In recent times, security and privacy at the physical (PHY) layer has been a major issue of several communication technologies which comprise the internet of things (IoT) and mostly, the emerging fifth-generation (5G) cellular network. The most real-world PHY security challenge stems from the fact that the passive eavesdropper's information is unavailable to the genuine source and destination (transmitter/receiver) nodes in the network. Without this information, it is difficult to optimize the broadcasting parameters. Therefore, in this research, we propose an efficient sequential convex estimation optimization (SCEO) algorithm to mitigate this challenge and improve the security of physical layer (PHY) in a three-node wireless communication network. The results of our experiments indicate that by using the SCEO algorithm, an optimal performance and enhanced convergence is achieved in the transmission. However, considering possible security challenges envisaged when a multiple eavesdropper is active in a network, we expanded our research to develop a swift privacy rate optimization algorithm for a multiple-input, multiple-output, multiple-eavesdropper (MIMOME) scenario as it is applicable to security in IoT and 5G technologies. The result of the investigation show that the algorithm executes significantly with minimal complexity when compared with nonoptimal parameters. We further employed the use of rate constraint together with self-interference of the full-duplex transmission at the receiving node, which makes the performance of our technique outstanding when compared with previous studies.
In recent times, security and privacy at the physical (PHY) layer has been a major issue of several communication technologies which comprise the internet of things (IoT) and mostly, the emerging fifth-generation (5G) cellular network. The most real-world PHY security challenge stems from the fact that the passive eavesdropper's information is unavailable to the genuine source and destination (transmitter/receiver) nodes in the network. Without this information, it is difficult to optimize the broadcasting parameters. Therefore, in this research, we propose an efficient sequential convex estimation optimization (SCEO) algorithm to mitigate this challenge and improve the security of physical layer (PHY) in a three-node wireless communication network. The results of our experiments indicate that by using the SCEO algorithm, an optimal performance and enhanced convergence is achieved in the transmission. However, considering possible security challenges envisaged when a multiple eavesdropper is active in a network, we expanded our research to develop a swift privacy rate optimization algorithm for a multiple-input, multiple-output, multiple-eavesdropper (MIMOME) scenario as it is applicable to security in IoT and 5G technologies. The result of the investigation show that the algorithm executes significantly with minimal complexity when compared with nonoptimal parameters. We further employed the use of rate constraint together with self-interference of the full-duplex transmission at the receiving node, which makes the performance of our technique outstanding when compared with previous studies.
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.
Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications. \n \nThis thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm.
Internet of Things (IoT) is rapidly spreading and reaching a multitude of different domains, since the fifth generation (5G) wireless technologies are the key enablers of many IoT applications. It is hence apparent that the broadcast nature of IoT devices makes data security unprecedentedly critical. Compared with traditional cryptography algorithms, which cannot cater for the features of IoT devices characterized by the severe limits in terms of energy, computation and storage capabilities, physical layer security (PLS) has been regarded as a promising solution to facilitate secure communications by exploiting the intrinsic randomness of the wireless medium. However, most of previous works assumed that all devices are static, and the impact of mobility on PLS deserves further investigation. In this paper, applying two types of random mobile models, i.e., the models of Random WayPoint (RWP) and Random Direction (RD), we study the impact of mobility on PLS in a scenario with three types of wireless devices (i.e., a destination, multiple interferers and an eavesdropper). Specifically, we establish an analytical framework for secrecy transmission capacity (STC), a fundamental metric in the study of PLS, under RWP and RD models. To the best of our knowledge, this is the first paper to derive STC and present the condition to achieve a positive STC with the consideration of mobility. We conclude that the RWP mobile destination can achieve a higher STC than that achievable in RD mobile and static scenarios, while RWP mobile eavesdropper is a challenging scenario to obtain a positive STC. Therefore, we propose an effective secrecy improvement strategy for the latter. Simulation validates the theoretical analyses.
Wireless communications are naturally exposed to important security risks due to their broadcast nature. In contrast with wired communications, no physical medium is isolating the transmission surroundings to a specific path. Traditional methods to reach the goals of communications security rely on algorithms at protocol stack level, which are based on mathematically derived cryptographic protocols. With the tremendous proliferation of wireless devices, and the increase in computational power available for decryption, traditional cryptographic measures may be compromised. To protect communications at the lowest level, physical layer (PHY) security methods have been proposed recently. These techniques aim to ensure secure communications by exploiting the unique characteristics of the physical medium between communicating nodes, effectively reducing the decryption capabilities of an eavesdropper to intercept transmissions. This thesis explains within a general framework the most important PHY layer security schemes. First we explore approaches which utilize the knowledge of the unique channel between communicating nodes to distort a transmitted signal with various kinds of randomizations, in such a way that the intended receivers are unaffected by the distortion, but any other observer will be exposed to it. These techniques, however, introduce several trade-offs between transmission resources and the effectiveness in achieving secrecy. Of particular interest for this investigation is a class of methods which can be referred to as "security enhancing techniques", in which minimal priors about the potential eavesdroppers locations and capabilities are assumed. In this thesis, a wave-based approach to the communication secrecy problem is introduced. An interesting perspective is provided by studying the relation between electromagnetic theory and information theory, the so-called electromagnetic information theory. This allows to properly identify the unavoidable limitations imposed by the laws of electromagnetics to our ability to communicate securely. The proposed model can be extended to the analysis of different signal distortion methodologies and different channel conditions. The resulting approach is highly relevant to the study of PHY layer security methods since it is physically fundamental as well as architecture independent, which leads to a consolidating point of view of the secrecy problem. In addition, the thesis presents computer simulation results which shed insight on the security possibilities and limitations of three different security scenarios, which are termed MISO (multiple-input, single-output) (with single eavesdropper), MISOME (multiple-input, single-output, multiple eavesdropper), and MIMOME (multiple-input, multiple-output, multiple-eavesdropper).
The Internet of Things (IoT) depicts a bright future, where any devices having sensorial and computing capabilities can interact with each other. Among all existing technologies, the techniques for the fifth generation (5G) systems are the main driving force for the actualization of IoT concept. However, due to the heterogeneous environment in 5G networks and the broadcast nature of radio propagation, the security assurance against eavesdropping is a vital yet challenging task. In this paper, we focus on the transmission design for secure relay communications in IoT networks, where the communication is exposed to eavesdroppers with unknown number and locations. The randomize-and-forward relay strategy specially designed for secure multi-hop communications is employed in our transmission protocol. First, we consider a single-antenna scenario, where all the devices in the network are equipped with the single antenna. We derive the expression for the secrecy outage probability of the two-hop transmission. Following this, a secrecy-rate-maximization problem subject to a secrecy-outage-probability constraint is formulated. The optimal power allocation and codeword rate design are obtained. Furthermore, we generalize the above analyses to a more generic scenario, where the relay and eavesdroppers are equipped with multiple antennas. Numerical results show that the proper use of relay transmission can enhance the secrecy throughput and extend the secure coverage range.
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.