PaperlayerView exact revision on arXiv

Source-linked AI summary

RIS-based Physical Layer Security for Integrated Sensing and Communication: A Comprehensive Survey

Yongxiao Li, Feroz Khan, Manzoor Ahmed, Aized Amin Soofi, Wali Ullah Khan, Chandan Kumar Sheemar, Muhammad Asif, Zhu Han

arXiv:2503.17721v1eess.SP

TL;DR

ISAC systems face security risks affecting both communications and sensing. This survey organizes RIS-assisted physical-layer security approaches into passive and active paradigms, finding trade-offs among security, throughput, and sensing accuracy.

  • Problem

    ISAC communications and sensing outputs remain vulnerable to eavesdropping, jamming, spoofing, and other adversarial attacks.

  • Method

    The survey synthesizes RIS-assisted physical-layer security methods for ISAC, covering passive RIS and ARIS paradigms, optimization techniques, challenges, and research directions.

  • Results

    The survey identifies trade-offs among security enhancement, communication throughput, and sensing accuracy, with ARIS improving secrecy and sensing but increasing complexity and energy requirements.

  • Takeaways & Limitations

    Secure RIS-assisted ISAC requires balancing security, communication, and sensing objectives while addressing hardware imperfections and accurate CSI acquisition.

Abstract

from arXiv · show

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C\&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C\&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C\&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into passive RIS and Active RIS (ARIS) paradigms. Passive RIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

I. INTRODUCTION · A. Related Surveys · B. Motivation and Contribution

ISAC integrates communication and sensing for efficient future 6G networks but exposes both functions to security threats. This survey addresses the gap by systematically reviewing RIS-assisted PLS across passive and active RIS configurations, optimization methods, challenges, and future directions.

  • I. INTRODUCTION: ISAC unifies communication and sensing, improving spectrum utilization, resource management, and system efficiency for future 6G applications.The integration reduces hardware redundancy and supports applications including autonomous driving, smart healthcare, industrial automation, digital twins, and V2X communication.
  • I. INTRODUCTION: Using existing communication infrastructure for sensing eliminates dedicated radar networks but exposes transmissions and sensing outputs to eavesdropping, jamming, and spoofing.Wireless signals can simultaneously transmit data and extract environmental information, creating security risks for both functions.
  • I. INTRODUCTION: PLS mitigates ISAC vulnerabilities by exploiting wireless-channel fading, interference, and noise to increase secrecy capacity, reduce eavesdropping risks, and improve signal robustness.This approach enhances security at the transmission level without relying on conventional cryptographic techniques.
  • I. INTRODUCTION: RIS dynamically controls electromagnetic-wave reflections and transmissions to strengthen legitimate links, suppress interference, and enhance ISAC physical-layer security.The survey identifies RIS as a key enabler for dynamically optimizing the wireless environment and strengthening ISAC PLS.
  • A. Related Surveys: Existing ISAC surveys emphasize fundamentals, architectures, and performance enhancement, while RIS-enabled security—especially comprehensive treatment of passive and Active RIS—remains underexplored.Prior work often discusses passive RIS benefits such as signal-strength enhancement and interference mitigation without holistically evaluating security characteristics and deployment considerations.
  • B. Motivation and Contribution: The survey establishes foundations covering RIS, PLS, and ISAC, including D-RIS and BD-RIS architectures, operating modes, and PLS technology categories.It provides a structured basis for understanding RIS–PLS integration in 6G networks while addressing architecture advantages and limitations.
  • B. Motivation and Contribution: It classifies RIS-assisted PLS into passive and ARIS approaches, reviewing passive optimization of throughput, covert and secrecy rates, sensing SNR, and WSR, alongside active beamforming for secrecy, covert rates, and sensing.The reviewed methods operate under communication, security, and other system constraints.
  • B. Motivation and Contribution: Beyond classification, the survey compares security–sensing–communication trade-offs, examines complexity, scalability, and deployment constraints, and identifies open challenges and future optimization directions.Its stated goal is secure, efficient, and scalable RIS-PLS solutions for next-generation wireless networks.

