6G Security AI-Powered Autonomous Threat Detection and Dynamic Security System

Abstract

The invention relates to a system and method for continuous verification of network assets, AI-assisted threat detection, dynamic management of security policies, and autonomous response to cyber threats in 6G communication networks, based on Zero-Trust architecture principles. The system operates on the principle of "never trust, always verify." Every interaction between users, devices, network functions, AI agents, and third-party applications is continuously verified. Real-time threat assessment is performed using AI-assisted anomaly detection engines, behavioral analyses, and threat intelligence information, and security policies are dynamically updated.

Description

1 TARIFF 6G Security AI-Powered Autonomous Threat Detection and Dynamic Security System Technical Area 5 The invention is based on Zero-Trust architecture principles in 6G communication networks. This includes continuous verification of network assets, AI-powered threat detection, and security. for the dynamic management of policies and autonomous response to cyber threats It is related to the system and method. State of the Art Current security systems rely on perimeter-based approaches (firewall, DMZ). It is based on; any entity entering the network is considered secure. This approach is advanced and sustainable. It is inadequate against threats (APTs) and insider attacks. Authentication is a one-time process. This is being done; the reliability of the entity is not continuously verified during the session. Security 15 Policies are governed by static rules; they are based on network conditions, threat status, and user experience. It cannot dynamically adapt to their behavior. Threat detection mostly relies on signatures. This is done using signature-based methods; zero-day attacks and unknown threats. It cannot be detected. After a security breach is detected, intervention is done manually. This is being done; this process can take hours or even days. In the multi-layered structure of 6G networks, 20 (RAN, Core, Edge, NTN, MEC) security cannot be managed holistically; each layer is separate. It operates with security policies. AI agents, IoT devices and machine-to-machine (M2M) There are no suitable security mechanisms for new types of entities such as communication. User and anomalies in asset behavior (insider threat, compromised device) in real time. It cannot be detected. Learning from past security incidents and future attacks 25 It lacks the ability to predict vectors. Encryption methods are vulnerable to quantum computer threats. It is insufficient to address this. These shortcomings result in a high risk of security breaches, long response times, high operational costs, data leaks, service disruptions, and customer trust. This leads to losses. Due to the negative factors described above and the current situation... Due to the inadequacy of the solutions on this subject, an improvement is needed in the relevant technical field. 30 It has been made necessary. Purpose of the Invention The invention represents a new breakthrough in this field, unlike the structures used in existing technology. It aims to create a structure with different technical specifications that bring about different results. 35 2 The main goal of the invention is to completely transform security management in 6G communication networks. An AI-powered and autonomous security ecosystem based on Zero-Trust architecture principles. to present. Another aim of the invention is to replace the traditional perimeter-based security approach in 6G networks. to eliminate, continuously protect multi-layered and distributed network assets, zero day 5 Providing proactive defense against zero-day attacks, improving security breach response time. The goal is to minimize and continuously improve security decisions through an algorithmic structure that learns from its decisions. Another objective of the invention is to enable the system to identify which entity is being targeted by an autonomous threat response engine. what level of risk it carries, what security policies will be implemented, and suspicious to automatically determine which actions to take in response to activities and 10 The aim is to implement a system that makes security management in 6G communication networks completely autonomous. An AI-powered and learning security ecosystem based on Zero-Trust architecture principles. To provide; a proactive threat-reducing system that can make its own decisions without requiring manual intervention. hunting, detecting zero-day attacks, quantum-safe, and learning from experience. The goal is to create a continuously improving 6G security system. 15 The structural and characteristic features and all the advantages of the invention are given in the figures below. The detailed explanation, written with references to figures, makes it clearer. This will be understood. Therefore, the evaluation should also take these figures and detailed explanations into account. It must be done by taking it. Figures that will help understand the invention. Figure 1 shows the general architecture of the system that is the subject of the invention. Explanation of Part References 1. Continuous identity and trust assessment engine. 