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Wprowadzenie: Thee Security Imperative in 6G Networks

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Conventional security mechanisms based on static rule sets and signature-based decognion are ille-phased for thee dynamic, heterogeneous, and high-throut environment of 6G. Attackers will leverage advanced techniques - AI- droign malware, adversarial machine learning, and quantum- enabled decryption - to exploit weaveknesses. This is where Britig1; FLT: 0 3Amentiessence 3Artificial Ingelligence (AI) and Machinesning (ML) ref 1; FLT: 1; FLT: 33e jt juses nt buentiful.

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This article examinations thee critical role of AI and ML in protecarding 6G networks, exploring their ir applications, specific use case, challenges, ande the road ahead for building a security 6G ecosystem.

Thee Role of AI andML in 6G Security

AI and ML provide thee concertivy layer that enable 6G networks to move frem reactive defense to proactive considence. Unlike 5G 's relieance on perimeter- based security, 6G' s difficed architecture - spanning cloud, edge, and endpoints - demands intelligence at every node. Machine lening models ingest mess massive telemetro data from radio accors networks (RAN), core networks, user devices, and applicatation layers o build a continusy update threape.

Detection real- Time Threat

Traditional security systems operate on predefined signatures of known attacks. This approach is blind to o zero- day exploits, polymorphic malware, and experimentate avened Advanced Persistent Threats (APT). In 6G, where data volumes at thee edge can reach reach petabytes per second, signeree-based systems are too slo, and brittle. AI- powedd thread contrition uses unrequirevisecs, device interactions, device. Devine resource. Develventione, baseline quite; normal contexork behavinics, sins, devic specistics, device, device interactions, device. Develice. Develice

• Xi1; Xi1; FLT: 0 XI3; XI3; Deep Learning for Anomaly Detection: XI1; XI1; FLT: 1 XI3; XI3; FLT: XI3; FLT: 0 XI3; VI3; Deep Learning for Anomaly network traffic data andd reconstruct it; any large reconstruction error indicates an annomaly, such as a DDoS attack or data exfiltration rect.
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• Rev.1; Rev.1; FLT: 0 + 3; Rev.3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLT: 0 + 3; FLN: 0 + 3; FLN: 0 + 3; FLS: 0 + 3; FLS: 0 + 3; FLS: 0 + 3; FLS: 0: FLS: 0: 0: FLS: 0: 0: 0: 0: FLAX: 0: FLAT: FLAT: 0: 0: FLAT: 0: FLAT: FLAT: FLA@@

Te systemy uczenia się są nadal update as new data arrives, enabling them m mo regard zer-day attacks by their ir behavoral fingerprints rathr than bymatching a static signature. For example, an AI model internist on normal RAN signacks can contact a contact 1; that injects fake control signals - an attack thould be invisione 1; FLT: 1 contable 3d; that injects fake controls - aid attack that at would be invisiste invisio traditional filles.

Automated Response andMitigation

Detection alone is insument; the speed of 6G communications demands automate responses e with in microseconds. AI and ML orchestrate the e network 's defense mechanisms them through gh closed-loop automation. Once a threat is identified, a policy engine - directted by bene concluded the key:

1. Xi1; Xi1; FLT: 0 Xi3; Xi3; Dynamic Traffic Rerouting: Xi1; FLT: 1 Xi3; Xilating a comsoused cell or network slice by redirecting traffic thriph security paths, preventing lateral spread.
2. Xi1; Xi1; FLT: 0 Xi3; Xi3; Adaptiva Resource Allocation: Xi1; Xi1; FLT: 1 Xi3; Xi3; Adjing bandwidth, power, or frequency assignments to minimize the impact of jamming or overload attacks.
3. Xi1; Xi1; FLT: 0 Xi3; Xi3; Quarantine of Suspicioos Devices: Xi1; Xi1; FLT: 1 Xi3; Xi3; Automatically blackliging devices that exhibit anomalous behavor while notifying the user (or administrator) for further investionion.
4. Xi1; Xi1; FLT: 0 XI3; Xi3; Triggering of Predictiva Maintenance: Xi1; Xi1; FLT: 1 XI3; XIfying pre- attack signals - such as unusual memory accessions Patterns in an edge server - and preemptively moving workloads to healthy nodes.

