\section{Introduction}
From the fifth-generation (5G) wireless communication to the forthcoming 6G wireless communication, we are moving towards the Internet of everything (IoE) era. This disruptive achievement is credited to massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) communication, integrated space-to-ground communication, and so on. However, due to the broadcast nature of wireless networks, malicious users can easily launch a series of attacks through the physical layer, such as eavesdropping, media access control (MAC) layer spoofing, etc \cite{7539590}. As more and more ubiquitous wireless networks put into use, the investigation of defense strategy of physical layer security (PLS) becomes more important. Thus, integrating security at physical layer is indispensable for the evolution of wireless communication. Traditionally, symmetric encryption schemes play an important role in information security, such as providing information confidentiality, information integrity and authentication. Whereas, the secret key managements of massive wireless devices, such as the key generation, update, and storage, are constantly facing substantial pressure.

Physical layer key generation (PLKG) is a promising technology to extract symmetric keys from wireless
channel fading in a plug-and-play manner \cite{8883129}. Specifically, the PLKG is based on short-term channel reciprocity, spatial channel uniqueness and  intrinsic channel randomness, which require no public key infrastructure (PKI). From the perspective of information-theoretical security, PLKG is the most promising scheme to achieve Shannon's perfect encryption. The standard process of PLKG includes channel probing, quantization, information reconciliation and privacy amplification, which can be described as follows;
\begin{itemize}
  \item \textit{Channel probing}: Based on the short-term channel reciprocity in time division duplex (TDD) systems, the legitimate users transmit the pilot sequences accordingly to estimate the channel and collect the channel probing characteristics, such as the received signal strength (RSS) and channel state information (CSI).
  \item \textit{Quantization}: The legitimate users independently quantify the channel characteristics into binary bit sequences, which are used as raw key sequences. Due to the quantization accuracy and channel intrinsic randomness, there are some mismatched bits in the raw key.
  \item \textit{Information reconciliation}: The legitimate users negotiate the possible bit disagreements between each other by using an error correcting code, i.e., low density parity check (LDPC) code or principle component analysis, etc.
  \item \textit{Privacy amplification}: To remove the possible information leakage in public negotiation, the final symmetric key is distilled from the discussed raw key sequences via the hash function, which completes the PLKG process.
\end{itemize}

However, the performance of the PLKG is strongly depended on the intrinsic channel randomness. The key generation rate cannot be guaranteed in some static fading environments but the data transmission is on the order of Gbit/s, which limits the large-scale penetration and deployment in practice. For example, in static indoor environments, negotiating a sufficiently random raw key is a laborious and time-consuming task due to the fact that the channel based attenuations are almost predictable. This situation is predominant in some scenarios, such as an empty room or a corridor at night. Previous works have focused on PLKG in static wireless environments \cite{6311224,9000831,1528749}. The widely studied approach is to increase the randomness of the wireless channel by employing a single relay and/or cooperative relays \cite{6311224}. The participation of untrusted relays can cause the information leakage for the secret key, and the deployment of additional trusted relays can increase the cost of PLKG. Artificial random source assistance is another method to improve the key generation rate \cite{9000831}. Although this method utilizes not only the channel intrinsic randomness but also the signal randomness, it needs to modify the frame structure of the pilot signal, which limits its application on existing commercial devices, i.e., Wi-Fi. What is more, intelligent antennas have been studied to provide a high fluctuation of the channel characteristics, thereby extracting the high-entropy secret key \cite{1528749}. Nevertheless, the scalability and compatibility of off-the-shelf devices is a hindrance to the development of this approach.

Reconfigurable intelligent surfaces (RISs) with their excellent ability on electromagnetic wave control, can customize the wireless channel with moderate hardware cost and low energy consumption, and for these reasons are becoming a potential innovative technology for 6G \cite{8746155}. At present, RISs are gradually coming at the research forefront for assisting PLKG and enhancing PLS. As an early attempt, a programmable metasurface, namely HyperSurface, has been developed to show groundbreaking performance and security potential in indoor wireless communication \cite{LIASKOS20191}. Moreover, the RIS units optimization and the prototype system measurement of RIS-aided PLKG have been respectively studied in recent works \cite{9298937,9569556,9771319}. On the other hand, from the attack perspective, RIS based attacks for PLKG have been investigated, such as environment reconstruction attack and RIS manipulating attack, which make the defense strategy of PLKG even more challenging \cite{8957701,1683399}.

