Dyadic-Chaotic Lifting S-Boxes for Enhanced Physical-Layer Security within 6G Networks

• URL: http://arxiv.org/abs/2511.12325v1
• Date: Sat, 15 Nov 2025 19:00:29 GMT
• Title: Dyadic-Chaotic Lifting S-Boxes for Enhanced Physical-Layer Security within 6G Networks
• Abstract summary: Sixth-Generation (6G) wireless networks will interconnect billions of resource-constrained devices and time-critical services.<n>Physical-Layer Security (PLS) is pivotal to deliver lightweight, information-theoretic protection.<n>We introduce the first-ever chaos-lifted substitution box (S-box) for PLS.
• Score: 1.5469452301122173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sixth-Generation (6G) wireless networks will interconnect billions of resource-constrained devices and time-critical services, where classical, fixed, and heavy cryptography strains latency and energy budgets and struggles against large-scale, pre-computation attacks. Physical-Layer Security (PLS) is therefore pivotal to deliver lightweight, information-theoretic protection, but still requires strong, reconfigurable confusion components that can be diversified per slice, session, or device to blunt large-scale precomputation and side-channel attacks. In order to address the above requirement, we introduce the first-ever chaos-lifted substitution box (S-box) for PLS that couples a $β$-transformation-driven dynamical system with dyadic conditional sampling to generate time-varying, seedable 8-bit permutations on demand. This construction preserves uniformity via ergodicity, yields full 8-bit bijections, and supports on-the-fly diversification across sessions. The resulting S-box attains optimal algebraic degree 7 on every output bit and high average nonlinearity 102.5 (85% of the 8-bit bound), strengthening resistance to algebraic and linear cryptanalysis. Differential and linear profiling report max DDT entry 10 (probability 0.039) and max linear probability 0.648, motivating deployment within a multi-round cipher with a strong diffusion layer, where the security-to-efficiency trade-off is compelling. Our proposed reconfigurable, lightweight S-box directly fulfills key PLS requirements of 6G networks by delivering fast, hardware-amenable confusion components with built-in agility against evolving threats.