II. RIS, PLS, AND ISAC FUNDAMENTALS … III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS

The paper establishes RIS, PLS, and ISAC fundamentals before organizing RIS-assisted PLS for ISAC into passive-RIS techniques addressing throughput, covert and secrecy rates, sensing SNR, and WSR under constraints.

  • II. RIS, PLS AND ISAC FUNDAMENTALS: RIS fundamentals distinguish D-RIS and BD-RIS, covering their architectures, operating modes, advantages, limitations, and tradeoffs.
  • II. RIS, PLS, AND ISAC FUNDAMENTALS: PLS fundamentals classify channel-based methods for maximizing secrecy capacity, key-based methods, and signal processing-based techniques.
  • I. INTRODUCTION: The introduction includes a subsection on motivation and contribution for the surveyed RIS-based PLS and ISAC integration.
  • 2. BD-RIS: The survey examines RIS-assisted PLS frameworks designed for ISAC systems.
  • III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS: Passive RIS techniques optimize system throughput under sensing and other constraints.
  • III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS: Passive RIS techniques maximize covert and secrecy rates while satisfying sensing and other constraints.
  • III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS: Passive RIS techniques maximize sensing SNR subject to secrecy and other constraints.
  • III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS: Passive RIS techniques jointly optimize WSR and sensing under constraints.

IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS … 1) D-RIS Overview:

The surveyed material organizes ARIS-based ISAC security around secrecy-rate, covert-rate, and sensing-SINR objectives, then outlines RIS fundamentals, limitations, D-RIS operating modes, and broader lessons and research directions.

  • IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS: ARIS-based advanced PLS approaches in ISAC systems are structured around optimizing secrecy rates under sensing and other constraints.
  • IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS: A complementary objective is maximizing covert rates while satisfying sensing and other constraints.
  • IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS: Another approach maximizes sensing SINR subject to communication and secrecy constraints.
  • V. LESSONS LEARNED, OPEN ISSUES AND FUTURE RESEARCH DIRECTIONS: The survey taxonomy consolidates the reviewed RIS-assisted PLS approaches and frames lessons learned, open issues, and future research directions.
  • A. RIS Overview and Types:: RIS dynamically controls electromagnetic-wave propagation through reconfigurable elements that adjust signal amplitude and phase, supporting next-generation wireless systems.
  • A. RIS Overview and Types:: Passive RIS reflects signals without amplification to enhance coverage, mitigate interference, and improve security, but channel estimation and double fading remain challenges.
  • 1) D-RIS Overview:: D-RIS uses a reconfigurable matrix with diagonal phase shifts and independently modulating elements, while tunable meta-atoms improve phase adjustment and signal steering.
  • 1) D-RIS Overview:: D-RIS supports reflective, transmissive, and STAR-RIS modes: reflection improves steering but risks self-interference, transmission passes and phase-controls signals, and STAR-RIS jointly reflects and transmits.

Advantages and Limitations of D-RIS: · 2) BD-RIS: · BD-RIS Architectural Configurations:

D-RIS simplifies implementation and reduces energy use through independently controlled diagonal elements, but its limited adaptability motivates BD-RIS. BD-RIS uses interconnected architectures to improve wave control and offers single-, fully-, and group-connected configurations with different complexity–performance trade-offs.

  • Advantages and Limitations of D-RIS:: D-RIS independently adjusts each element’s phase, yielding a diagonal reflective matrix that simplifies hardware design, modeling, and control.Its simple operational principle supports straightforward system implementation.
  • Advantages and Limitations of D-RIS:: D-RIS operates passively without amplifiers or RF chains, making it highly energy-efficient.The reduced reliance on power-hungry components lowers operational power requirements.
  • 2) BD-RIS:: BD-RIS integrates non-diagonal components and interconnected structures to improve electromagnetic-wave control, beam patterns, coverage, interference suppression, and adaptability.It preserves conventional reflective and transmissive operational modes while responding to environmental and user changes.
  • BD-RIS Architectural Configurations:: BD-RIS architectures comprise single-connected, fully-connected, and group-connected configurations with distinct complexity and performance trade-offs.The configurations differ in element interconnection and phase-shift-matrix structure.
  • BD-RIS Architectural Configurations:: Single-connected BD-RIS uses independently functioning, non-interconnected elements with diagonal phase-shift matrices subject to energy-conservation constraints.Reflection and transmission coefficients characterize the nth element.
  • BD-RIS Architectural Configurations:: Fully-connected BD-RIS links all elements through an impedance network, enabling non-diagonal matrices and greater beamforming flexibility under a unitary condition.The reflection and transmission phase-shift matrices both participate in this constraint.
  • BD-RIS Architectural Configurations:: Group-connected BD-RIS forms fully interconnected subarray clusters with block-diagonal matrices, balancing performance and complexity for large-scale deployments.Each group’s reflection phase-shift matrix corresponds to its subarray, whose size is represented by ¯N.

