis module 116 to refine detection heuristics based on network packet behaviours. By integrating AI-driven analytics, this module 110 ensures proactive threat anticipation and faster incident response.

[00036] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with automated response and mitigation system 112, which executes predefined security actions upon detecting a cyber intrusion. This module 112 autonomously blocks malicious IP addresses, isolates compromised network segments, and generates real-time security alerts. It works in conjunction with AI-driven detection engine 106, receiving classified threat data and triggering appropriate countermeasures. The automated response and mitigation system 112 also integrates with user interface and visualization dashboard 114, ensuring security administrators can monitor and override automated responses if needed. Additionally, it leverages insights from self-healing security module 132 to initiate corrective actions and restore network integrity after an attack.

[00037] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with user interface and visualization dashboard 114, which provides real-time monitoring, analytics, and incident tracking for security administrators. In different embodiments cloud-integrated or web-based dashboard may be provided for visualisation purposes. It visualizes network threats, attack trends, and system health metrics. It interacts with automated response and mitigation system 112 to display security alerts and response actions. The user interface and visualization dashboard 114 works closely with explainable AI (XAI) module 134 to provide human-readable explanations for detected threats. Additionally, it enhances situational awareness by integrating real-time threat intelligence from threat correlation and intelligence module 110.

[00038] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with real-time packet inspection and deep packet analysis module 116, which analyzes network packets in real time to detect encrypted threats, stealth attacks, and malware signatures. This module 116 performs deep packet inspection (DPI) to examine payload data and detect sophisticated attack techniques. It receives network traffic logs from data collection module 102 and refines them using insights from feature extraction and preprocessing unit 104. The real-time packet inspection and deep packet analysis module 116 collaborates with threat correlation and intelligence module 110 to enhance its detection capabilities by integrating external threat indicators.

[00039] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with AI-powered phishing and social engineering detection module 118, which utilizes natural language processing (NLP) and AI-driven analysis to identify phishing attempts, fraudulent messages, and social engineering attacks. This module scans incoming emails, URLs, and chat messages to detect suspicious content and prevent credential theft. It works in conjunction with threat correlation and intelligence module 110 to verify phishing indicators against known attack patterns. The AI-powered phishing and social engineering detection module 118 also enhances user behavior analytics (UBA) module 120 by flagging user interactions that indicate potential manipulation attempts.

[00040] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with user behavior analytics (UBA) module 120, which continuously monitors and analyzes user activity to detect deviations from normal behavioral patterns that could indicate insider threats or unauthorized access. It leverages machine learning algorithms to profile user behaviour and identify anomalies, such as unusual login locations, irregular access times, or abnormal data access patterns. The user behavior analytics (UBA) module 120 collaborates with AI-driven detection engine 106 to enhance threat classification accuracy. Additionally, it interacts with automated response and mitigation system 112 to initiate security alerts or lockdown mechanisms in case of suspicious activity.

[00041] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with blockchain-enhanced security logging system 122, which ensures the integrity, immutability, and tamper resistance of security logs and event records. This module records intrusion detection data using a blockchain ledger, preventing unauthorized modifications or deletions. It works closely with data collection module 102 to store network logs securely and integrates with threat correlation and intelligence module 110 to validate attack patterns based on historical security events. The blockchain-enhanced security logging system 122 further enhances forensic investigations by providing cryptographically verifiable incident logs.

[00042] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with smart honeypots for attack diversion 124, which deploy AI-driven decoy systems to lure attackers and analyze their behavior in a controlled environment. These honeypots 124 mimic real network services and applications to deceive cybercriminals while collecting intelligence on their tactics and techniques. The smart honeypots for attack diversion 124 module integrates with AI-driven detection engine 106 to refine attack detection models based on attacker interactions. Additionally, it works with automated response and mitigation system 112 to trigger alerts and countermeasures when an attacker engages with the honeypot.

[00043] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with multi-layered defense system 126, which combines network security, endpoint protection, and behavioral analytics to create a comprehensive cybersecurity framework. This module 126 ensures that threat detection operates at multiple layers, including network perimeter, internal traffic, and user behavior monitoring. The multi-layered defense system 126 works closely with real-time packet inspection and deep packet analysis module 116 to detect encrypted threats and with user behavior analytics (uba) module 120 to track suspicious user activity. It also integrates with automated response and mitigation system 112 for proactive security enforcement.

[00044] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with automated compliance monitoring and reporting system 128, which ensures continuous compliance with regulatory frameworks such as GDPR, HIPAA, and NIST. This system 128 dynamically assesses security policies, configurations, and network activity against industry standards and legal requirements. The automated compliance monitoring and reporting system 128 generates detailed compliance reports and collaborates with blockchain-enhanced security logging system 122 to maintain an immutable record of compliance-related security events. It also assists threat correlation and intelligence module 110 in ensuring security updates align with evolving regulations.