2. AI-based threat detection and anomaly analysis module 25 3. Dynamic security policy manager 4. Autonomous threat response orchestrator 5. Learning security optimization engine Detailed Description of the Invention 30 In this detailed description, the preferred configurations of the system that is the subject of the invention are listed only. This will contribute to a better understanding of the subject and will not have any limiting effects. The invention is based on Zero-Trust architecture principles in 6G communication networks. This includes continuous verification of network assets, AI-powered threat detection, and security. 35 for dynamic management of policies and autonomous response to cyber threats It is related to the system and method. 3 The system operates on the principle of "never trust, always verify." Users, devices, network functions, every interaction between AI agents and third-party applications is continuous This is verified by AI-powered anomaly detection engines, behavioral analyses, and threat intelligence. Real-time threat assessments are performed using this information, and security policies are implemented. It is updated dynamically. 5 The goal is to eliminate the traditional perimeter-based security approach in 6G networks, making them much more efficient. Continuously protecting layered and distributed network assets against zero-day attacks. to provide proactive defense against, minimize security breach response time, and The goal is to continuously improve security decisions through an algorithmic structure that learns from those decisions. Thanks to the autonomous threat response engine, the system identifies which asset poses what level of risk. what it carries, what security policies will be implemented, and what measures will be taken against suspicious activities. It automatically determines and implements the actions to be taken. Current security systems rely on perimeter-based approaches (firewall, DMZ); network The incoming asset is considered safe. This approach protects against advanced persistent threats (APTs) and It is inadequate against insider attacks. Authentication is a one-time process; session 15 The reliability of the entity is not continuously verified throughout this period. Security policies are static. It is governed by rules; based on network conditions, threat situation and user behavior. It cannot adapt dynamically. Threat detection is mostly signature-based. These methods are used to carry out zero-day attacks and unknown threats, which go undetected. Once a security breach is detected, the intervention is carried out manually; this process takes 20 hours. It can even take days. In 6G networks, the multi-layered structure (RAN, Core, Edge, NTN, MEC (Mechanical, Economic, and Chemical) security cannot be managed holistically; each layer has separate security policies. It is working on new assets such as AI agents, IoT devices, and machine-to-machine (M2M) communication. There are no suitable security mechanisms for these types of users and assets. Real-time detection of abnormalities in behavior (insider threat, compromised device) 25 It is not possible to learn from past security incidents and anticipate future attack vectors. It lacks predictive capability. Encryption methods are inadequate against quantum computer threats. These shortcomings result in a high risk of security breaches, long response times, and high... operational costs, data leaks, service disruptions, and loss of customer trust This leads to... 30 This invention, Zero-Trust, completely transforms security management in 6G communication networks. It offers an AI-powered and autonomous security ecosystem based on existing architectural principles. Security in the systems is perimeter-based, authentication is one-time, and intervention is reactive. Meanwhile, thanks to this invention, every asset is continuously verified, threats are proactively detected, and The intervention happens automatically within seconds. 35 4 The system includes a Continuous Identity and Trust Assessment Engine (1), AI-Based Threat Detection and Anomaly Analysis Module (2), Dynamic Security Policy Manager (3), Autonomous Threat from the components of Intervention Orchestrator (4) and Learning Security Optimization Engine (5) This integrated structure autonomously monitors the entire security lifecycle in 6G networks. He / She manages. 