Deep Transfer Learning for Physical Layer Security in Wireless Communication Systems

Node cooperation approaches improve physical layer security in wireless networks without applying data encryption. Selecting the appropriate nodes to cooperate as relays or friendly jammers, to degrade eavesdropper's link, is a challenging problem which has attracted a lot of attention in recent years. Various approaches, based on conventional exhaustive search, have been suggested for optimal relay-jammer selection and power allocation in the literature. The complexity of these methods is proportional to the number of intermediate nodes. As the number of intermediate nodes exceeds a specific bound, conventional exhaustive search appears infeasible due to high computational complexity. In this paper, we propose a novel approach based on genetic algorithm (GA), for relay-jammer selection, power allocation and computational complexity reduction. Our system model consists of a source-destination pair, one eavesdropper and a number of intermediate nodes which cooperate as relays or friendly jammers. Considering the achievable secrecy rate as the cost function for GA optimization, the proposed algorithm is performed in two steps. First, we determine the desired channel gains that maximize the secrecy rate using GA. In the second step, nodes for which the corresponding channel gains have the smallest deviation from the obtained values in the first step, will be chosen as relays or jammers. We compare the proposed method with two kinds of conventional exhaustive search techniques one with fixed and the other with adaptive power allocated to jammers. Simulation results show that the proposed method reduces computational complexity while attaining a secrecy rate close to conventional techniques. Also the power which is allocated to jammers during each phase of transmission will be declined using GA optimization. Despite of conventional exhaustive search, the complexity of the proposed algorithm is almost the same as the number of cooperative nodes increases. So, the proposed method will outperform the previous works in terms of simplicity, efficiency and security.

Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Recently, batch-mode active learning, where a set of samples are selected concurrently for labeling, based on their collective merit, has attracted a lot of attention. The objective of batch-mode active learning is to select a set of informative samples so that a classifier learned on these samples has good generalization performance on the unlabeled data. Most of the existing batch-mode active learning methodologies try to achieve this by selecting samples based on certain criteria. In this article we propose a novel criterion which achieves good generalization performance of a classifier by specifically selecting a set of query samples that minimize the difference in distribution between the labeled and the unlabeled data, after annotation. We explicitly measure this difference based on all candidate subsets of the unlabeled data and select the best subset. The proposed objective is an NP-hard integer programming optimization problem. We provide two optimization techniques to solve this problem. In the first one, the problem is transformed into a convex quadratic programming problem and in the second method the problem is transformed into a linear programming problem. Our empirical studies using publicly available UCI datasets and two biomedical image databases demonstrate the effectiveness of the proposed approach in comparison with the state-of-the-art batch-mode active learning methods. We also present two extensions of the proposed approach, which incorporate uncertainty of the predicted labels of the unlabeled data and transfer learning in the proposed formulation. In addition, we present a joint optimization framework for performing both transfer and active learning simultaneously unlike the existing approaches of learning in two separate stages, that is, typically, transfer learning followed by active learning. We specifically minimize a common objective of reducing distribution difference between the domain adapted source, the queried and labeled samples and the rest of the unlabeled target domain data. Our empirical studies on two biomedical image databases and on a publicly available 20 Newsgroups dataset show that incorporation of uncertainty information and transfer learning further improves the performance of the proposed active learning based classifier. Our empirical studies also show that the proposed transfer-active method based on the joint optimization framework performs significantly better than a framework which implements transfer and active learning in two separate stages.

Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.