The combination of PLKG with RISs entails new challenges and opportunities. For instance, in RIS assisted wireless communication, the participants can realize dual-functional integration via RIS that is improving PLKG while communicating. This gives rise to the interesting concept of 6G intrinsic security that we refer to as integrating communications and security (ICAS). In the past five years, although several works have been reported on RIS-aided wireless communication with extremely diversified application scenarios, research on RIS-aided PLKG is still relatively scarce \cite{9326394}. In this context, we illustrate several potential application scenarios of RIS-aided PLKG in Fig. \ref{Fig:RIS_deployment}. The first scenario is the basic indoor RIS-aided PLKG, where Eve attempts to eavesdrop the channel information and recover the raw key. The second scenario considers a multi-RIS cooperation that is used to further increase the bit generation rate and reduce the information leakage on Eve via the cooperative RIS control strategy. In the third scenario, the direct link between Alice and Bob is blocked, and a RIS can be deployed at the obstacle edge to boost the PLKG. The optimization deployment of RIS is critical for facilitating key generation and preventing information leakage. The fourth application scenario shows that given the wireless coverage enhancement assisted by the RIS, Eve can hide in the corner of the obstacle to eavesdrop the key information, which increases the covertness of Eve. The fifth scenario is from the active attack perspective: it illustrates that the RIS can be utilized by a malicious user to launch denial of service (DoS) attacks to further prevent key generation and communication. The last one is an attack-defense scenario where both the legitimate users and malicious user are equipped with RIS. In this scenario, the control strategy of the RIS between the honker and hacker is a promising research field. The deployment of mobile RIS for PLKG, aerial RIS-aided PLKG as well as RIS-aided PLKG among multi-user and/or multi-antenna are also potential application scenarios. In addition, we highlight that only few works have measured the actual performance of the RIS-aided PLKG, whilst many PLKG challenges for RIS-aided channel are still unknown. Therefore, the real-world performance and design challenges of RIS-aided PLKG in static indoor environments should be further studied. This discussion is the motivating factor of this article in the space of RIS-aided PLKG. We start our analysis with the RIS-aided channel model and hardware architecture; then, we present proof-of-concept experiments using a RIS-aided PLKG prototype
system and discuss the design challenges, which includes channel reciprocity, RIS switch speed and RIS deployment. Moreover, we design a RIS flip attack via prototype experiments and discuss its attack-defense countermeasures. Some insightful conclusions and future research pathways are identified.

\begin{figure*}[!ht]
 \centering
  \includegraphics[width=18cm]{./figures/RIS_PKG1}
  \caption{The potential application scenarios for RIS-aided PLKG.}
  \label{Fig:RIS_deployment}
\end{figure*}






\section{System Model}
In this section, we first provide the RIS-aided channel model, and then give the hardware architecture of RIS.
\subsection{Channel Model}
We consider a Alice-Bob-Eve network in a static indoor environment, where transmitter Alice and Bob are legitimate users and Eve is a malicious user. All participants are equipped with a single antenna and work in the TDD mode. Alice and Bob plan to generate a common secret key from wireless fading channel, whilst Eve plans to hear the PLKG information over the wireless fading channel. When the RIS is added to the Alice-Bob-Eve network, the RIS-aided channel can be roughly written as the sum of the multiplicative channel and the direct link channel. Thus, the RIS-aided channel from Alice to Bob can be denoted as
\begin{equation}
\label{A2B}
\widetilde{h}_{AB}=\underbrace{\mathbf{h}^T_{RB}\mathbf{\Phi}\mathbf{h}_{AR}}_{\text{Multiplicative
channel}}+\underbrace{h_{AB}}_{\text{Direct link channel}},
\end{equation}
and the RIS-aided channel from Bob to Alice is given by
\begin{equation}
\label{A2B}
\widetilde{h}_{BA}=\mathbf{h}^T_{RA}\mathbf{\Phi}\mathbf{h}_{BR}
+h_{BA},
\end{equation}
where $\mathbf{h}_{AR}$ and $\mathbf{h}_{BR}$ represent the channel from Alice to RIS and from Bob to RIS, $\mathbf{h}_{RA}$ and $\mathbf{h}_{RB}$ represent the channel from RIS to Alice and from RIS to Bob, $(\cdot)^T$ is the transpose operation, and $\mathbf{\Phi}$ denotes the reflection matrix of the RIS in bidirectional channel probing, respectively. Similarly, the channel from Alice to Eve or from Bob to Eve can be written-out in the same manner by substituting $\mathbf{h}^T_{RE}\mathbf{\Phi}\mathbf{h}_{AR}$ or $\mathbf{h}^T_{RE}\mathbf{\Phi}\mathbf{h}_{BR}$ to the multiplicative channel and substituting $h_{AE}$ or $h_{BE}$ to the direct link channel.
\subsection{Hardware Architecture}
A RIS is made of a planar digitally programmable metasurfaces \cite{CuiTieJunRIS}. Specifically, a RIS is typically composed of three layers and a smart controller. The outer layer contains a large number of periodically repeated metasurface units, which can act directly on the incident electromagnetic signals. The subwavelength metamaterial units are composed of individual units equivalent to ``molecules/atoms" of natural materials. The middle layer is a metal isolation plate, which is used to avoid electromagnetic leakage. The inner layer is the control circuit, which is used to adjust the reflection amplitude and/or phase shift of each metasurface unit. The smart controller of RIS is usually a programmable field-programmable gate array (FPGA), which can send coding sequence to RIS and connect wirelessly to communication components, i.e., access points and terminals. In this case, by using FPGA as a smart controller, the RIS can realize different wireless propagation functions.