5 The system not only detects known threats, but also behavioral analyses. It analyzes anomalies, learns threat patterns, and predicts future attack vectors. Continuous Identity and Trust Assessment Engine (1), the basic element of Zero-Trust architecture It applies its principles. Identity Verification Engine verifies the identities of users, devices, and AI agents. It constantly verifies your identity. Multi-Factor Authentication Manager uses biometric, behavioral, and 10 It combines cryptographic factors. The Trust Score Calculator provides a dynamic trust score for each asset. The calculations; this score is based on the asset's past behavior, current context, and risk indicators. It is constantly updated. Context-aware Access Control makes access decisions based on the real-time context. It gives. AI-Based Threat Detection and Anomaly Analysis Module (2) analyzes all activities on the network in real time. It analyzes real-time trends. The Behavioral Analytics Engine analyzes normal behavior patterns in users and entities. Network Traffic Analyzer detects off-patterns using machine learning models. It identifies suspicious activities (DDoS, port scanning, lateral movement) in traffic. Threat Intelligence Aggregator, from global threat intelligence sources (MITRE ATT&CK, CVE) It collects data and compares it to current threats. The Zero-day Detection Engine, previously developed in 20 countries... It detects previously unseen attack vectors through behavioral analysis and machine learning. Dynamic Security Policy Manager (3) adapts security policies to network conditions and threats. Policy Engine dynamically updates security rules based on risk level. Adaptive Access Control configures access permissions according to the real-time threat situation. It adjusts accordingly. Micro-segmentation Manager divides the network into small security zones horizontally 25 It prevents lateral movement. The Encryption Policy Controller controls data encryption levels. It determines sensitivity and manages quantum-safe algorithms. The Autonomous Threat Response Orchestrator (4) takes automatic action against detected threats. Automated Incident Response implements the appropriate response plan based on the type of threat. The Containment Engine isolates affected entities and prevents the threat from spreading. Attack 30 Mitigation Module takes actions such as DDoS protection, traffic filtering, and malicious IP blocking. It performs this function. The Forensic Data Collector collects and stores all evidence for post-incident analysis. SOAR (Security Orchestration, Automation and Response) integration with playbook-based system. It provides automatic intervention. The Learning Security Optimization Engine (5) learns from past security events. Historical 35 Incident Analytics analyzes successful and unsuccessful threat responses. Attack Pattern Recognition detects repetitive attack tactics and integrates them into the MITRE ATT&CK framework. peers. Predictive Threat Modeling predicts potential future attacks. Knowledge Base, It stores learned threat profiles and response strategies. Reinforcement Learning The algorithm continuously improves decision quality by receiving feedback from each security event. Unlike existing techniques, this invention is fully autonomous, based on Zero-Trust principles. And it offers a learning structure. Security breach response time is reduced from hours to seconds. Zero Daily attacks are detected through behavioral analysis. Insider threats are constantly emerging. It is detected through verification. Quantum-secure encryption prepares it for future threats. Each A security incident increases the system's intelligence. Thanks to its modular structure, it can be used in 5G, 6G, NTN, private networks and IoT infrastructures with the same methodology. It is feasible. The API-based integration structure is compatible with existing SIEM (Security Information and Event Management) systems. Easily integrated into SOAR (Endpoint Detection and Response) and EDR (Endpoint Management) systems. It adapts. It provides operational transparency with a visual and analytical security management panel. SOC (Security Operations Center) Operations Center teams assess the threat situation, actions taken, risk scores, and security 15 You can monitor your metrics in real time. Telecommunications operators, corporate customers, critical infrastructure operators (energy, finance, healthcare), a large market for the defense industry, government agencies and cybersecurity service providers It has potential. The Continuous Identity and Trust Assessment Engine (1) is based on the Zero-Trust architecture's "never trust, 20 By applying the "always verify" principle, all entities on the network (user, device, AI agent, The application continuously verifies its identity. The Identity Verification Engine uses cryptographic methods. It performs identity verification using certificates, biometric data, and behavioral factors. Multi- Factor Authentication Manager combines multiple authentication factors. Trust Score The Calculator calculates a dynamic confidence score between 0 and 100 for each asset; this score is based on the asset's past 25 years. Its behavior is continuously updated according to the current context and risk indicators. Context-aware Access Control makes access decisions based on real-time context (location, time, device, network status). It provides. The Continuous Verification Loop repeats the verification process throughout the session. AI-Based Threat Detection and Anomaly Analysis Module (2) analyzes all activities on the network in real time. By analyzing data in real time, it detects threats and anomalies. Behavioral Analytics Engine, 30 Detecting abnormal patterns in user and entity behavior using machine learning models. It detects (LSTM, autoencoder, isolation forest). Network Traffic Analyzer analyzes network traffic. Identifies suspicious activities (DDoS, port scanning, lateral movement, data exfiltration). Threat Intelligence Aggregator draws on global threat intelligence from sources such as MITRE ATT&CK, CVE, STIX / TAXII) collects data. Zero-day Detection Engine detects previously unseen attacks. 