In single-input single-output (SISO) systems, transmitting the expected signals with artificially generated noise can effectively enhance the physical layer security (PLS). However, the traditional noise suppression at the intended receivers will increase algorithm complexity, particularly over multi-path fading channels, which poses challenges for some massive Internet-of-Things (IoT) networks that take advantage of low-cost and resource-constrained nodes. In this paper, we design a novel self-noise (SN) waveform for transmission security. The transmitter stacks the signal segments periodically, and the additive SN in the current signal period derives from the previous signal periods. Based on this, SN can be completely suppressed at intended receivers by differential operations between two adjacent signal periods. Both analysis and simulation results confirm that the proposed scheme can significantly reduce the complexity of noise suppression and its performance is not affected by multi-path channel environments. Besides, the proposed scheme can effectively worsen the signal-to-noise ratio (SNR) of eavesdroppers while having negligible impacts on intended receivers.
In single-input single-output (SISO) systems, transmitting the expected signals with artificially generated noise can effectively enhance the physical layer security (PLS). However, the traditional noise suppression at the intended receivers will increase algorithm complexity, particularly over multi-path fading channels, which poses challenges for some massive Internet-of-Things (IoT) networks that take advantage of low-cost and resource-constrained nodes. In this paper, we design a novel self-noise (SN) waveform for transmission security. The transmitter stacks the signal segments periodically, and the additive SN in the current signal period derives from the previous signal periods. Based on this, SN can be completely suppressed at intended receivers by differential operations between two adjacent signal periods. Both analysis and simulation results confirm that the proposed scheme can significantly reduce the complexity of noise suppression and its performance is not affected by multi-path channel environments. Besides, the proposed scheme can effectively worsen the signal-to-noise ratio (SNR) of eavesdroppers while having negligible impacts on intended receivers.
In this paper, we propose a reference-free artificial noise (AN) waveform design and cancellation scheme for single-input-single-output (SISO) wireless secure transmission. First, a system structure with the aid of AN is presented, where we provide a specific method extracting the sign information of each symbol from the desired signal (DS) to form AN at the transmitter. Then, we notice that the legitimate receiver enable to reconstruct the AN from the received signal by almost the same method as the transmitter without AN reference, which reduces the system complexity in practice. Subsequently, the AN cancellation performance and the secrecy capacity at the receiver are analyzed, and their upper bounds are also derived. Finally, simulation results confirm that the proposed reference-free AN design and cancellation scheme can effectively suppress the AN at legitimate receiver, while degrades the signal to noise ratio (SNR) at eavesdropper seriously, which provides impressive secure performance in terms of secrecy capacity.
An increasing number of devices exchanges data via the wireless medium into large scale, decentralized, and heterogeneous networks, leading to the concept of Internet of Things (IoT). This PhD work proposes a new practical scheme to perform physical layer security (PLS) in this IoT context.Wireless communications in IoT networks are prone to passive eavesdropping. Therefore, this work introduces a scheme that guarantees the reliability of a communication from a transmitter (Alice) towards a legitimate user (Bob), while preventing multiple passive eavesdroppers (Eve) to correctly decode the data in a worst-cases scenario, thereby considering i) Eve to be noiseless, ii) Eve equipped with an arbitrarily large number of antennas, iii) Alice imperfectly estimating Bob's channel state information (CSI). To do so, a frequency domain time-reversal (TR) precoder is proposed using orthogonal frequency-division multiplexing (OFDM) and artificial noise (AN) injection. The scheme exploits the frequency diversity selective behaviour in multipath channels, and can be incorporated into existing standards, such as in LTE or 5G networks. Different configurations are investigated, namely, single-input single-output (SISO), multiple-input single-output (MISO), and single-input multiple-output (SIMO) systems.The handshake procedures between Alice and Bob are described, both in time-division duplexing (TDD) and frequency-division duplexing (FDD) systems, highlighting the amount of CSI acquisition at Eve, which influences the secrecy performances. Depending on the handshake procedures, different decoding structures are investigated at Eve for each system configuration, considering a block-fading environment. Closed-form approximations of the signal-to-noise ratio required at Bob and the maximal CSI error that can be made at Alice, in order to guarantee a communication ergodic secrecy rate (ESR), are derived. Furthermore, the optimal amount of AN energy to inject, as well as the maximal number of eavesdropper's antennas allowed, considering imperfect CSI, are also given as closed-form expressions. A trade-off on the choice of the spreading factor of the TR precoder is established between maximizing the ESR and decreasing the percentage of outage, leading to information leaked to Eve. Finally, thanks to these results, Alice can be a-priori aware of the ESR over which she can establish a secure communication, assuming worst-case assumptions regarding the eavesdroppers. The PLS scheme presented in this PhD work therefore emerges as a promising solution to secure wireless communications in IoT networks.