Operating Modes of BD-RIS:

BD-RIS operates in four distinct modes—reflective, transmissive, hybrid, and multi-sector—each tailored to specific communication conditions. The described modes support half-space coverage, obstacle penetration, dual-sided communication, or targeted spatial coverage through different signal paths and surface configurations.

  • Reflective Mode: Reflective mode directs signals toward a receiver on the transmitter’s side and is optimal for half-space coverage.Its phase-shift matrix supports reflection toward receivers positioned on the same side as the transmitter.
  • Transmissive Mode: Transmissive mode lets signals traverse the BD-RIS, enhancing obstacle penetration through block-diagonal phase-shift constraints.This mode is designed for signal transmission through the surface rather than reflection toward the same side.
  • STAR-RIS: STAR-RIS mode integrates reflection and transmission to enable dual-sided communication and dynamically adjust both operations.The combined operation is described as improving flexibility and overall performance.
  • Multi-sector Mode: Multi-sector mode partitions the BD-RIS into independent sectors for targeted spatial coverage using high-gain, narrow-beamwidth elements.These elements enhance precision in beamforming and overall spatial coverage.

Advantages and Disadvantages of BD-RIS: · B. PLS Overview · 1) Channel-Based PLS (Secrecy Capacity Techniques):

BD-RIS improves on traditional D-RIS through interconnected-element beamforming that jointly controls phase and amplitude, reduces interference, and provides 360-degree coverage. PLS secures wireless communications by exploiting channel characteristics to favor legitimate receivers over Eves, with secrecy-capacity methods including wiretap channels, artificial noise, beamforming, and cooperative jamming.

  • Advantages and Disadvantages of BD-RIS:: BD-RIS jointly controls phase and amplitude through interconnected elements, enhancing signal strength, quality, and flexibility across directions and communication scenarios.This beamforming capability is identified as a primary advantage over traditional D-RIS.
  • Advantages and Disadvantages of BD-RIS:: BD-RIS reduces interference more comprehensively than D-RIS by adjusting both amplitude and phase.Its broader interference-management capability complements its enhanced beamforming flexibility.
  • Advantages and Disadvantages of BD-RIS:: BD-RIS provides complete 360-degree coverage, exceeding D-RIS’s typical 180-degree coverage for uniform signal distribution in applications such as industrial IoT and NTNs.This coverage advantage supports its candidacy for future communication frameworks.
  • B. PLS Overview: PLS secures wireless communications by exploiting noise, fading, and interference to support legitimate-user decoding while obstructing eavesdroppers.This approach moves beyond exclusive reliance on cryptographic techniques.
  • B. PLS Overview: Secrecy capacity defines the maximum confidential-transmission rate when the legitimate receiver has a superior channel to the Eve.PLS also considers interceptive monitoring, CSI manipulation, and jamming-based attacks.
  • 1) Channel-Based PLS (Secrecy Capacity Techniques):: Channel-based PLS maximizes the Secrecy Rate (SR) by ensuring superior channel conditions for legitimate receivers compared with unauthorized entities.Its techniques include wiretap channels, AN injection, beamforming, and cooperative jamming.
  • 1) Channel-Based PLS (Secrecy Capacity Techniques):: Wiretap channels favor higher legitimate-user capacity, while AN injection, beamforming, and cooperative jamming limit Eve’s ability to intercept or decode transmissions.AN targets directions containing potential Eves, beamforming minimizes unintended leakage, and friendly nodes generate interference for cooperative jamming.

