The industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issue of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this article is on establishing efficient privacy in an IIoT-multiple-input–multiple-output–multiple-antenna eavesdropping communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as optimal counter-eavesdropping channel approximation technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times the number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise.
The industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issue of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this article is on establishing efficient privacy in an IIoT-multiple-input–multiple-output–multiple-antenna eavesdropping communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as optimal counter-eavesdropping channel approximation technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times the number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise.
With the exponential increase of interconnected communicating devices which make up the Internet of Things (IoT), securing the network transmission and the fifth generation (5G) systems which is the bedrock for IoT concept actualization is becoming more and more challenging. One of the major attacks which poses a great risk to data transmission is the eavesdropper (Eve) attack which occurs in both single input, single output (SISO), multiple input and multiple output (MIMO) systems. Thus, in this study, our focus is to establish a secured connection in a multiple-antenna transmission when the channel state information (CSI) of Eve is unknown to the network users. Our model comprises a secure wireless communication standard where Eve performs either optimal matched filtering (OMF) or a basic matched filtering (BMF) while the transmitting IoT node employs smart jamming strategy in order to compromise the activities of Eve. With respect to this and in attempt to realize maximum privacy, we examined the design of optimal jamming parameters. In the end, the numerical analysis of our investigation indicates that a substantial privacy advantage is achievable while utilizing only full-duplex jamming against using artificial noise from the transmitter only. However, a joint performance of both results shows a higher privacy improvement.
Security and privacy are vital challenges in Industrial Internet of Things (IIoT) systems due to the inherent broadcast property of industrial wireless communication. Therefore, how to improve the security of smart devices in IIoT systems has attracted widespread attention. In this article, a novel security optimization scheme for space shift keying (SSK)-based IIoT systems is proposed by applying secrecy enhancing techniques in the physical layer. Specifically, two methods are adopted in the proposed optimization scheme. First, the sender transmits artificial noise (AN) to interfere with the eavesdropper, while the legitimate receiver can eliminate AN. Second, the sender can select optimal antennas based on the proposed minmax criteria to transmit AN and information, which further promotes the security of industrial wireless communication. The optimal antenna selection pattern increases the difference of the average mutual information between the legitimate receiver and the eavesdropper. In addition, the proposed minmax antenna selection approach only requires the channel state information of the legitimate receiver, which satisfies the application scenario of unknown eavesdropper attacks in IIoT. Finally, we adopt a deep learning-based maximum likelihood (ML) detection algorithm to detect the optimal antenna index and analyze the bit error rate (BER) and secrecy rate (SR) of the proposed optimization scheme. Simulation results show that the proposed scheme can achieve similar SR improvements and superior BER performance compared with the state-of-the-art scheme, namely the signal-leakage-to-noise ratio (SLNR) scheme. The BER performance of the proposed scheme achieves a maximum of 100 times superior to that of the SLNR scheme when the signal-to-noise ratio (SNR) is 27 dB. Moreover, the proposed scheme outperforms the traditional antenna selection scheme (random selection) in terms of BER and SR.
In this article, we consider physical-layer security (PLS) of the Internet-of-Things (IoT) communications based on hybrid automatic repeat request (HARQ) protocols in multiple-input single-output single-eavesdropper (MISOSE) channels. Owing to the reciprocity of wireless channels, a negative acknowledge frame can be used to obtain the channel state information (CSI) from a source with multiple antennas to an IoT destination device, and this CSI of a legitimate link is used for beamforming for the next transmission round (TR). Therefore, a higher secrecy rate can be expected from retransmissions with beamforming based on the CSI as compared to the first TR without CSI. To evaluate the secrecy throughput under time-invariant and time-varying channels, connection and secrecy outage probabilities (OPs) are derived. By exploiting the characteristics of the OPs, we suggest an approximated secrecy throughput. We then consider the optimization of the approximated secrecy throughput considering the rate parameters for legitimate and eavesdropping links. With an alternating optimization approach, an algorithm to find convergent system parameters, i.e., the rates for legitimate and eavesdropping links, is proposed. Moreover, an asymptotic analysis with the large number of transmit antennas indicates an interesting behavior of the secrecy rate with regard to the number of TRs. The analytical results are verified by rigorous numerical simulations and they can serve as a useful guideline to those involved in the design of secure IoT communication systems with HARQ in MISOSE channels.
Physical layer security is a promising way to secure the wireless communications in the Internet of Things (IoT). Motivated by the fact that the limited feedback resources in the IoT network would degrade the secrecy advantage of the multiple-antenna technique, we attempt to investigate the problem of how many transmit antennas should be utilized to perform secure communications. In particular, we consider the heterogeneous IoT downlink network and design a multiuser secure transmission scheme. In this scheme, the zero-forcing beamforming technique is adopted to serve the IoT legitimate users, and the remaining spatial freedoms are utilized to send artificial noise (AN) for confusing the passive eavesdroppers. Given the secrecy outage constraints, we derive the closed-form expression for the network secrecy throughput and formulate a non-convex optimization problem with multiple parameters, e.g., the number of transmit antennas, the wiretap codes, the feedback bits allocation strategy, and the power allocation ratio between the information bearing signal and the AN. To effectively tackle this problem, we develop an optimization framework involving the block coordinate descent algorithm and the 1-D search method. Simulation results validate the effectiveness of our proposed optimization framework and show that the optimal number of transmit antennas increases as the secrecy outage constraints become stricter, or the feedback resources become scarcer.
Physical-layer security (PLS) has become an intriguing technology to address eavesdropping issues in Internet of Things (IoT) systems owing to its low complexity and latency. As wireless communication technology and computing capabilities advance by leaps and bounds, eavesdroppers have enhanced eavesdropping capabilities. They can analyse the environment and move to find better locations for eavesdropping. To mitigate the significant impact of multiple intelligent eavesdroppers extremely close to the device-constrained transmitters in the uplink multiple-input-multiple-output IoT systems, we exploit time-domain artificial noise (AN) in the PLS design and formulate a game problem against the intelligent eavesdroppers. First, we derive the optimal closed-form solution for eavesdroppers and propose a low complexity difference of the convex (LCDC) algorithm to obtain the optimal strategy of the legitimate users and the access point equipped with a zero-forcing receiver. For general linear receivers, we propose a successive convex approximation (SCA) algorithm for the game problem. Simulations are conducted to verify the convergence and effectiveness of our proposed algorithms. The system secrecy performance of the time-domain AN is much better than that of the frequency-domain AN with single-antenna transmitters.
With the great-leap-forward development of Internet of Things (IoT), it is extremely important to study secure wireless transmission for IoT systems. A cooperative jamming (CJ) strategy has been extensively studied to enhance physical layer security in IoT systems. However, CJ's secrecy performance has not been well studied in the scenario of collusive eavesdroppers. In this paper, we propose a CJ scheme for IoT systems to fight against multiple passive and collusive eavesdroppers of unknown channel state information. Considering nodes with multi-antenna in an IoT system, we design beamforming vectors to maximize signal-to- interference-plus-noise ratio (SINR) of the wireless link from a controller (transmitter) to an actuator (receiver). Under the worst-case assumption that eavesdroppers can aggregate all signals to enhance their eavesdropping abilities, we derive the closed- form expressions of the secrecy outage probability (SOP) with and without CJ. In addition, we employ a strict mathematical asymptotic analysis to provide insights into the effects of various system parameters on the SOP. Numerical results verify that the CJ scheme can effectively prevent eavesdropping and the effects of system parameters on SOP is consistent with theoretical analytical results.