6 It captures vectors through behavioral analysis and unsupervised learning. UEBA (User and Entity The Behavior Analytics module detects insider threats. Dynamic Security Policy Manager (3) adapts security policies to network conditions and threats. Policy Engine dynamically updates and applies policies based on the situation. It's a security tool. It configures and deploys its rules in real-time based on risk level. Adaptive Access 5 Control adjusts access permissions according to the current threat situation; if a high risk is detected, it adjusts them accordingly. Access to resources is restricted. Micro-segmentation Manager divides the network into small security zones. It prevents lateral movement by separating segments. Encryption Policy Controller It determines data encryption levels according to data sensitivity and is quantum-secure (post-quantum). It manages algorithms (cryptography). Compliance Manager complies with regulations such as GDPR and KVKK. It ensures harmony. The Autonomous Threat Response Orchestrator (4) takes automatic action against detected threats and It coordinates the intervention process. Automated Incident Response (AIS) prepares responses based on the type of threat. It runs the defined playbooks. The Threat Containment Engine identifies the affected entities (compromised entities). It quickly isolates the user, infected device, and malicious IP and prevents the threat from spreading. Attack 15 Mitigation Module, DDoS protection, traffic filtering, malicious IP blocking, session It performs actions such as termination. Forensic Data Collector is used for post-event analysis. It collects all evidence (logs, traffic capture, memory dump) and stores it as an immutable record. It stores. The SOAR Integration Layer integrates with existing security tools (SIEM, EDR, XDR). It works. Recovery Manager restores affected systems to a safe state. 20 The Learning Security Optimization Engine (5) learns from past security incidents and The goal is to continuously improve the system to protect against future threats. Historical Incident Analytics is successful. and analyzes failed threat responses; determining which actions are effective under which conditions. Attack Pattern Recognition assesses that it is a repetitive attack tactic or technique. and identifies its procedures (TTP) and maps them to the MITRE ATT&CK framework. Predictive 25 Threat modeling predicts potential future attacks based on current trends and intelligence. It anticipates threats. The Knowledge Base stores learned threat profiles and best response strategies. The Reinforcement Learning Module uses reward / punishment signals from each safety incident to guide decision-making. It improves the quality. The Model Retraining Scheduler periodically retrains AI models with new data. retrains. 30 The system provides access to all entities on the network (user, device, AI agent, application, network function). It constantly monitors their requests and activities. Continuous Identity and Trust Assessment Engine (1), Identity Verification Engine checks cryptographic certificates and biometric data with each access request. It performs authentication using behavioral factors. Multi-Factor Authentication Manager combines multiple validation factors. The Trust Score Calculator provides 35 values ​​for each entity, ranging from 0 to 100. It calculates a dynamic trust score based on the asset's past behavior, device health, 7 It is constantly updated based on location, time, and the current threat situation. Context-aware Access Control makes access decisions based on the immediate context. It applies to assets with low trust scores. Access is restricted or additional verification is required. The system analyzes all activity on the network in real time. AI-based threat detection and... Anomaly Analysis Module (2), Behavioral Analytics Engine with user and entity 5 abnormal patterns in their behavior can be identified using machine learning models (LSTM, autoencoder, Network Traffic Analyzer detects suspicious activity in network traffic (isolation forest). (abnormal data transfer, port scanning, lateral movement, command-and-control traffic) It defines global threats such as Threat Intelligence Aggregator, MITRE ATT&CK, CVE, and STIX / TAXII. It gathers data from threat intelligence sources and compares it to current activities. Zero-day 10 Detection Engine detects previously unseen attack vectors through behavioral analysis and It detects insider threats using unsupervised learning. The UEBA module detects insider threats. It detects them. A risk score is generated for each threat. Data from