2) Key-Based PLS (Physical Layer Key Generation): · 3) Signal Processing-Based PLS:

Key-based PLS dynamically generates secret encryption keys from random, time-varying wireless channels, while signal processing-based PLS embeds security into transmitted signals to obscure information from eavesdroppers. Their methods include reciprocity-based generation, random modulation, pilot contamination defense, directional modulation, secret key embedding, and covert communication.

  • 2) Key-Based PLS (Physical Layer Key Generation):: Key-based PLS generates secret encryption keys dynamically from random, time-varying wireless-channel characteristics rather than preshared keys.Unique multipath fading across communication links makes the generated keys difficult for Eve to replicate.
  • 2) Key-Based PLS (Physical Layer Key Generation):: Reciprocity-based key generation extracts identical secret keys from symmetrical channel variations between communicating parties.The method relies on the wireless channel exhibiting reciprocal properties between the legitimate endpoints.
  • 2) Key-Based PLS (Physical Layer Key Generation):: Random modulation applies intentional signal distortions known only to the legitimate receiver, hindering adversarial deciphering.The receiver-specific distortions make the transmitted communication difficult for an adversary to interpret.
  • 2) Key-Based PLS (Physical Layer Key Generation):: Pilot contamination defense alters pilot signals to prevent Eves from accurately estimating the secret key.In WiFi, devices can measure random fading fluctuations to generate a shared encryption key unknown to an external Eve.
  • 3) Signal Processing-Based PLS:: Signal processing-based PLS embeds security measures directly into transmitted signals while maintaining clarity for intended users.It uses signal processing and transmission techniques to obscure information from Eves and prevent unauthorized access.
  • 3) Signal Processing-Based PLS:: Directional modulation dynamically alters signal phase and amplitude so messages are intelligible only at a specific angle.Signals become undecodable elsewhere, restricting message interpretation to the intended direction.
  • 3) Signal Processing-Based PLS:: Secret key embedding integrates encryption keys into the physical signal waveform for access only by authorized users with the appropriate decoding mechanism.The key is carried within the waveform rather than transmitted as a separate element.
  • 3) Signal Processing-Based PLS:: Covert communication conceals transmissions within ambient noise or legitimate background interference, making detection and interception by Eves highly challenging.A satellite system can transmit information so that only receivers at a precise location can decode it.

C. ISAC Overview … Advantages of RIS-Assisted PLS for ISAC Systems:

ISAC unifies wireless communication and radar-based sensing through shared resources and RF signals, while RIS-assisted PLS jointly improves security, sensing, communication quality, robustness, and efficiency. The surveyed advantages include flexible sensing configurations, intelligent signal control, interference management, diversity, scalability, energy efficiency, and seamless C&S integration.

  • C. ISAC Overview: ISAC unifies communication and radar-based sensing by using wireless RF signals for simultaneous data transmission and environmental sensing.This convergence enables synchronized multimodal data acquisition and transmission while improving system efficiency and performance.
  • C. ISAC Overview: ISAC shares frequency bands, hardware components, and computational platforms to transform communication networks into active sensory systems.Network infrastructure uses emitted radio signals to detect, track, and characterize objects in the environment.
  • C. ISAC Overview: Sensing-derived CSI and localization data improve channel estimation, adaptive beamforming, and interference management in dynamic environments.These capabilities support reliable and efficient communication when obstacles and user mobility alter propagation conditions.
  • Types of ISAC Sensing:: ISAC uses monostatic, bistatic, and multistatic sensing, each offering distinct deployment and application benefits.Monostatic sensing uses one device for transmission and reception; bistatic sensing separates them for flexible viewpoints; multistatic sensing distributes multiple transmitters and receivers for broad coverage.
  • D. RIS-Assisted PLS for ISAC Systems: RIS unifies communication and sensing by intelligently controlling electromagnetic waves and allowing communication systems to function as environmental sensors.This integration provides a joint mechanism for optimizing sensing, communication, and security rather than treating C&S separately.
  • Advantages of RIS-Assisted PLS for ISAC Systems:: RIS-assisted PLS makes interception more difficult by exploiting channel noise and fading while preserving high-quality communication for legitimate users.RIS also supports deliberate noise or jamming and spatial, temporal, and frequency diversity to improve interference management, robustness, and security.
  • Advantages of RIS-Assisted PLS for ISAC Systems:: RIS-assisted PLS is scalable and adaptable to dynamic environments, energy-efficient for low-power devices, cost-effective through simpler hardware, and supportive of seamless C&S integration.Its passive nature contributes to EE, while dynamic operation improves resource usage and system efficiency.