Wireless Local Area Network (WLAN) is an alternative in overcoming cabling problems in a local network. Often wireless network security that is installed still uses vendor default settings such as SSID, IP Address, remote management, DHCP enabled, frequency channels, without encryption, even user or password for wireless administration. How does a strong WLAN security system work? The most common security system applied to wireless networks at this time is starting from securing access points by applying the MAC Filtering concept, using WPA / WPA2-PSK security keys, and RADIUS server authentication. To see the quality of wireless LAN network security, how do you analyze the test of the existing security system in the network. The method that can be used in evaluating wireless networks is by testing the system by simulating forms of attacks on wireless networks with the Penetration Testing method. By carrying out 4 stages of research using the penetration testing method (i) the planning stage, (ii) the discovery stage, (iii) the attack stage and (iv) the report stage, from the attack stage (Cracking The Encryption, Bypassing MAC Address, Attacking The Infrastructure and MITM ) using Kali Linux got the results of four types of attacks carried out, only one of which was failed, namely the cracking attack type of the encryption on the RADIUS server due to using captive portal authentication

In this paper, we present our study of an Eigenvector-based artificial noise-based jamming technique developed to provide increased wireless physical layer security in transmit-receive diversity systems and analyze the impact of channel estimation errors on system performance. Our simulation results showed that with knowledge of perfect channel state information, the technique provided secrecy capacity of approximately 7 bits/s/Hz for a normalized transmit power of 25 dB for a variety of transmit, receive, and eavesdropper configurations. We also describe a novel method to simulate generalized channel state information estimation errors. While other publications neglect the impact of these estimation errors, our simulations show that the secrecy capacity decreased rapidly as the channel estimation errors increased. For instance, at 25% error the secrecy capacity of the jamming technique was only slightly better than the non-jamming case. Our paper also outlines upcoming research efforts to further explore error sensitivity and channel state temporal stability through experimentation.

Large‐scale multiple‐input multiple‐output (MIMO) system is considered one of promising technologies to realise next‐generation wireless communication system (5G). So far, channel estimation problem is a big obstacle to develop large‐scale MIMO system design due to high computational complexity and curse of dimensionality, which are caused by the long delay spread as well as a large number of antennas. Hence, devising any low‐complexity channel estimation method could promote the successful development of the large‐scale MIMO system. Due to the fact that, large‐scale MIMO channels often exhibit sparse or/and cluster‐sparse structure, in this study, the authors propose an effective low‐complexity large‐scale MIMO channel estimation method by using affine combination of sparse adaptive filtering filters. First, problem formulation and standard affine combination of adaptive least mean square (LMS) filters are introduced. Then they propose an effective affine combination method with two sparse LMS filters and design an approximate optimum affine combiner according to stochastic gradient search method. Later, to verify the proposed algorithm for large‐scale MIMO channel estimation, both theoretical analysis and numerical simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the traditional methods.

This study evaluates the current state of wireless network security in the CBD of Nuku'alofa. Wireless Networks have grown significantly since their introduction. Security protocols supplied with the wireless equipment have reached a level of strong and robust security. However, security remains a problem; this study aims to answer two questions. What is the status of wireless network growth and security in Tonga? and what can be done to improve wireless network security in Tonga. The war-drive results of Nuku'alofa produced comprehensive data which clearly shows the number of networks, both company and home networks. An exploratory research approach is employed to guide the design of the study. The results show that there is a significant growth in terms of WLAN usage since the implementation of the fiber optic network. However, this study concludes that WLAN security in Nuku'alofa is still in its early stages.

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.

LPD communication has recently emerged as a new transmission technology to address privacy and security in wireless networks. Recent studies have established the fundamental limits of LPD communication in terms of the amount of information that can be conveyed from a transmitter to a receiver subject to a constraint on a warden's detection error probability. The established information- theoretic metric enables analytical studies on the design and performance of LPD communication under various channel conditions. In this article, we present the key features of LPD communication and discuss various important design considerations. First, we clarify the differences between LPD communication and well-known physical-layer security. Then, from an information-theoretic point of view, we discuss the optimal signaling strategies for transmitting the message-carrying signal and artificial noise signal for LPD communication. Finally, we identify the key challenges in the design of practical LPD communication systems and point out future research directions in this context. This article provides guidelines for designing practical LPD communication strategies in wireless systems and networks.

Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.