[00045] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with AI-powered deception module 130, which creates realistic fake data, credentials, and network services to mislead attackers and gather intelligence on their behavior. This module 130 is designed to confuse cybercriminals by providing false targets, preventing unauthorized access to sensitive data while capturing details of attack strategies. The AI-powered deception module 130 integrates with smart honeypots for attack diversion 124 to create a sophisticated deception framework and collaborates with AI-driven detection engine 106 to refine adversary profiling and countermeasures.

[00046] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with self-healing security module 132, which ensures continuous security adaptation by automatically patching vulnerabilities and reconfiguring system defenses after detecting a cyberattack. This module 132 interacts with automated response and mitigation system 112 to implement proactive security measures. It works closely with federated learning and adaptive AI module 108 to refine defense strategies and minimize future attack risks. The self-healing security module 132 also integrates with AI-driven detection engine 106 to provide real-time feedback on system performance and vulnerability assessment.

[00047] Referring to Fig. 1, AI-powered Intrusion Detection System (AI-IDS) is provided with explainable AI (xai) module 134, which ensures transparency in AI-driven threat detection by providing interpretable explanations for security decisions. This module works with user interface and visualization dashboard 114 to generate human-readable insights on detected anomalies. It collaborates with AI-driven detection engine 106 to explain how threats are classified and why certain security actions are taken. The explainable AI (xai) module 134 also enhances trust and compliance by ensuring security teams can validate AI-driven cybersecurity decisions.

[00048] Referring to Fig 2, there is illustrated method 200 for AI-powered intrusion detection system (AI-IDS) 100. The method comprises:
At step 202, method 200 includes data collection module 102 gathering real-time network traffic, system logs, and endpoint activity for security analysis;
At step 204, method 200 includes feature extraction and preprocessing unit 104 processing the collected data by filtering noise, normalizing values, and extracting security-relevant features for analysis;
At step 206, method 200 includes AI-driven detection engine 106 analyzing structured data using deep learning models to classify network activity as normal or potentially malicious;
At step 208, method 200 includes federated learning and adaptive AI module 108 continuously refining detection models by learning from emerging cyber threats across multiple distributed instances;
At step 210, method 200 includes threat correlation and intelligence module 110 integrating external threat intelligence feeds, attack databases, and cybersecurity reports to enhance threat detection and prediction;
At step 212, method 200 includes real-time packet inspection and deep packet analysis module 116 performing deep packet inspection to detect hidden threats, encrypted attacks, and stealth malware signatures within network traffic;
At step 214, method 200 includes AI-powered phishing and social engineering detection module 118 analyzing emails, messages, and URLs using natural language processing (NLP) to detect phishing and social engineering attacks;
At step 216, method 200 includes user behavior analytics (uba) module 120 monitoring user activity to detect insider threats and unauthorized access based on deviations from normal behavior patterns;
At step 218, method 200 includes blockchain-enhanced security logging system 122 securely recording intrusion detection events and security logs using blockchain technology to ensure data integrity and immutability;
At step 220, method 200 includes smart honeypots for attack diversion 124 deploying AI-driven honeypots to lure attackers, analyze their tactics, and prevent real system compromise;
At step 222, method 200 includes multi-layered defense system 126 coordinating network security, endpoint protection, and behavioral analytics to create a comprehensive cybersecurity defense strategy;
At step 224, method 200 includes automated response and mitigation system 112 executing predefined security actions such as blocking malicious IPs, isolating infected devices, and generating automated security alerts;
At step 226, method 200 includes self-healing security module 132 automatically applying security patches, reconfiguring settings, and restoring system integrity after an attack is detected;
At step 228, method 200 includes automated compliance monitoring and reporting system 128 ensuring compliance with cybersecurity regulations by continuously monitoring network policies and generating compliance reports;
At step 230, method 200 includes AI-powered deception module 130 creating realistic fake data and network services to mislead attackers and gather intelligence on their strategies;
At step 232, method 200 includes explainable AI (xai) module 134 providing interpretable AI-driven insights on detected threats, helping security administrators understand decision-making processes.
[00049] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “fixed” “attached” “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00050] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, “is” used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00051] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