III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS … B. Maximizing Covert and Secrecy Rates Under Sensing and Other Constraints

The section surveys passive RIS techniques that jointly strengthen physical-layer security, throughput, covert and secrecy rates, and sensing under multiple constraints. It highlights joint optimization across beamforming, RIS configuration, antenna mobility, waveform design, UAV coordination, and emerging RIS architectures.

  • III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS: Passive RIS improves ISAC physical-layer security by manipulating wireless propagation to reduce information leakage while enhancing detection precision and overall system performance.The section frames passive RIS as an enabler for jointly optimizing communication, sensing, and security.
  • III. PASSIVE RIS-BASED ADVANCED PLS TECHNIQUES IN ISAC SYSTEMS: Passive RIS-assisted ISAC optimization seeks to maximize throughput while satisfying sensing requirements and preserving sensing accuracy.The core challenge is resource allocation that avoids compromising sensing while increasing data transmission rates.
  • A. Optimizing System Throughput Under Sensing and Other Constraints: Advanced throughput-oriented schemes combine Movable Antennas, RIS-based backscatter, UAV-assisted networks, and DISCO-RIS with passive jamming to mitigate eavesdropping threats.Reported optimization variables include beamformers, RIS reflection coefficients, phase shifts, UAV trajectories, and power allocation.
  • A. Optimizing System Throughput Under Sensing and Other Constraints: Jointly optimizing BS beamformers, RIS reflection coefficients, and MA locations balances communication performance, radar sensing efficiency, and eavesdropping defense.The described solution uses penalty-based methods, Rayleigh quotient optimization, convex refinement, and Majorization-Minimization updates.
  • A. Optimizing System Throughput Under Sensing and Other Constraints: RIS-based backscatter and RIS-equipped UAV networks support secure transmission with sensing by optimizing waveform design and coordinating target tracking with communication users.The UAV-assisted setting tracks J targets while serving K communication users.
  • Discussion:: The reviewed passive RIS studies employ beamforming, RIS reflection tuning, UAV trajectory control, and power allocation to improve secure communication, sensing accuracy, and throughput.The discussion identifies real-time adaptability, broader adversarial risks, and scalability beyond single-RIS models as continuing concerns.
  • B. Maximizing Covert and Secrecy Rates Under Sensing and Other Constraints: Secrecy and covert communication schemes use beamforming, phase-shift control, trajectory design, artificial noise, UAV support, STAR-RIS, and TRIS while preserving sensing performance.The subsection emphasizes RIS, AI optimization, and advanced signal processing for strengthening PLS under sensing and other constraints.

1) Covert Rate Under Sensing and Other Constraints Schemes: · 2) Refelctive-RIS based Secrecy Rate Under Sensing and Other Constraints Schemes: · 3) STAR-RIS and TRIS based Secrecy Rate Under Sensing and Other Constraints Schemes:

The surveyed passive RIS-based ISAC security schemes optimize covert or secrecy rates under sensing, CSI, power, QoS, and detection constraints. They span reflective RIS, STAR-RIS, and TRIS designs using beamforming, phase or reflection control, artificial noise, optimization, and reinforcement learning, while facing practical eavesdropper-CSI limitations.