the Continuous Identity Engine (1) and the Threat Detection Module (2) are used in Dynamic Security. It is evaluated by the Policy Manager (3). Policy Engine, according to the current risk level, 15 It reconfigures security rules. Adaptive Access Control adjusts access permissions based on real-time threats. Settings are adjusted according to the situation; for example, a user identified as high risk may not have access to sensitive data. It is automatically restricted. Micro-segmentation Manager divides the network into small security zones. It prevents horizontal movement; the segment where the threat is detected is isolated from the others. Encryption Policy Controller determines encryption levels based on data sensitivity and sets quantum-secure 20 Activates algorithms (CRYSTALS-Kyber, CRYSTALS-Dilithium). New policies apply to the entire network. Their assets are distributed in real time. When a critical threat or security breach is detected, the Autonomous Threat Response Orchestra (4) initiates an automatic response. Automated Incident Response pre-determines the type of threat. It runs defined playbooks; for example, traffic filtering for DDoS attacks, ransomware 25 Device isolation and session termination playbooks are activated to prevent data leakage. Threat Containment Engine detects affected entities (compromised user, infected device, malicious IP) It isolates attacks within seconds. The Attack Mitigation Module provides network-level protection (firewall). (rules, malicious IP blocking, traffic shaping). Forensic Data Collector, post-incident analysis. It collects all evidence (logs, packet captures, memory dumps) and creates an immutable 30 It stores information using blockchain-based storage. Once the intervention process is complete, the system restores the affected assets to a safe state. It brings. Recovery Manager within Autonomous Threat Response Orchestrator (4), isolated It cleans up the assets and returns to normal operation. Continuous Identity Engine (1), affected reassesses the trust scores of assets; stricter 35 for these assets after the threat. Verification is applied. AI Based Threat Detection Module (2), new observed during the event 8 It records attack patterns. Dynamic Security Policy Manager (3) records similar attacks. It implements policy updates to prevent future incidents. SOC teams provide detailed information about critical events. They are informed through reports; routine threats are managed entirely autonomously. The system performs continuous monitoring and proactive threat hunting. AI-based threat. The Detection Module (2) continuously analyzes all activities on the network even if there is no active attack and 5 It searches for suspicious patterns. Threat Intelligence Aggregator identifies newly discovered threats. It integrates intelligence about disclosed CVEs (zero-day vulnerabilities) into the system. Dynamic Security Policy Manager (3), proactive policy updates based on this intelligence. It does this; for example, when a new CVE is released, it adds extra protection to affected systems until a patch is applied. Protective measures are put into effect. Predictive Threat Modeling, based on current trends, 10 It anticipates future attacks. The system strengthens its defenses before an attack occurs. After each security incident, all process data is processed by the Learning Security Optimization Engine (5) Historical Incident Analytics analyzes the event based on its success criteria. Detection time (MTTD), response time (MTTR), isolation success, false positive / negative rates. Attack Pattern Recognition (TTP) 15 attack tactics, techniques, and procedures. It detects and maps to the MITRE ATT&CK framework. The Knowledge Base is updated; new threats are identified. Profiles and effective intervention strategies are added. Reinforcement Learning Module, successful. It rewards interventions and punishes wrong decisions. Predictive Threat Modeling. It predicts future attack scenarios. Model Retraining Scheduler retrains AI models. It periodically retrains with new data. The feedback loop repeats all elements (1, 2, 3, 4) 20 It continuously improves; the system is faster, more accurate, and more effective in the face of the next similar threat. He intervenes. This invention, Zero-, makes security management in 6G communication networks completely autonomous. A security ecosystem based on trust architecture principles, powered by AI and capable of learning. It operates based on 25. The operating principle begins with the continuous monitoring and verification of all assets on the network. Continuous The Identity and Trust Assessment Engine (1) assesses the identity of each entity using cryptographic, biometric and It verifies behavioral factors and calculates dynamic trust scores. "Never trust, always..." The "time verify" principle is applied. Simultaneously, the AI-Based Threat Detection and Anomaly Analysis Module (2) detects all 30 on the network. It analyzes activities. The Behavioral Analytics Engine detects behavioral anomalies, Network Traffic Analyzer detects suspicious traffic, Threat Intelligence Aggregator globally. It integrates intelligence and the Zero-day Detection Engine detects unknown threats. Detected threats and security scores are determined by the Dynamic Security Policy Manager (3). Policy Engine, Adaptive Access Control, Micro-segmentation Manager and 35 are evaluated. 