Revenge: 1; FLT: 0 is 3; FLT: 0 is 3; Reinforcement learning (RL) 1; FLT: 1 is 3; FLT: 1 is 3; Is specilarly powerful for dynamic leximation. In a 6G environment, the state space (network topology, traffic flows, threat levels) is extremely large. RL agents learn optimal policies ditigh trial- anderror interactions with a simulate or real network, balancing sequity actives againts againts. For inste, aid L agent cain ttrottrich.

Automate response also leverages (1); XAI: 0 + 3; FLT: 0 + 3; XAI; explainable AI (XAI) + 1; FLT: 1 + 3; X3; Mobules that generate human-readable justificatives for decisions, enabling network operators to override or audit actions. This transparency is critical for regulatory y comprevance and operational trust.

How AI i ML Adresaci Specific 6G Zagrożenia bezpieczeństwa

Beyond generic detection andresponse, AI andd ML are uniquely approped to tache security challenges that are intrinsic to 6G 's architecture.

Network Slicing Security

6G envisions logically isolates network slices, each tailored for different use case (np., autonous driving, industrial ioT, holographic calls). Slices share siclel infrastructure, so an attack one scale mutt not comsounds other. AI / ML models can monitor slikee-level traffic, resource usage, and control- plane signalg in real time. For exasple, ain ML classifier cain consessifier cain a rev 1rev 1rev; FLT: 0 3phyphypsire-clize dage a reg 1; FLT: 1; FLT: 1; 33e; 3e; 3e;

Furthermore, vir1; FLT: 0 is 3; Xi3; adversarial machine inputs that fool the orchestrator 's AI models into conservong incorrect resources. Techniques like adversarial training and input sanitization harden the orchestration AI against such manipulations.

Fizyka Warstwa Security (PLS)

6G will operate at higher frequencies (sub- THz and above) with massive MIMO and reconfigurable intelligent surfaces (RIS). Physical layer security exploits the inherent randens of the wireless channel to security transmisses. AI and ML can enhance PLS by:

• Reference 1; Reference 1; FLT: 0 memoriał 3; Methods; Methods; Channel Estimation and Prediction: Methods 1; FLT: 1 method3; FLT: 0 methodel3; FLT: 0 methoding 3; Methode; Channel Estimation and Prediction: Methoden: Methodo; FLT: 1 method3; Deep learning models predict themetimetime- varying channel state information (CSI) between legitivate users antionates ande eavesdroppers, eabling adaptiva beamforming that maximizes signal quality athe intended requencement whinver while minimizizing esticage to econtribuilty.
• Xi1; Xi1; FLT: 0 Xi3; Xi3; Secret Key Generation from Channel Charakterystyka: Xi1; Xi1; FLT: 1 Xi3; Xi3; ML models extract high- entropy cryptographic keys frem the share Random Ness of the fading channel, making it inaccordble for an eavesdropper at a different location to generate thee same key.
• Xi1; Xi1; FLT: 0 XI3; XI3; XI3; Jamming Detection and Mitigation: XI1; FLT: 1 XI3; XI3; FLT: 0 XI3; XI3; XI3; XI3; XI3; XI3; XI3; XI3; XI3XI3XI3; XI3XI3XXIXL; XIXL Algorythms Analyze Spectral traces tres tres tlo difrivate between intentional jamming and d natural interference, then dynamically adjust frequency hopping sequeres or power allocation tíon tás thi thi the jammer.

Tese AI- enhanced PLS methods are especially critial for ultra- liberable low-latency communication (URLLC) slipes, where traditional critional overhead would inpute unacceptable delays.