The communication network indicators are analyzed and cryptographic analysis is considered to ensure information security of wireless communication networks using the application. Exploring fundamental principles and key algorithms like Advanced Encryption Standard and Rivest-Shamir-Adleman, this article equips readers with practical insights. This paper examines the challenges of researching cryptographic analysis to ensure the security of wireless communication networks using the application By understanding cryptographic algorithms and their application in wireless networks, readers can enhance the security their digital communications. A wireless network is used to connect various wired organizational structures and provide connectivity within the organization for employees to move freely by avoiding the hurdle of a physical network. Subsequent sections offer theoretical insights and performance analysis of cryptographic algorithms using the Java programming language.

Security has always played a critical role in wireless cooperative communication systems design. Eavesdropping and jamming are two common threats to the information security in wireless networks. However, jamming can be used in a cooperative manner to enable a secure communication link between the legitimate transmitter and the intended receiver. This paper presents a comprehensive survey on different jamming methods used to enhance the physical layer security. This survey outlines first the underlying concept and challenges with respect to security in wireless network design followed by a comprehensive literature review and analysis of jamming techniques with their applications in this field. For each jamming protocol, the paper categorizes different techniques within the existing literature by elaborating on their application, and corresponding performances.

Jamming emits white Gaussian noise that causes interference to both the legitimate receiver and eavesdropper. It has been an effective method for improving physical-layer security in wireless communication. In this paper, cautious jamming strategy based on optimal jammer selection method is proposed. More specifically, by using perfect channel state information, cautious jamming strategy selects an optimal jammer to improve the secrecy performance. For the purpose of comparison, we consider the direct jamming strategy and the adaptive jamming strategy as benchmark schemes. Closed-form expressions of the exact intercept probability for three jamming strategies are derived over Rayleigh fading channels and the theoretical analysis is validated by Monte-Carlo simulation. Both the analytical and the simulation results show that: 1) increasing the power of jammer is an effective approach to mitigate the intercept probability; 2) the intercept probability can be decreased and the wiretapped range of eavesdropper can be reduced by increasing the number of jammer; 3) by using perfect CSI and selecting optimal jammer, cautious jamming strategy outperforms direct jamming strategy and adaptive jamming strategy.

Multipath fading is one of the primary factors for degrading the performance in a wireless network. Information theoretic and past research suggest the use various diversity techniques to combat fading in wireless networks. Antenna diversity, a form of diversity technique, when incorporated in a wireless transceiver increases the system capacity and is one of the effective methods to combat fading in wireless systems. Also, recent research by Laneman et.al., Sendonaris et.al. suggests that cooperation among users in a wireless networks is an effective approach for a better signal reception in multipath fading environments. The diversity gains obtained by cooperation among the users of a wireless network is termed as cooperative diversity . Although, prior research in cooperative diversity considers users equipped with single antenna, in practical scenarios users may be able to accommodate multiple antennas due to the recent advanced research in semiconductor industry. Hence, the primary purpose of this thesis is to design, simulate and analyze an end-end performance of multi-antenna wireless systems employing cooperative multi antenna relay nodes so as to exploit the cooperative diversity and antenna diversity simultaneously in a wireless networks. Three main contributions to the area of cooperative multiple-input multiple-output (MIMO) wireless systems is presented in this thesis. First, we perform information theoretic analysis to study the impact of antenna arrays on cooperative wireless networks and propose the best possible distribution of antenna arrays among the three terminals of a simple three terminal cooperative relay network. Second, we design, simulate, and analyze a cooperative multiple-input multiple-output (MIMO) wireless systems employing orthogonal space-time block codes as proposed by Alamouti in 1998 with a decode-and-forward (DF) relay terminal. We implement a maximal ratio combining receiver that provides almost twice the diversity gain with respect to point-point multiple input multiple output link. Finally, we implement a practical receiver for cooperative reception using multiple antennas at all nodes based on Bell-Labs Layered Space Time architecture (BLAST). We incorporate a practical adaptive decode-and-forward (DF) relaying technique for reliable signal retransmission for both Alamouti space-time coding and the BLAST schemes. Results presented in terms of bit error rates and throughput show that remarkable performance gains are achievable by combining the concepts drawn from space-time coding, cooperative relaying and array processing.

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