Theoretically, the reflection amplitude and/or phase can be continuously adjusted, where the reflection amplitude can be effectively customized within $[0,1]$ by changing the resistor load, while the reflection phase can be shifted within $[0,\pi]$ by designing binary coding sequences. However, due to the hardware cost and implementation complexity, the existing works often consider the discrete control with finite amplitude and/or phase values. Take $1$-bit phase shift control as an example: each metasurface unit can independently realize 0 and $\pi$ phase shifts by switching the PIN diode between ``OFF" and ``ON" states, respectively.

\section{Challenges in Design}
\begin{figure*}[!ht]
 \centering
  \includegraphics[width=18cm]{./figures/RIS_deployment}
  \caption{The experiment representation for RIS-aided PLKG. Figure 2(a) is for the experiment of channel
reciprocity and RIS switch speed, where Alice, Bob and RIS are at fixed positions. Figure 2(b) is for the experiment of RIS deployment, where Alice and RIS static, while Bob can move along the horizontal direction.}
  \label{Fig:RIS_deployment}
\end{figure*}
In this section, we analyze the main challenges in designing RIS-aided PLKG from proof-of-concept experiments, which are shown in Fig. \ref{Fig:RIS_deployment}, including channel reciprocity, RIS switch speed and RIS deployment. Specifically, the experiment scenario of channel reciprocity and RIS switch speed is shown in Fig. \ref{Fig:RIS_deployment}(a), and the experiment of RIS deployment is based on the scenario in Fig. \ref{Fig:RIS_deployment}(b).

Regarding the experiment architecture, the experiment requires the usage of the high-performance notebook HOST PC, the software radio platform USRP-RIO, the synchronous clock node WR LEN, the clock distributor WR switch, and the RIS. The RIS operates at $4.25$ GHz, and has $8$ rows $\times$ $32$ columns RIS units, where each RIS unit is a square with size $0.012$ m and every two rows of RIS units are a group. The RIS is controlled by four voltage signals V1, V2, V3, V4, where each voltage signal can randomly generate one of 16 equal interval voltages within $[0,21]$ to control four groups of RIS units. Alice and Bob are configured with a single antenna and connected to the PC terminal via an optical fiber. For signal design, each wireless frame is $10$ ms and $2$ OFDM symbols with $1200$ sub-carriers for each symbol are inserted to each frame. The first OFDM symbol is inserted with a pilot at every $6$ sub-carriers, and a total of $200$ pilots are inserted for the acquisition of CSI. The second OFDM symbol includes $1200$ bit of data for the calculation of RSS. The least squares (LS) channel estimation is used to obtain the CSI, and the double threshold quantization method and single bit cumulative distribution function (CDF) quantization method are adopted to obtain the raw key sequences \cite{CDFquantization}. All the raw key sequences are carried on randomness tests based on the statistical test suite for random number generators (NIST).