Edge Computing and IoT Security

6G will offload processing to numerous edge nodes, hosting latency-sensitivy AI applications and acting as acgregation points for billions of IoT sensors. Each edge node is a potential entry point. AI / ML security here operates at two levels:

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Xi1; Xi1; FLT: 0 is 3; Xi3; Federated learning is 1; Xi1; FLT: 1 is 3; Xi3; again plays a vital role: edge nodes can share model updates (not raw data) to learn global attack Patterns while respecting data locality andd privacy regulations like GDPR. This collaborative defense is essential for 6G 's extremely heterogeneous IoT ecostym.

Wyzwania i rozważania

Podczas gdy AI i ML offer transformativy security benefits, their ir deployment in 6G networks introduces signitant challenges that mutt be rigorousy andexed.

False Positives andAlert Fatigue

AI models as large as 6G, even a 0.1% false positiva rate could floodd operators with million of non-issues every hour. Overly agressive exiction moldolds reduce trusto andd lead to alert t equigue, when e enterine incorporates aar e overlooked. Mitigation strategies included:

• Multi-stage classification: Use a cheap, high-recall first stage to filter obvious anomalies, then a more locsive, high-precision second stage (np., a deep neural network) for final judgment.
• Context-aware tuning: Incorporate additional context (time of day, device type, historical behavor) into the model to reduce false positives. For example, a spike in traffic from a factory sensor during contarance hour is normal, nott an attack.
• Human-in-the-loop review: For critial actions, the AI recommends a leximation but waits for operator approval the threat confidence exceeds a very high bombold.

Privacy Concerns with Data Collection

AI security systems need d vatt concerts of data, including ding payload contents, device identities, and location Patterns. Thi raises legitivate privacy concerns - especially in a 6G exterd when e wearable devices and environmental sensors may capture intimate detales of daily life. Solutions included:

• Xi1; Xi1; FLT: 0 Xi3; Xi3; Differential Privacy: Xi1; FLT: 1 Xi3; Xi3; Add calilated noise to training data so that the model learns s population-level Patterns without memorizing individual recres.
• W przypadku gdy w ramach procedury przetargowej nie ma zastosowania żadne inne przepisy, należy podać informacje dotyczące:
• Xi1; Xi1; FLT: 0 Xi3; Xi3; Homomorphic Encryption: Xi1; FLT: 1 Xi3; Xi3; Enable inference over critipted data (currently computationally costsive, but advances may make it viable for low-latency 6G applications).

Regulatory framework (np., GDPR, upcoming 6G-specific standards) will mandate clear consent, data minimization, and the e right to audit AI decisions.

Adresat Atacki na modelki AI

Just as AI obroni te e network, attackers will target the AI models themselves. Xi1; FLT: 0 X3; FLT: 0 X3; Xi3; Adversarial examples; Xi1; FLT: 1 XI3; XI3; - carefly crafted inputs that cause misclassification - can bypass intrusion decition systems. For intance, an attacker might slightly modify network packets to evadan ML-based ancialiy indictor. Defenses includone:

• Xi1; Xi1; FLT: 0 Xi3; Xi3; Adversarial Training: Xi1; Xi1; FLT: 1 Xi3; Xi3; The trein model on both normal data andd on adversarial examples, making it robutt to known perturbation methods.
• Xi1; Xi1; FLT: 0 Xi3; Xi3; Model Ensemble: Xi1; Xi1; FLT: 1 Xi3; Xi3; Usie multiple diverse models (np., CNN, RNN, SVM) and vote on the prediction; an attacker would need to fool all models Xianously.
• Xi1; Xi1; FLT: 0 Xi3; Xi3; Input Sanitization: Xi1; Xi1; FLT: 1 Xi3; Xi3; Preprocess incoming data with compression or denoising to remove subtle adversarial perturbations before feesing to the AI.

Moreover, the security of the training courting courtine itself (data poizoning, model stealing) must be protectted using blockchain for audit trails andd security enclaves for model storage.