Related papers

• Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G [90.42669277637026]
  Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence.<n>This article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks.
  arXiv  Detail & Related papers  (2026-09-09T01:36:44Z)
• AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation [13.344675738769395]
  Cyber-physical systems will run over ultra-reliable low-latency slices in 6G networks.<n>This survey reframes 6G CPS security as a closed-loop, AI-native pipeline.<n>We formalise a per-slice, tail-bounded latency contract on the sense, detect, mitigate stages.
  arXiv  Detail & Related papers  (2026-06-06T13:36:59Z)
• Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks [49.19977685617281]
  Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments.<n>We propose a RIS-enabled Open-RAN framework for integrated terrestrial/non-terrestrial (TN/NTN) industrial 6G networks.<n>We show improvements of up to 75% in data rate, 25% latency reduction, and 16% energy savings compared with state-of-the-art learning-based and non-RIS baselines.
  arXiv  Detail & Related papers  (2026-05-31T16:18:25Z)
• LAAF: Logic-layer Automated Attack Framework A Systematic Red-Teaming Methodology for LPCI Vulnerabilities in Agentic Large Language Model Systems [0.39875976220956705]
  LAAF is the first automated red-teaming framework to combine an LPCI-specific technique taxonomy with stage-sequential seed escalation.<n>We show that LAAF achieves higher stage-breakthrough efficiency than single-technique random testing.
  arXiv  Detail & Related papers  (2026-03-18T00:51:36Z)
• Spectral Gating Networks [65.9496901693099]
  We introduce Spectral Gating Networks (SGN) to introduce frequency-rich expressivity in feed-forward networks.<n>SGN augments a standard activation pathway with a compact spectral pathway and learnable gates that allow the model to start from a stable base behavior.<n>It consistently improves accuracy-efficiency trade-offs under comparable computational budgets.
  arXiv  Detail & Related papers  (2026-02-07T20:00:49Z)
• Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping [61.459927600301654]
  Multi-condition control is bottlenecked by the conventional concatenate-and-attend'' strategy.<n>Our analysis reveals that much of this cross-modal interaction is spatially or semantically redundant.<n>We propose Position-aligned and Keyword-scoped Attention (PKA), a highly efficient framework designed to eliminate these redundancies.
  arXiv  Detail & Related papers  (2026-02-06T16:39:10Z)
• Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization [4.154839547291294]
  We propose an adaptive post-quantum cryptography (PQC) framework that predicts short-term mobility and channel variations.<n>We show that the proposed framework reduces end-to-end latency by up to 27%, lowers communication overhead by up to 65%, and effectively stabilizes cryptographic switching behavior using reinforcement learning.
  arXiv  Detail & Related papers  (2026-02-01T17:11:43Z)
• AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers [69.56534335936534]
  AmbShield is an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers'<n>In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices.
  arXiv  Detail & Related papers  (2026-01-14T20:56:50Z)
• Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks [58.70163955407538]
  Malicious eavesdroppers pose a serious threat to private information via satellite-terrestrial networks (STNs)<n>We propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing.<n>We exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer.
  arXiv  Detail & Related papers  (2026-01-06T10:30:41Z)
• Cross-Layer Isochronous Diffusion Protocol (CIDP): A Rigorous Information-Theoretic and Control-Theoretic Framework for Sovereign Tactical Anonymity [0.0]
  Next-generation tactical networks face a critical Anonymity Trilemma.<n>It is impossible to simultaneously achieve strong anonymity, low latency (isochrony), and low bandwidth overhead under a global adversary.<n> CIDP breaks this deadlock by injecting physical-layer entropy via rapid antenna sidelobe modulation.
  arXiv  Detail & Related papers  (2025-12-09T16:55:00Z)
• Hybrid SIFT-SNN for Efficient Anomaly Detection of Traffic Flow-Control Infrastructure [0.30586855806896046]
  The SIFT-SNN framework is a low-latency neuromorphic signal-processing pipeline for real-time detection of structural anomalies in transport infrastructure.<n>The presented system achieves a classification accuracy of 92.3% with a per-frame inference time of 9.5 ms.<n>The framework is validated through a working prototype deployed on a consumer-grade system.
  arXiv  Detail & Related papers  (2025-11-26T12:40:18Z)
• Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway [5.541753997410371]
  This paper presents an edge-centric Intrusion Detection System (IDS) framework that integrates lightweight machine learning (ML) based IDS models with pre-trained large language models (LLMs)<n>The system evaluates six ML-based IDS models: Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and a hybrid CNN-LSTM model on low-power edge gateways.<n>For anomaly detection, the system transmits a compact and secure telemetry snapshot via low-bandwidth API calls to LLMs.
  arXiv  Detail & Related papers  (2025-11-23T00:33:51Z)
• Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation [3.3894236476098185]
  We propose a dispatch-aware deep neural network (DA-DNN) that accelerates DC-OTS without relying on pre-solved labels.<n>DA-DNN predicts line states and passes them through a differentiable DC-OPF layer.<n>It produces a provably feasible topology and dispatch pair in the same time as solving the DCOPF.
  arXiv  Detail & Related papers  (2025-07-23T04:39:29Z)
• A Hybrid Chaos-Based Cryptographic Framework for Post-Quantum Secure Communications [2.474908349649168]
  We present CryptoChaos, a novel hybrid cryptographic framework that synergizes deterministic chaos theory with cutting-edge cryptographic primitives to achieve robust, post-quantum resilient encryption.<n> CryptoChaos harnesses the intrinsic unpredictability of four discrete chaotic maps to generate a high-entropy, multidimensional key from a unified entropy pool.
  arXiv  Detail & Related papers  (2025-04-11T15:24:21Z)
• Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability Analysis [2.1198879079315573]
  It is difficult to enforce constraints on neural networks in safety-critical control applications.<n>We propose a method that can encourage exact image of a non- hybrid input set for a neural network with rectified linear unit (ReLU) nonlinearities.
  arXiv  Detail & Related papers  (2025-01-22T17:13:48Z)
• Multi-Domain Polarization for Enhancing the Physical Layer Security of MIMO Systems [51.125572358881556]
  A novel Physical Layer Security (PLS) framework is conceived for enhancing the security of wireless communication systems. We design a sophisticated key generation scheme based on multi-domain polarization, and the corresponding receivers. Our findings indicate that the innovative PLS framework effectively enhances the security and reliability of wireless communication systems.
  arXiv  Detail & Related papers  (2023-10-31T05:50:24Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers [105.74546828182834]
  We show a hardware-efficient dynamic inference regime, named dynamic weight slicing, which adaptively slice a part of network parameters for inputs with diverse difficulty levels. We present dynamic slimmable network (DS-Net) and dynamic slice-able network (DS-Net++) by input-dependently adjusting filter numbers of CNNs and multiple dimensions in both CNNs and transformers.
  arXiv  Detail & Related papers  (2021-09-21T09:57:21Z)
• Dynamic Slimmable Network [105.74546828182834]
  We develop a dynamic network slimming regime named Dynamic Slimmable Network (DS-Net) Our DS-Net is empowered with the ability of dynamic inference by the proposed double-headed dynamic gate. It consistently outperforms its static counterparts as well as state-of-the-art static and dynamic model compression methods.
  arXiv  Detail & Related papers  (2021-03-24T15:25:20Z)
• Sparse Multi-Family Deep Scattering Network [14.932318540666543]
  We propose a novel architecture exploiting the interpretability of the Deep Scattering Network (DSN) The SMF-DSN enhances the DSN by increasing the diversity of the scattering coefficients and (ii) improves its robustness with respect to non-stationary noise.
  arXiv  Detail & Related papers  (2020-12-14T16:06:14Z)
• EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference [82.1584439276834]
  Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. We present EdgeBERT, an in-depth algorithm- hardware co-design for latency-aware energy optimization for multi-task NLP.
  arXiv  Detail & Related papers  (2020-11-28T19:21:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.