\subsection{Channel Reciprocity}
In the experiment of channel reciprocity, we discuss that whether the introduction of RIS can preserve the channel reciprocity or not.
\subsubsection{Setup}
For this experiment setup, the Alice-Bob transmitter pair is placed in a static indoor environment, where channel fading is varying slowly and there are few scatterers. Alice and Bob are at fixed locations and the distance between Alice and Bob is about $1.5$ m. In order to compare it to the case without RIS, we first collect $20000$ frames in the Alice-Bob pair and refer to as DATA 1. Next, by setting the RIS switch speed to be $100$ ms/time, we extract one frame every ten frames and record it in the PC terminal. Then, we take $20000$ frames in $2000$ seconds and represent it as DATA 2.
\subsubsection{Results} With CSI estimation, RSS calculation and bit quantization for DATA 1 and DATA 2, respectively, we can obtain the raw key sequences before information reconciliation and privacy amplification. From the results in Fig. \ref{Fig:PGR11}(a), we find that the bit inconsistency rate using CSI is significantly lower than the bit inconsistency rate using RSS, both in double threshold quantization and single bit CDF quantization. The reason is that RSS represents the average signal energy over a period of time and is a coarse-grained measure of channel information. On the contrary, CSI is a kind of fine-grained channel information measurement, which can better reflect the characteristics of the channel. Most obviously, from the results of DATA 1 and DATA 2 in Fig. \ref{Fig:PGR11}(a), we see that the bit inconsistency rate of DATA 1 is significantly higher than that of
DATA 2, which indicates that the received signal energy can be enhanced with the assistance of the RIS in the reflected path. In other words, the addition of RIS in Alice-Bob transmitter pair can improve the channel reciprocity by increasing the signal to noise ratio (SNR), and then reduce the inconsistency rate of bit, which facilitates the raw key generation. The CDF of RSS for DATA 1 and DATA 2 can be found in Fig. \ref{Fig:PGR11}(b); the CDF of DATA 1 changes rapidly with RSS, whereas this change is slower in DATA 2. This observation showcases that with the addition of RIS, the RSS values have a wider fluctuation range, which increases the randomness of the channel. Notably, from Fig. \ref{Fig:PGR11}(d), we observe that the key generation rate has 15-fold improvement.
\subsection{RIS Switch Speed}
In this experiment, by analyzing the different RIS switch frequencies, we reveal the influence of RIS control on PLKG.
\subsubsection{Setup} In this experimental design, Alice and Bob are also placed in a static indoor environment with fixed locations. Apart from changing the RIS switch speed with $100$ ms/time just like in the channel reciprocity experiment, we also adjust the RIS switch speed with $1000$ ms/time and collect one wireless frame in every $100$ frames. After recording $20000$ frames on the PC terminal, we denote this data set as DATA 3.
\subsubsection{Results} DATA 3 is also transformed into raw key bits via CSI estimation, RSS calculation and bit quantization. In Fig. \ref{Fig:PGR11}(a), compared with DATA 1 and DATA 2, the bit inconsistency rate of DATA 3 is the smallest. This observation showcases that the RIS switch speed can affect the reciprocity of the channel and change the inconsistency rate of raw key. This implies that a high RIS switch speed may increase the bit mismatch rate due to the increase of channel randomness. This experiment also fully proves the importance of optimization on RIS switch speed to attain a tradeoff between bit mismatch rate and channel randomness in RIS-aided PLKG design. From the results of DATA 2 and DATA 3 in Fig. \ref{Fig:PGR11}(b), we can further conclude that the RIS plays a constructive role in PLKG. In addition, based on Fig. \ref{Fig:PGR11}(d), we find that the key generation rate of DATA 3 is slightly lower than that of DATA 2. The reason is that the reduction of RIS switch speed leads to the decrease of channel fluctuation frequency, which reduces the successful probability of quantization.
\subsection{RIS Deployment}
The deployment of RIS gives a new degree of freedom for PLKG, which creates new challenges and opportunities. Here, we reveal the impact of RIS deployment on PLKG.
\begin{figure*}[!ht]
 \centering
  \includegraphics[width=18cm]{./figures/PGR11.eps}
  \caption{The experiment results of RIS-aided PLKG design.}
  \label{Fig:PGR11}
\end{figure*}
\subsubsection{Setup}
In a static indoor scenario, Alice's location is fixed and $0.5$ m away from the RIS, while Bob can move horizontally to different locations, thereby representing different RIS deployments. Specifically, the RIS switch speed is $100$ ms/time, thus one frame is extracted and stored from every ten frames. We collect three data sets, DATA 4, DATA 5, and DATA 6 with respect to the distance between RIS and Bob at $0.5$ m, $1$ m and $1.5$ m, respectively.
\subsubsection{Results} In the same way, LS channel estimation is used to obtain the CSI, and then the double threshold quantization  and single bit CDF quantization are utilized to produce the raw key. Based on Fig. \ref{Fig:PGR11}(a), the results show that with the distance between Bob and RIS growing, the bit inconsistency rate gets higher, which indicates the following two things. On one hand, the longer the distance between two communication participants, the greater the path loss will be. Thus, low SNR results in poor channel quality, which leads to a higher bit inconsistency rate. On the other hand, the longer the distance between Bob and RIS is, the proportion of the energy of RIS reflected path in the energy of all scattered paths will decrease, and then the RIS will no longer play a major role in the wireless environment. As a result, with the channel reciprocity decreasing, the bit inconsistency rate increases. This phenomenon can also be seen from Fig. \ref{Fig:PGR11}(d), which shows that it is better to deploy the RIS near the transmitter sides to reach a high key generation rate. Furthermore, this experiment also reflects that CSI has a better performance than that of RSS for PLKG with different RIS deployments. Moreover, from Fig. \ref{Fig:PGR11}(c), we can prove that the closer RIS is to the transmitter, the larger the channel fluctuation becomes.