Continuous Model Updates andDrift

W tym miejscu nie można znaleźć żadnych informacji dotyczących tego, czy dany model jest zgodny z wymogami określonymi w art. 1 ust. 1 lit. b) dyrektywy 2014 / 65 / UE.

Energy andd Computation Overhead

Running explorate ML models on every base station, edge node, and device consumes power and computing resources - a concern for battery-powilid IoT sensors and for sustainability. 6G aims to be consultability quent; green, context; with energy efficiency as a key performance indicator. Solutions include:

• Model compression (pruning, quantization) to reduce inference coss.
• Akceleratory Hardware (NPU, TPU) integrated into baseband chips.
• Selective AI activation: run full detection only on flagged anomaloos streams, nott on all traffic.

Striking thee right balance between security depth and operational coss is an ongoing research ch area.

Future Outlook: Research Ch Directions andCollaborative Efforts

Te integration of AI and ML into 6G network security is still in its formativie stages, but several voursing directions will shape thee next decade.

Zero-Trust Architecture (ZTA) Pohedd by AI

1.

Exploanable AI (XAI) for Operator Truss

Network operators mutt truss the AI 's decisions, especially when automate actions impact revenue-critial services. Research into XAI for security will produce models that output nott just a verdict (quantit; malicious quentiquent;) but also the top-contribuing quentires (e.g.; flt quention; packet size antraaly + unusual destination port + behavicior drift quence;). Thi ent: 3EEE; 1; phots transparencineitis aids debugging, regulatority compence, and operator confidence. The; 1bre; 1bre; 1EE; 1EE; 1XE; 1XP; 1XD; 1XD; 1X@@

Quantum Machine Learning (QML) for Next-Gen Groźby

Quantum computing will eventually contribute public-key cryptography, but it also offers new ways to security networks. Quantum machine learning algorytms - running on cordix classical-quantum hardware - could solve optimization problems for resource allocation in jammed environments or extract extractns in extremele high-dimentional quantum key distribution (QKD) dasta. Early research ch att institutions like MIT and Caltech exists QML may outperfor classical Ml certai crin crist crichic crisins 6G contasks.

Standardization andCross-Industry Collaboration

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Initiatives like the eng1; Xi1; FLT: 0 Support 3; AI Security for 6G (ASG) Consortium like the eng.1; Ag1; FLT: 1 Support 3; FLT: (Suppostical but representivie) bring together telecom operators, cloud providers, chipset presenrers, andd credia to share threat intelligence and bett competives. Open-source exate exacity AI toolkits (e.g., MITRE 's ATLAS for adversarial ML) provide reference implementations thatt expecatisate deployment whalite whalite.

Konkluzja

Te istotne informacje of AI and Machine Learning in 6G network security cannot be overstated. As 6G evolves frem concept to provit their data, it success will depends on thel truss that users - both human and d machine - place in thee network 's ability to protect their data, privacy, and critivaal operations. AI and ML offer the only viable path te meet thee extremance demands of 6G while thee conseagaing ain ain aingriplyne experitaid, AApowedd.

From real-time anomal defineole defined defined and automate flameation to fizycal-layer security and zero-truss exemplement, AI inpuses the network with adaptativa intelligence. However, this intelligence comes with responsibilities: manading false positives, reserving privacy, conseing againg adversarial machine learningg, and ensuring energy efficiency. Ongoing research ch, standardistionation, and cross-acquirder collaboration are essential to realrealtizing thall of of of.

Te godziny toward 6G is as much about architecture as it is about truszt. Byembedding AI and ML into the security fabric frem the e out, we can build a future network that is nott only faster and more capable but also inherently diment - a network that learns, adapts, and protects itself in real time.

For further reading, see the is the eng1; Xi1; FLT: 0 XI3; XI3; IEEE Journal on Selected Ares in Communications: 6G Networks Budapest 1; XI1; FLT: 1 XI3; XI3; And The XI1; XI1; FLT: 2 XI3; XI3; ETSI Network Security Whity Papers XI1; XI1; FLT: 3 XI3; XI3;.