9 The Encryption Policy Controller updates and enforces security policies based on current conditions. Quantum-secure encryption is activated. When critical threats are detected, the Autonomous Threat Response Orchestrator (4) takes automatic action. It runs Automated Incident Response playbooks, Threat Containment Engine. It isolates the affected entities, the Attack Mitigation Module stops the attack, and Forensic Data 5 The collector gathers the evidence. Data collected throughout this process is used by the Learning Security Optimization Engine (5) It is analyzed by Historical Incident Analytics, Attack Pattern Recognition, and Predictive Analytics. System security is determined using Threat Modeling and Reinforcement Learning algorithms. The decisions are constantly being improved. 10 Thanks to the feedback loop, the system learns over time. The system goes through verification → detection → A fully autonomous security system operating in a policy → intervention → learning cycle. It transforms into its structure. In short, it's a proactive threat that can make its own decisions without requiring manual intervention. hunting, detecting zero-day attacks, quantum-safe, and learning from experience 15 A constantly improving 6G security system emerges.

Claims

REQUESTS 1.6G communication networks are based on Zero-Trust architecture principles. continuous verification of assets, AI-assisted threat detection, security policies It is a system for dynamic management and autonomous response to cyber threats, Feature; 5 • Access to all entities on the network (user, device, AI agent, application, network function) Continuously monitoring their requests and activities, cryptographic certificates with every access request, Multiple identity verification systems that use biometric data and behavioral factors It combines validation factors and calculates a dynamic confidence score between 0 and 100 for each asset. Access decisions are made based on the immediate context, and access to assets with a low confidence score is restricted. Continuous identity and trust assessment engine that restricts or requires additional verification (1), • By analyzing all activity on the network in real time, it detects threats and anomalies. Detecting abnormal patterns in user and entity behavior using machine learning models. detects and identifies suspicious activity in network traffic, global threat intelligence. By gathering data from various sources, behavioral analysis of previously unseen attack vectors, and 15 Capturing threats from within using unsupervised learning, and identifying threats for each individual threat. AI-based threat detection and anomaly analysis module that generates a risk score (2), • dynamically updating security policies based on network conditions and threat situations, implementing, configuring security rules according to risk level and deploying them in real time, By adjusting access permissions according to the current threat situation and dividing the network into small security zones, 20 It prevents horizontal movement, determines data encryption levels according to data sensitivity, and Managing quantum-safe algorithms, ensuring compliance with regulations such as GDPR and KVKK. dynamic security policy manager (3), • takes automated action on detected threats and coordinates the response process, threat It runs predefined playbooks based on the type, quickly isolates affected entities, and 25 Preventing the spread of threats, providing network-level protection, and for post-incident analysis. It collects and stores all evidence immutably, integrated with existing security tools. autonomous threat response that restores affected systems to a secure state. orchestrator (4), • Learning from past security incidents and continuously updating the system against future threats 30 improving, analyzing successful and unsuccessful threat responses, repeated attacks Identifying their tactics, techniques and procedures and incorporating them into the MITRE ATT&CK framework mapping, predicting potential future attacks based on current trends and intelligence, It stores learned threat profiles and best response strategies from every security incident. AI models that improve decision quality through reward / punishment signals and are periodically updated with new data. 35 retraining learning security optimization engine (5) 11 It includes.

2. It is a system that complies with Request 1 and its feature is; autonomous threat response orchestrator (4), It includes a recovery manager that restores affected systems to a safe state.

3. It is a system that complies with Request 1 and its feature is; within the learning security optimization engine (5), Model retraining scheduler 5 periodically retrains AI models with new data. It includes.

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