, Claims:WE CLAIM:
1. An AI-powered intrusion detection system (AI-IDS) 100 comprising of
data collection module 102 to gather real-time network traffic, system logs, and endpoint activity for security analysis;
feature extraction and preprocessing unit 104 to process collected data by filtering noise, normalizing values, and extracting security-relevant features;
AI-driven detection engine 106 to analyze structured data using deep learning models for classifying network activity and detecting anomalies;
federated learning and adaptive AI module 108 to refine detection models by continuously learning from emerging cyber threats;
threat correlation and intelligence module 110 to integrate external threat intelligence feeds and cybersecurity reports for enhanced detection;
automated response and mitigation system 112 to execute predefined security actions such as blocking malicious IPs and isolating infected devices;
user interface and visualization dashboard 114 to provide real-time monitoring, analytics, and incident tracking for security administrators through a cloud-integrated or web-based interface;
real-time packet inspection and deep packet analysis module 116 to perform deep packet inspection for detecting encrypted threats and stealth malware;
AI-powered phishing and social engineering detection module 118 to analyze emails, messages, and URLs for phishing and social engineering attacks;
user behaviour analytics (UBA) module 120 to monitor user activity and detect insider threats based on deviations from normal behaviour patterns;
blockchain-enhanced security logging system 122 to securely record intrusion detection events and ensure data integrity using blockchain technology;
smart honeypots for attack diversion 124 to deploy AI-driven honeypots that lure attackers and analyze their tactics without system compromise;
multi-layered defense system 126 to coordinate network security, endpoint protection, and behavioural analytics for a comprehensive defense strategy;
automated compliance monitoring and reporting system 128 to ensure regulatory compliance by continuously monitoring network policies and generating reports;
AI-powered deception module 130 to create realistic fake data and network services for misleading attackers and gathering intelligence;
self-healing security module 132 to apply security patches, reconfigure settings, and restore system integrity after an attack is detected; and
explainable AI (XAI) module 134 to provide interpretable AI-driven insights for detected threats and enhance transparency in security decision-making.

2. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein AI-driven detection engine 106 is configured to analyze network traffic using deep learning models and enabling adaptive anomaly detection and classification of emerging cyber threats with minimal false positives.

3. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein federated learning and adaptive AI module 108 is configured to continuously update and refine intrusion detection models across distributed systems without centralizing sensitive data, ensuring improved threat intelligence sharing while maintaining data privacy.

4. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein real-time packet inspection and deep packet analysis module 116 is configured to perform deep packet inspection (DPI) for analyzing encrypted and obfuscated network traffic, detecting advanced persistent threats, malware signatures, and covert cyberattacks in real time.

5. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein threat correlation and intelligence module 110 is configured to integrate external threat intelligence feeds, cybersecurity reports, and industry-specific attack databases, enabling proactive defense by correlating threat indicators with real-time network activity.

6. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein automated response and mitigation system 112 is configured to autonomously execute security actions, including blocking malicious IP addresses, isolating infected network segments, and triggering predefined countermeasures upon confirmed threat detection.

7. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein user behavior analytics (UBA) module 120 is configured to analyze user activity patterns using AI-driven behavioral profiling to detect insider threats, unauthorized access attempts, and account compromises through real-time anomaly detection.

8. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein self-healing security module 132 is configured to apply automated security patches, reconfigure defense parameters, and restore compromised system components, ensuring continuous adaptation and resilience against evolving cyber threats.

9. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein explainable AI (XAI) module 134 is configured to generate interpretable security insights, providing human-readable justifications for AI-driven threat classifications and response actions, enabling transparent cybersecurity decision-making.

10. The AI-powered intrusion detection system (AI-IDS) 100 as claimed in claim 1, wherein method comprises of
data collection module 102 gathering real-time network traffic, system logs, and endpoint activity for security analysis;
feature extraction and preprocessing unit 104 processing the collected data by filtering noise, normalizing values, and extracting security-relevant features for analysis;
AI-driven detection engine 106 analyzing structured data using deep learning models to classify network activity as normal or potentially malicious;
federated learning and adaptive AI module 108 continuously refining detection models by learning from emerging cyber threats across multiple distributed instances;
threat correlation and intelligence module 110 integrating external threat intelligence feeds, attack databases, and cybersecurity reports to enhance threat detection and prediction;
real-time packet inspection and deep packet analysis module 116 performing deep packet inspection to detect hidden threats, encrypted attacks, and stealth malware signatures within network traffic;
AI-powered phishing and social engineering detection module 118 analysing emails, messages, and URLs using natural language processing (NLP) to detect phishing and social engineering attacks;
user behavior analytics (UBA) module 120 monitoring user activity to detect insider threats and unauthorized access based on deviations from normal behaviour patterns;
blockchain-enhanced security logging system 122 securely recording intrusion detection events and security logs using blockchain technology to ensure data integrity and immutability;
smart honeypots for attack diversion 124 deploying AI-driven honeypots to lure attackers, analyze their tactics, and prevent real system compromise;
multi-layered defense system 126 coordinating network security, endpoint protection, and behavioural analytics to create a comprehensive cybersecurity defense strategy;
automated response and mitigation system 112 executing predefined security actions such as blocking malicious IPs, isolating infected devices, and generating automated security alerts;
self-healing security module 132 automatically applying security patches, reconfiguring settings, and restoring system integrity after an attack is detected;
automated compliance monitoring and reporting system 128 ensuring compliance with cybersecurity regulations by continuously monitoring network policies and generating compliance reports;
AI-powered deception module 130 creating realistic fake data and network services to mislead attackers and gather intelligence on their strategies;
explainable AI (xai) module 134 providing interpretable AI-driven insights on detected threats, helping security administrators understand decision-making processes.