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Anti-Malicious ISAC Using Proactive Monitoring

Zonghan Wang, Zahra Mobini, Hien Quoc Ngo, Michail Matthaiou

TL;DR

This paper investigates proactive monitoring to mitigate malicious activities in integrated sensing and communication (ISAC) systems and derives closed-form expressions for the sensing signal-to-interference-noise ratio (SINR), as well as the received SINR at the UEs and overheard SINR at the monitor.

Abstract

In this paper, we investigate proactive monitoring to mitigate malicious activities in integrated sensing and communication (ISAC) systems. Our focus is on a scenario where a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture is exploited by malicious actors. Malicious actors use multiple access points (APs) to illegally sense a legitimate target while communicating with users (UEs), one of which is suspected of illegal activities. In our approach, a proactive monitor overhears the suspicious UE and simultaneously sends a jamming signal to degrade the communication links between the APs and suspicious UE. Simultaneously, the monitor sends a precoded jamming signal toward the legitimate target to hinder the malicious sensing attempts. We derive closed-form expressions for the sensing signal-to-interference-noise ratio (SINR), as well as the received SINR at the UEs and overheard SINR at the monitor. The simulation results show that our anti-malicious CF-mMIMO ISAC strategy can significantly reduce the sensing performance while offering excellent monitoring performance.

Anti-Malicious ISAC Using Proactive Monitoring

TL;DR

This paper investigates proactive monitoring to mitigate malicious activities in integrated sensing and communication (ISAC) systems and derives closed-form expressions for the sensing signal-to-interference-noise ratio (SINR), as well as the received SINR at the UEs and overheard SINR at the monitor.

Abstract

In this paper, we investigate proactive monitoring to mitigate malicious activities in integrated sensing and communication (ISAC) systems. Our focus is on a scenario where a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture is exploited by malicious actors. Malicious actors use multiple access points (APs) to illegally sense a legitimate target while communicating with users (UEs), one of which is suspected of illegal activities. In our approach, a proactive monitor overhears the suspicious UE and simultaneously sends a jamming signal to degrade the communication links between the APs and suspicious UE. Simultaneously, the monitor sends a precoded jamming signal toward the legitimate target to hinder the malicious sensing attempts. We derive closed-form expressions for the sensing signal-to-interference-noise ratio (SINR), as well as the received SINR at the UEs and overheard SINR at the monitor. The simulation results show that our anti-malicious CF-mMIMO ISAC strategy can significantly reduce the sensing performance while offering excellent monitoring performance.
Paper Structure (6 sections, 3 theorems, 21 equations, 3 figures)

This paper contains 6 sections, 3 theorems, 21 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Performance Analysis
  4. Simulation Results
  5. Conclusion
  6. Proof of Proposition \ref{['Theorem2']}

Key Result

Proposition 1

The received SINR for UE $1$ at the monitor is given by eq:SINR_J, shown on the top of the next page, where

Figures (3)

  • Figure 1: Anti-malicious CF-mMIMO ISAC design using an FD proactive monitor.
  • Figure 2: MSP and SDP versus $\theta_{\mathtt{pm},t}$.
  • Figure 3: SDP versus $N_\mathrm{pm}$.

Theorems & Definitions (4)

  • Proposition 1
  • proof
  • Proposition 2
  • Proposition 3