Reconfigurable Intelligence Surface Assisted Key Generation Resistant to Signal Injection Attacks

YANG Lijun; WANG Haoming; ZHU Tiancheng; WU Meng

doi:10.11999/JEIT251281

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YANG Lijun, WANG Haoming, ZHU Tiancheng, WU Meng. Reconfigurable Intelligence Surface Assisted Key Generation Resistant to Signal Injection Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251281

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YANG Lijun, WANG Haoming, ZHU Tiancheng, WU Meng. Reconfigurable Intelligence Surface Assisted Key Generation Resistant to Signal Injection Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251281

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Reconfigurable Intelligence Surface Assisted Key Generation Resistant to Signal Injection Attacks

doi: 10.11999/JEIT251281 cstr: 32379.14.JEIT251281

1.
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: This paper was supported by the National Natural Science Foundation of China(62372244, 62172235), ZTE Industry-university-Research Fund(2023ZTE08-02), Primary Research & Developement Plan of Jiangsu Province (BE2023025), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX24_0299), Natural Science Foundation of Nanjing University of Posts and Telecommunications (Grant No. NY225164).

Accepted Date: 2026-02-05
Rev Recd Date: 2026-02-05

Available Online: 2030-08-24

Abstract

Abstract

Objective This work investigates the potential threat of signal injection attacks to physical layer key generation (PLKG) in reconfigurable intelligent surface (RIS)-assisted wireless systems, which can be particularly impactful in quasi-static channels where the channel state remains highly correlated across multiple probing rounds. From both attack and defense perspectives, the study clarifies how the spatial correlation between RIS reflection channels and eavesdropping channels may be exploited to enhance key inference, and designs a channel-randomization mechanism that leverages the controllability of RIS to suppress key leakage, reduce the eavesdropper’s key capacity, and improve the security of RIS-assisted PLKG for future 6G scenarios. It further provides quantitative insights into the interplay among injection power, SNR, and spatial correlation, which may serve as a useful reference for future studies on robust RIS configuration and secure system design in practice. Methods A RIS-assisted TDD system is considered where single-antenna Alice and Bob generate symmetric keys from a reciprocal channel, while a two-antenna active eavesdropper Eve injects signals using previously observed CSI (Fig. 1). The links follow quasi-static Rayleigh block fading, and CSI for Alice/Bob/Eve is specified per time slot in a coherence interval. We model a conventional injection attack in which Eve estimates the eavesdropping channel in one slot, precodes an injected waveform, and contaminates the next probing at Alice and Bob, partly steering their key source. We then present a joint key-inference strategy that exploits spatial correlation between RIS reflection and eavesdropping channels and the common RIS-induced subchannel shared by legitimate and eavesdropping links (Table 1). As a defense, we propose channel-randomization PLKG: Alice randomly reconfigures RIS coefficients at each probing round so the Alice–Bob, Alice–Eve, and Bob–Eve effective channels vary independently across rounds while Alice–Bob reciprocity within a round remains. Eve’s injection, precoded with outdated CSI, then appears as uncorrelated interference. We derive mutual-information-based bounds on secret-key capacity to obtain key capacities, define Eve’s key recovery rate (KRR), and validate results via 10,000-trial MATLAB Monte Carlo simulations using an information-theoretic estimator toolbox under varying SNR, injection power, and spatial correlation (Figs. 2–5, Table 2). Results and Discussions Analysis of the conventional injection attack without RIS defense shows that at high SNR, due to channel reciprocity, Alice and Bob observe nearly identical reciprocal channels, while Eve’s injected-signal-based estimate follows a similar trend with noticeable mismatch (Fig. 2). Thus, Eve recovers some key bits but with notable errors and only still moderate KRR. With the proposed joint key inference strategy, Eve’s reconstructed channel matches the legitimate response more closely (Fig. 3), since RIS-assisted PLKG causes legitimate and eavesdropping links to share an RIS-induced subchannel, introducing exploitable spatial correlation beyond the known injected signal; consequently, Eve’s key capacity and KRR increase significantly, further highlighting a stronger RIS-specific threat. At fixed SNR (Fig. 4), Eve’s key capacity without defense rises rapidly with injection power and can approach or exceed the legitimate capacity, whereas under RIS randomization the legitimate capacity degrades slightly and Eve’s remains small and nearly constant, indicating that randomization converts structured injection into noise. Spatial-correlation results in Fig. 5 show that Eve’s capacity without defense grows quickly and becomes critical near correlation one, while with RIS randomization it increases slowly and can be near zero at moderate correlation. Table 2 further confirms these trends in KRR: about 50% with no correlation and no injection; about 62.5% with injection but zero correlation while defense stays near random; and over 80% with higher correlation and injection, reduced to roughly 57%–66% by the defense. Conclusions The study investigates the dual role of RIS in PLKG security, showing that RIS can act both as a vulnerability amplifier and as a defensive tool. By exploiting the correlation between RIS reflection channels and eavesdropping channels, an improved joint key inference attack is developed, which increases the eavesdropper’s key capacity and recovery rate compared with conventional injection attacks, thus revealing a new attack vector for RIS-assisted systems. By harnessing the dynamic controllability of RIS, a channel-randomization PLKG scheme is then proposed, in which RIS is used to shorten the effective coherence time to a single probing round and to decorrelate successive channel realizations from the attacker’s perspective. Theoretical derivations and Monte Carlo simulations demonstrate that this defense scheme transforms malicious injection signals into uncorrelated interference, reduces the eavesdropping key capacity, and pushes the eavesdropper’s KRR close to that of random guessing, even under high SNR, strong spatial correlation, and large injection power. The proposed mechanism achieves these security enhancements with low hardware overhead compared with reconfigurable antenna-based solutions, since RISs are already envisioned as key infrastructure elements in 6G networks. The insights gained from this work provide guidelines for the secure design of RIS-assisted PLKG, suggesting that the controllable nature of RIS should further effectively be exploited for both performance enhancement and security hardening.
- Reconfigurable Intelligence Surface,
- key speculation,
- physical layer key generation,
- signal injection attack

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References(24)

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