  • 1) Covert Rate Under Sensing and Other Constraints Schemes:: RIS-assisted covert transmission jointly optimizes beamforming and phase shifts to improve secret or covert rates under secrecy, power, mutual-detection, and imperfect-CSI constraints.The reviewed methods use SDR, SCA, and S-procedure-based optimization, with STAR-RIS generating channel uncertainty for covert signaling.
  • 2) Refelctive-RIS based Secrecy Rate Under Sensing and Other Constraints Schemes:: Reflective-RIS security designs suppress eavesdropping by reducing Eve’s SINR while satisfying communication quality, power, and sensing requirements.The approaches combine passive RIS and active BS beamforming, radar signals as artificial noise, and alternating procedures such as FP-SDP-AO, SCA, and Rayleigh-quotient optimization.
  • 2) Refelctive-RIS based Secrecy Rate Under Sensing and Other Constraints Schemes:: Reflective-RIS schemes maximize secrecy rates by jointly designing BS beamforming, RIS phase shifts or reflection coefficients, sensing operations, and artificial-noise transmission.Representative methods address bounded or imperfect CSI, radar-signal jamming, discrete phase shifts, multi-user or multi-target sensing, and non-convex optimization.
  • 3) STAR-RIS and TRIS based Secrecy Rate Under Sensing and Other Constraints Schemes:: STAR-RIS and TRIS frameworks extend secrecy-rate optimization through transmission-reflection control, time switching, energy splitting, time-division sensing-communication, and RSMA.These designs target multi-user C&S accuracy, sensing SNR, long-term security rates, and reduced eavesdropping risks.
  • 3) STAR-RIS and TRIS based Secrecy Rate Under Sensing and Other Constraints Schemes:: STAR-RIS-based methods optimize BS beamforming and transmission-reflection coefficients for overall secrecy rate while meeting sensing-SNR and user-rate constraints.The surveyed implementations use SCA-SRCR, alternating optimization, and DRL methods including DDPG and SAC under varying Eve-CSI assumptions.
  • 3) STAR-RIS and TRIS based Secrecy Rate Under Sensing and Other Constraints Schemes:: Across the three subsections, optimization variables include beamforming, RIS phase or reflection control, UAV trajectory, power allocation, and artificial-noise injection.The category covers covert-rate optimization, reflective-RIS secure communication, and STAR-RIS/TRIS secrecy-rate enhancement.
  • 3) STAR-RIS and TRIS based Secrecy Rate Under Sensing and Other Constraints Schemes:: A central limitation is reliance on available or perfect eavesdropper CSI, although practical attackers may have unavailable or imperfect CSI.Suggested directions include probabilistic CSI models, robust estimation, adversarial machine learning, and more robust security solutions for 6G and beyond.

C. Maximizing Sensing SNR Under Secrecy and Other Constraints

RIS-assisted ISAC methods maximize sensing SNR while preserving secrecy through joint transmit/reflection beamforming, radar filtering, and RIS phase optimization. These approaches use SCA, SOCP, MM, AO, and related techniques, while virtual LoS links and robust designs support secure target detection under practical constraints.

  • Secure sensing SNR maximization: SCA-, SOCP-, MM-, and AO-based optimization jointly refines beamforming, radar filtering, and RIS phase shifts for secure sensing.The methods address nonconvex design coupling and balance sensing quality with communication security.
  • Secure sensing SNR maximization: Joint transmit/reflection beamforming and radar receive filtering increase sensing SNR while limiting information leakage in secure MU-MISO ISAC.RIS transmits a dedicated sensing signal alongside communication signals and optimizes the transmit, reflection, and receive-processing designs.
  • Robust and hardware-aware designs: Robust designs using the S-procedure and sign-definiteness address imperfect CSI, while IOS phase coupling enables simultaneous communication and sensing without additional sensors.These methods demonstrate RIS potential for securing ISAC while maintaining C&S quality.
  • Virtual LoS and advanced sensing: RIS establishes virtual LoS links for NLoS users and targets, and AO-based designs outperform random-phase and separate-beamforming baselines in sensing performance.Related work also considers multi-target sensing and game-theoretic radar stealth against unauthorized ISAC access.
  • Open challenges: Perfect-CSI assumptions, offline AO/SDR/SOCP optimization, RIS phase quantization, and power limitations constrain robustness, real-time adaptability, and practical deployment.Probabilistic CSI models, robust estimation, and reinforcement-learning-based optimization are identified as future directions.

