The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Key generation from the randomness of wireless channels is a promising alternative to public key cryptography for the establishment of cryptographic keys between any two users. This paper reviews the current techniques for wireless key generation. The principles, performance metrics and key generation procedure are comprehensively surveyed. Methods for optimizing the performance of key generation are also discussed. Key generation applications in various environments are then introduced along with the challenges of applying the approach in each scenario. The paper concludes with some suggestions for future studies.

/pdf/key-generation-from-wireless-channels-a-review-3vw6b26ial.pdf

Key Generation From Wireless Channels: A Review

Machine learning based solutions for security of Internet of Things (IoT): A survey

Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Most physical-layer authentication techniques use hypothesis tests to compare the radio channel information with the channel record of Alice to detect spoofer Eve in wireless networks. However, the test threshold in the hypothesis test is not always available, especially in dynamic networks. In this letter, we propose a physical-layer authentication scheme based on extreme learning machine that exploit multi-dimensional characters of radio channels and use the training data generated from the spoofing model to improve the spoofing detection accuracy. Simulation results show that our proposed technique can significantly improve the authentication accuracy compared with the state-of-the-art method.

Physical-Layer Authentication Based on Extreme Learning Machine

In this letter, the possibility of using device-free sensing (DFS) technology for personnel detection in a foliage environment is investigated. Although the conventional algorithm that based on statistical properties of the received-signal strength (RSS) for target detection at indoor or open-field environment has come a long way in recent years, it is still questionable if this algorithm is fully functional at outdoor with the changing atmosphere and ground conditions, such as a foliage environment. To answer this question, a variety of the measured data have been taken using different targets in a foliage environment. Applying these data along with support vector machine, the impact on detection accuracy due to different classification algorithms is studied. An algorithm that based on the extraction of the high-order cumulant (HOC) of the signals is presented, while the conventional RSS-based one is used as a benchmark. The measurement results show that the classification accuracy of the HOC-based algorithm is better than the RSS-based one by at least 17%. Moreover, to ensure the reliability of the HOC-based approach, the impact on classification accuracy due to different numbers of training samples and different values of signal-to-noise ratio is extensively verified using experimentally recorded samples. To the best of our knowledge, this is the first time that a DFS-based sensing approach is demonstrated to have a potential to distinguish between human and small-animal targets in a foliage environment.

Device-Free Sensing for Personnel Detection in a Foliage Environment

The well-known Internet of Things (IoT) is recently being considered for critical missions, such as search and rescue, surveillance, and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this paper, we present a relatively low-cost, but robust approach that uses a combination of device-free sensing (DFS) and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband (IR-UWB) transceivers. These data are then used to investigate the influence of different statistical properties of the radio-frequency (RF) signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely, standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples, and different values of signal-to-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91% in terms of distinguishing between human and animal targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.

Internet of Mission-Critical Things: Human and Animal Classification—A Device-Free Sensing Approach

Foliage penetration (FOPEN) has been found to be a critical mission for a variety of applications, ranging from surveillance to military. Recently, an emerging technology, namely wireless sensor network (WSN)-based device-free sensing (DFS), has been introduced to the domain of FOPEN. This technology only utilizes radio-frequency signals for target detection and classification; thus, no additional hardware is required, just a wireless transceiver. Although the feasibility of using this technology for human detection indoors has been explored to some extent, it is questionable if the same technology can be transferred to outdoors. As far as FOPEN is concerned, the impact of seasonal variations on detection accuracy can be severe. To address this concern, in this paper, an experiment is conducted in four seasons, and how to ensure reasonable detection accuracy with seasonal variations is intensively investigated. To fully evaluate the potential of using the WSN-based DFS for FOPEN, an impulse-radio ultrawideband technology-based prototype is used to collect data samples in different seasons. Unlike the conventional approach based on a combination of statistical properties of received-signal strength and a support vector machine, this approach adopts two special measures for performance enhancement. One measure is to use a higher order cumulant (HOC) algorithm for feature extraction, so that the impact on detection accuracy due to unwanted clutters can be minimized. The other one is to determine the optimal parameters of the classifier by means of a flower pollination algorithm. Consequently, the adverse effects on detection accuracy due to variations of weather conditions in four seasons can be accommodated. According to the experimental result, it is shown that the average classification accuracy of the presented approach can be improved by at least 20% under all seasons with an ensured robustness.

Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach

Wireless networks strive to integrate information technology into every corner of the world. This openness of radio propagation is one reason why holistic wireless security mechanisms only rarely enter the picture. In this paper, we propose a physical (PHY)-layer security authentication scheme that takes advantage of channel randomness to detect spoofing attacks in wireless networks. Unlike most existing authentication techniques that rely on comparing message information between the legitimate user and potential spoofer, our proposed authentication scheme uses a Gaussian mixture model (GMM) to detect spoofing attackers. Probabilistic models of different transmitters are used to cluster messages. Furthermore, a 2-D feature measure space is exploited to preprocess the channel information. Training data for a spoofer operating through an unknown channel, a pseudo adversary model is developed to enhance the spoofing detection performance. Monte Carlo simulations are used to evaluate the detection performance of the GMM-based PHY-layer authentication scheme. The results show that the probability of detecting a spoofer is higher than that obtained using similar approaches.

Ting Jiang

Papers

Physical-Layer Authentication Based on Extreme Learning Machine

Device-Free Sensing for Personnel Detection in a Foliage Environment

Internet of Mission-Critical Things: Human and Animal Classification—A Device-Free Sensing Approach

Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach

Physical Layer Authentication Enhancement Using a Gaussian Mixture Model