\section{RIS for Attack}
In this section, we study the case that the RIS is controlled by Eve and give some insightful results from real-world experiments. Then, we discuss potential countermeasures.
\subsection{Challenges \& Experiments}
When malicious Eve has control over a RIS, it can rapidly flip its RIS to change the wireless environment, which can affect the PLKG between Alice and Bob. Here, this attack is referred to as RIS flip attack. Next, we show the influence of such an attack by analyzing the RIS switch speed during PLKG.
\subsubsection{Setup}
In this considered scenario, the location of Alice is fixed and the distances between Bob and RIS are $0.5$ m, $1$ m and $1.5$ m, respectively. Particularly, the RIS is assumed to be controlled by Eve and the RIS switch speed is set to be $1$ ms/time. According to the different distances between Bob and RIS, we collect three data sets, namely, DATA 7, DATA 8 and DATA 9, respectively.
\begin{figure}[!ht]
 \centering
  \includegraphics[width=8.8cm]{./figures/risATTACK.eps}
  \caption{The key generation rate: Legitimate RIS vs. RIS flip attack.}
  \label{Fig:RISat}
\end{figure}
\subsubsection{Results}
Based on the raw key sequences generated from DATA 7, DATA 8 and DATA 9, we observe that when the RIS switch speed is changed to $1$ ms/time, the Alice-Bob communication pair cannot complete an effective channel probing within a coherence time. In this case, the channel reciprocity between Alice and Bob is seriously compromised. Therefore, at the same distance between Bob and RIS, the bit inconsistency rate with RIS switch speed $1$ ms/time is much higher than that of $100$ ms/time. This produces a great pressure on information reconciliation for PLKG, which indicates more negotiation overhead. Figure \ref{Fig:RISat} shows that when the RIS is switched at $1$ ms/time by Eve, the key generation rate in different distances is only $0$-$1$ bit/frame, which seriously hinders the PLKG process of legitimate users.
\subsection{Discussions}
The RIS flip attack is an active attack which can change the RIS switch speed in a very short time, i.e., in the order of $\mu s$, which can be regarded as if the channel coherence time being compressed to some extend. In this case, Alice and Bob cannot complete bidirectional probing for at least one frame. Thus, the RIS attack is a DoS attack, which restrains the process of PLKG. Unfortunately, different from traditional active attack countermeasures, which can trace such attack based on wireless radio frequency (RF) fingerprints, RIS flip attack is inherently undetectable due to the passive nature of RIS which has no RF chain. Therefore, some new ideas are necessary to defend against such detrimental attack.
\begin{itemize}
  \item \textbf{Countermeasure 1}: Since the flip attack may achieve its malicious objective by changing the wireless channel environment using a RIS, similarly, legitimate users can deploy RIS to counteract the changes of radio electromagnetic wave, thus extending the coherence time of the channel. Specifically, Alice and Bob can superimpose a multiplicative channel, i.e., $\mathbf{h}^T_{RB}\mathbf{\Phi}\mathbf{h}_{AR}$, to counteract the influence of the illegal multiplicative channel, i.e., $\mathbf{h}^T_{EB}\mathbf{\Phi}_E\mathbf{h}_{AE}$, on the wireless signal. The challenge of this defense strategy is that the legitimate users need to know the RIS switch speed model of attack. Deep learning and reinforcement learning are powerful tools to learn the control strategy of RIS in a model-free approach, thereby confronting the attack strategy. Furthermore, multi-RIS can be used by legitimate users to simultaneously resist this attack and improve the PLKG rate in a static indoor environments.
  \item \textbf{Countermeasure 2}: The RIS flip attack causes a rapid variation of the wireless signal's amplitude and phase, which makes bidirectional channel probing fail. In this case, we can choose more robust features of the wireless channel to generate the raw key. Specifically, although the bidirectional channel probing for RSS or CSI is different in the presence of RIS flip attack, the legitimate users share the physical space and the scatterers. In this case, we can deploy multiple antennas on the legitimate users and extract the spatial reciprocity features to generate the raw key. For example, the delay and angle of each path are bidirectional reciprocal and can be captured based on Newton orthogonal matching pursuit (NOMP) algorithm. Then, we can robustly generate a key without considering the damage caused by the RIS flip attack. Since the spatial reciprocity features are often slow-varying, we have to deploy RISs for legitimate users to artificially create time-varying scatter environment or design high resolution quantization approaches to increase the key generation rate.
  \item \textbf{Countermeasure 3}: From the experiment of channel reciprocity, we saw that the addition of RIS improves the SNR of PLKG. Inspired by this, we can detect the RIS attack by monitoring the variation of RSS. Specifically, we have to obtain the statistical property of RSS in both secure and attack states. Then, a binary hypothesis test can be used to decide whether there is a RIS flip attack in the current wireless environment. When the attack occurs, the Alice-Bob pair stops the PLKG process to reduce the unnecessary resource consumption. However, this countermeasure is a passive defense strategy which can detect the existence of RIS flip attack but cannot eliminate such attack to achieve the fundamental PLKG purposes. Therefore, to transform passive defense strategy to active defense strategy, we can use a fine-grained wireless link signature to trace back the physical location of the attack and then mitigate it.
\end{itemize}

