Wireless Communication, Sensing, and REM: A Security Perspective

Abstract

The diverse requirements of next-generation communication systems necessitate awareness, flexibility, and intelligence as essential building blocks of future wireless networks. The awareness can be obtained from the radio signals in the environment using wireless sensing and radio environment mapping (REM) methods. This is, however, accompanied by threats such as eavesdropping, manipulation, and disruption posed by malicious attackers. To this end, this work analyzes the wireless sensing and radio environment awareness mechanisms, highlighting their vulnerabilities and provides solutions for mitigating them. As an example, the different threats to REM and its consequences in a vehicular communication scenario are described. Furthermore, the use of REM for securing communications is discussed and future directions regarding sensing/REM security are highlighted.INDEX TERMS 5G, 6G, cryptography, joint radar and communication (JRC), physical layer security, radio environment mapping (REM), REM security, sensing security, vehicle-to-everything (V2X) communication, wireless sensing, WLAN sensing.As explained earlier, our surroundings contain a myriad of radio signals. These signals can be leveraged to extract information about the environment itself and the users/devices within. As such, a malicious node might have the following motivations when attacking a (communication and/or sensing) wireless network:• Confidentiality Violation: The malicious node tries to intercept either the communication or characteristics of the legitimate nodes. In the context of sensing, this information may include a user's location, mobility pattern, social trends, etc. • Degradation: The malicious node attempts to impair the communication link and/or sensing capabilities of the legitimate parties. • Exploitation: The malicious node tries to exploit the communication and/or sensing for its own benefit. This may include learning and then manipulating the legitimate communication (or sensing) to acquire resources for itself.Corresponding to the aforementioned intentions of the malicious attackers, the network strives to guarantee that its nodes and users are protected. This includes ensuring no illegitimate access to the information/transmission is allowed, the data shared between legitimate nodes is accurate, and there is no disruption of the services offered by the network [16]. B. SECURITY THREATS -COMMUNICATION VS SENSINGThe broadcast nature of wireless communication (or signals in general) renders it susceptible to various security threats. Given below is an overview of how these threats compare for communication and sensing aspects.

Flexible Physical Layer Security for Joint Data and Pilots in Future Wireless Networks

“…Using the pilots P (k) at k p , the precoded CFR Habe (k) = H ae (k)H ap ab (k) is estimated as described in (5). As seen from ( 15), eavesdropper will not be able to correctly estimates the channel because of unknown randomness caused by all-pass component (H ap ab (k)) of legitimate channel in (15).…”

“…The first goal of PLS algorithms is to degrade the data decoding capability of non-legitimate nodes compared to the legitimate node by exploiting different properties of the wireless channel. While the second goal is to enforce poor channel estimation at the eavesdropper/attacker, which will degrade the signal recovery capability at the attacker [5]- [7].…”

“…The increase in the number of wireless communicationbased applications with varying requirements motivates the need for adaptive and flexible security designs [5]. Inspired by this motivation, in this section, we firstly present the channel decomposition concept, and then we propose novel algorithms that are capable of providing adaptive and flexible security.…”

“…Using the pilots P (k) at k p , the precoded CFR Habp (k) = H ab (k)H ap ab (k) is estimated as described in (5). Afterwards, Hab (k) is estimated as Hab (k) = Hmin ab (k) ( Hap ab ) 2 .…”

Privacy for 5G-Supported Vehicular Networks

“…A large-scale analysis of vehicular data can lead to valuable insights that address public transportation challenges and come across new scientific discoveries [43]. However, as a nontrivial combination and extension built upon existing infrastructures and technologies, it is inevitable that some issues remain in 5GVNs, especially privacy issues [44], [45], [46], [47], [48], [49], [50], [51]. SPs may have malicious or mischievous employees who steal users' data and sell them to the black market.…”