D. Optimizing WSR and Sensing Under Constraints · IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS

The section reviews secure RIS-assisted ISAC optimization for WSR, sensing, and target detection using beamforming, RIS phase tuning, and interference-based measures. It then presents ARIS-based PLS as a framework for dynamic beamforming, amplification, covert communication, and improved security-sensing trade-offs, while noting practical limitations.

  • D. Optimizing WSR and Sensing Under Constraints: Secure WSR and sensing optimization combines DFRC, semi-passive RIS, secure beamforming, and adaptive interference-based measures to improve communication security and sensing accuracy.The reviewed techniques include frequency-shifted chirp spread spectrum modulation, symbol-level precoding, and active-passive beamforming optimization.
  • D. Optimizing WSR and Sensing Under Constraints: The RIS-enhanced DFRC strategy uses FSCSS-IM for radar-based data transfer and radar-acquired azimuth information for communication beamforming against amplitude and phase distortions from potential Eves.The design is presented as an ISAC strategy tailored for future 6G networks.
  • D. Optimizing WSR and Sensing Under Constraints: For point and extended targets, optimization addresses user SNR and CRB under power, constructive-interference, and security constraints using Taylor expansion and SLM.Direction-of-arrival estimation supports point targets, whereas extended targets require the complete target response matrix.
  • D. Optimizing WSR and Sensing Under Constraints: The surveyed RIS-assisted methods target secure communication and target detection through beamforming, RIS phase tuning, radar-aided beamforming, CRB minimization, and symbol-level precoding.The discussion identifies studies – as combining these mechanisms for security and sensing accuracy.
  • D. Optimizing WSR and Sensing Under Constraints: Existing studies commonly assume ideal RIS operation and unlimited power, omit imperfect CSI, and lack scalable multi-user security models for realistic ISAC deployment.Symbol-level precoding and interference-based security in adaptive beamforming also remain underexplored for eavesdropping resistance.
  • IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS: ARIS-based PLS enables dynamic beamforming and amplification, strengthening resistance to eavesdropping while supporting covert communication and enhanced sensing capabilities.ARIS provides greater control than passive RIS through optimized beamforming and effective power management.
  • IV. ARIS-BASED ADVANCED PLS APPROACHES IN ISAC SYSTEMS: ARIS optimization uses SDP, AO, and SCA to balance security and sensing alongside communication capabilities in future ISAC systems.These methods are presented as advanced optimization techniques for managing the security-sensing trade-off.

A. Optimizing Secrecy Rates Under Sensing and Other Constraints … V. LESSONS LEARNED, OPEN ISSUES AND FUTURE

The surveyed RIS-assisted PLS methods jointly optimize secrecy or covert communication and sensing performance under practical communication, detection, power, and security constraints. ARIS improves these trade-offs through signal amplification, but introduces energy, hardware, CSI, and real-time optimization challenges requiring new research directions.