\section{Conclusions and Future Directions}
In this article, we have provided an overview of the promising RIS-aided PLKG technology for achieving a high key generation rate in a static indoor environment. Notably, a RIS can increase the SNR, thereby improving the channel reciprocity. Moreover, the RIS switch speed affects the bit mismatch rate, but the overall key generation rate is 15-fold higher than that without RIS. Next, to achieve a high bit generation rate, we find that it is better to deploy RIS near the transceiver side. Furthermore, the investigation of RIS attack-defense for RIS-aided PLKG is important. Based on our review, we identify several open challenges for RIS-aided PLKG in the following.
\begin{itemize}
  \item \textbf{RIS and/or multi-RIS optimization and configuration in RIS-aided PLKG}: Although the RIS-aided PLKG can increase the channel fluctuation and improve key generation rate, the condition of the wiretap channel is also changed. When the multipath scattering and interference are not rich enough, i.e., in an empty room, there is a strong correlation between the legitimate channel and wiretap channel even their protected zone is more than half a wavelength away. One possible direction of investigation is to optimize the RIS and/or multi-RIS to reconstruct the channel environment, thereby obtaining the achievable key secrecy rate.
  \item \textbf{RIS-aided PLKG in frequency division duplex (FDD) system}: Different from TDD communication system, FDD communication system lacks reciprocity on the frequency related channel parameters between uplink and downlink. One possible solution is to control RIS and/or multi-RIS to artificially create a large number of frequency independent reciprocal channel parameters to generate the secret key, i.e., multipath delay, angle of arrival and/or departure, etc. In addition, for massive MIMO FDD communication systems, the RIS-aided PLKG can be integrated with CSI feedback, which is a promising topic for ICAS.
  \item \textbf{Defense strategy of PLKG for RIS active attacks}: RIS active attacks can realize a reconfigurable smart and programmable jamming and eavesdropping channel. Then, by injecting the malicious signal to manipulate the channel probing, the information of the secret key can be partially or completely leaked. Particularly, given the passive components of RIS, these attacks are often highly covert. On these lines, the countermeasures for RIS active attacks in PLKG are still largely open.
  \item \textbf{RIS-aided PLKG in ubiquitous 6G wireless networks}:
      Except for the static indoor scenario, RIS-aided PLKG can be combined with ubiquitous wireless networks, i.e., airborne, autonomous vehicle networks, and so on. This provides new degrees of freedom to further jointly optimize the RIS and network mobility, thereby improving the performance of key generation rate and data transmission rate, simultaneously.
\end{itemize}





















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