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.
Establishing robust cybersecurity for Internet of Things (IoT) ecosystems poses significant challenges for system operators due to IoT resource constraints, trade-offs between security and performance, diversity of applications, and their security requirements, usability, and scalability. This article introduces a physical-layer security (PLS) approach that enables IoT devices to maintain specified levels of information confidentiality against wireless channel eavesdropping threats. This work proposes applying PLS active defense mechanisms utilizing spectrum-sharing schemes combined with fair scheduling and power management algorithms to mitigate the risk of eavesdropping attacks on resource-constrained IoT environments. Specifically, an IoT device communicating over an insecure wireless channel will utilize intentional noise signals transmitted alongside the actual IoT information signal. The intentional noise signal will appear to an eavesdropper (EVE) as additional noise, reducing the EVE’s signal-to-interference-plus-noise ratio (SINR) and increasing the EVE’s outage probability, thereby restricting their capacity to decode the transmitted IoT information, resulting in better protection for the confidentiality of the IoT device’s transmission. The proposed communication strategy serves as a complementary solution to existing security methods. Analytical and numerical analyses presented in this article validate the effectiveness of the proposed strategy, demonstrating that IoT devices can achieve the desired levels of confidentiality.
The Internet of Things (IoT) is one of the most promising technological revolutions. However, the IoT is inherently vulnerable to attacks. In this paper, we propose to enhance the physical layer security of the IoT by using a technique called information passing and interference addition. In the physical layer security analysis, the achieved secrecy rate has an inverse relationship with the intruder's distance to the legitimate transmitter. Our work is, first, to increase the achievable secrecy rate by letting the legitimate transmitter find an alternative route to the fusion center that could achieve a higher secrecy rate than the direct route. Hence, the legitimate transmitter must not disclose the actual information to the fusion center if there is an eavesdropper located between them, and rather must pass its information to another node via a directional antenna to concentrate the power towards the intended receiving node. Passing the information to the nearest node must happen under the condition that there is no intruder in between. Second, we use simultaneous multi-stream beamforming transmission to strengthen the legitimate receiver's signal-to-noise ratios and at the same time poison the eavesdropper channel with interference. Third, we disclose false information to the intruder. We created the system design and investigated the secrecy analysis of that system. Also, we discussed the use of directional antennas on the legitimate nodes. The result showed that using directional antennas would increase the probability of having at least one secure connection coming out of the node to the other legitimate nodes. Moreover, the results showed that using the simultaneous multi-stream beamforming improved the legitimate channel's achievable secrecy rate and that it has superiority over the directional antenna effect.
For the cases of imperfect channel state information (CSI) on the main channel in the physical-layer-security communications, we study the artificial-noise-aided robust transmit designs. We consider two optimization problems for the multi-input single-output single-antenna eavesdropper (MISOSE) and multi-input multi-output multi-antenna eavesdropper (MIMOME) systems. Under the deterministic uncertainty model (DM), the optimization problem aims to minimize the total transmit power while guaranteeing the probabilistic constraint for the signal to interference and noise ratios (SINR) at the eavesdropper, and the SINR constraint at the legitimate receiver. For the stochastic uncertainty model (SM), the optimization problem is also to minimize the total transmit power under the probabilistic constraints for the SINRs at the legitimate receiver and the eavesdropper. These problems are hard to be solved directly. We transform the optimization problems into a single-variable optimization problem with solving a sequence of semidefinite programs. By comparison, we propose another low-complexity method based on the expectation value. Finally, we verify the performance for the proposed robust transmit designs by the simulation results.