  • A. Optimizing Secrecy Rates Under Sensing and Other Constraints: ARIS-based methods maximize secrecy rates by jointly optimizing beamforming, RIS reflection coefficients, and radar reception while enforcing sensing and power constraints.These approaches address malicious UAV and target-as-eavesdropper scenarios using FP, MM, or iterative optimization for non-convex problems.
  • A. Optimizing Secrecy Rates Under Sensing and Other Constraints: Rate-splitting, artificial noise, RIS control, and radar beamforming are iteratively optimized to improve security in RSMA-based ISAC under RIS power limits.Simulations report that the proposed method significantly outperforms existing benchmarks.
  • B. Maximizing Covert Rates Under Sensing and Other Constraints: Covert-rate studies extend secure ISAC through ARIS-assisted NOMA and millimeter-wave systems, outperforming passive-RIS and non-RIS configurations in secrecy rate.These systems jointly support sensing and covert communication while addressing privacy risks from uncontrolled wireless transmission and limited block length.
  • B. Maximizing Covert Rates Under Sensing and Other Constraints: Active STAR-RIS designs jointly optimize beamforming, filtering, and transmission/reflection to maximize secrecy and covert rates under SNR, DEP, and power constraints.Pinsker’s inequality and large-system analysis bound Willie’s DEP, while SCA, SDR, and rank-one relaxation solve the optimization iteratively.
  • C. Maximizing Sensing SINR Under Communication and Secrecy Constraints: Robust active beamforming maximizes radar SINR under secrecy-outage constraints with imperfect CSI, using alternating SDR and Taylor-expansion updates.Simulations show that the AO algorithm enhances secure communication and sensing compared with conventional approaches.
  • C. Maximizing Sensing SINR Under Communication and Secrecy Constraints: ARIS improves secrecy rate, sensing SINR, and covert communication by reflecting and amplifying signals, mitigating multiplicative fading, and optimizing secrecy–sensing trade-offs.Compared with passive RIS, ARIS can achieve comparable performance without relying on larger surfaces under low-power constraints.
  • V. LESSONS LEARNED, OPEN ISSUES AND FUTURE: ARIS-based ISAC remains limited by higher power consumption, hardware complexity, heat dissipation, offline optimization, unrealistic CSI assumptions, and underexplored interference-aware sensing security.Future directions include power-efficient designs, machine-learning optimization for real-time adaptation, multi-user covert security, probabilistic CSI estimation, and adversarial learning.
  • V. LESSONS LEARNED, OPEN ISSUES AND FUTURE: The concluding discussion synthesizes insights, unresolved challenges, and future research directions for RIS-based physical-layer security in ISAC.The research agenda spans the integration of secure communication and sensing in next-generation wireless systems.

A. Lessons Learned • · B. Open Issues and Future Research Directions • · VI. CONCLUSION

RIS-assisted PLS for ISAC balances security, communication, and sensing through passive and active RIS, but faces hardware, estimation, energy, complexity, and sophisticated-attack limitations. Future work emphasizes adaptive AI-enabled designs, sustainable power, and hybrid quantum-resistant security for scalable 6G deployment.

  • A. Lessons Learned •: Passive RIS improves security by modifying the wireless environment, whereas ARIS offers better secrecy and covert-communication control through active beamforming at higher energy and complexity costs.Passive RIS is constrained by phase shifts; ARIS provides greater control but increases energy consumption and system complexity.
  • A. Lessons Learned •: Security, communication efficiency, and sensing performance exhibit trade-offs, requiring joint optimization because stronger secrecy or covert communication can reduce sensing accuracy or throughput.Secure beamforming, AN, and jamming strengthen security but remain insufficient against sophisticated attacks.
  • A. Lessons Learned •: Signal attenuation, phase noise, limited phase resolution, passive operation, and large RIS arrays hinder deployment, motivating energy-efficient designs, scalable implementations, and improved CSI estimation.AI-driven and hybrid estimation approaches are needed because conventional methods often struggle with RIS characteristics.
  • A. Lessons Learned •: Future RIS-PLS systems should autonomously adapt phase shifts and beamforming to real-time threats, integrating machine learning and advanced optimization for resilient 6G ISAC.The intended outcome is a scalable and efficient security solution for next-generation ISAC networks.
  • B. Open Issues and Future Research Directions •: AI can strengthen ISAC security through real-time threat identification, anomaly detection, cyber-threat detection, and prediction of potential security breaches.AI-driven algorithms evaluate sensing and communication data and may support sectors including smart cities, supply chains, agriculture, transportation, and environmental monitoring.
  • B. Open Issues and Future Research Directions •: RF and solar energy harvesting offer power alternatives for RIS, but RF efficiency is limited by energy availability and conversion rates, making it insufficient for high-power applications.Solar-assisted RIS is presented as a more sustainable alternative enabling signal processing and power generation.
  • B. Open Issues and Future Research Directions •: Future RIS-assisted ISAC should combine quantum-resistant protocols with hybrid classical-quantum frameworks for enduring defense, secure key distribution, authentication, and encryption.Hybrid designs address the gradual transition toward entirely quantum-secure networks.
  • VI. CONCLUSION: The survey distinguishes D-RIS and BD-RIS architectures and passive versus ARIS paradigms, highlighting their roles in balancing security, covert communication, and sensing performance.It identifies restricted passive phase shifting and ARIS beamforming as central contrasts in security and sensing optimization.
Loading 2503.17721v1…