The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users’ visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs’ locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users’ QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user’s content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users’ content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.

Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

Machine Learning in IoT Security: Current Solutions and Future Challenges

This article discusses opportunities and challenges of physical layer security integration in MaMIMO systems. Specifically, we first show that MaMIMO itself is robust against passive eavesdropping attacks. We then review a pilot contamination scheme that actively attacks the channel estimation process. This pilot contamination attack not only dramatically reduces the achievable secrecy capacity but is also difficult to detect. We proceed by reviewing some methods from literature that detect active attacks on MaMIMO. The last part of the article surveys the open research problems that we believe are the most important to address in the future and give a few promising directions of research to solve them.

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Physical layer security for massive MIMO: An overview on passive eavesdropping and active attacks

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Aging power industries, together with the increase in demand from industrial and residential customers, are the main incentive for policy makers to define a road map to the next-generation power system called the smart grid. In the smart grid, the overall monitoring costs will be decreased, but at the same time, the risk of cyber attacks might be increased. Recently, a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine-learning-based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a distributed support vector machine (SVM). The design of the distributed SVM is based on the alternating direction method of multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects the deviation in measurements. In both methods, principal component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on IEEE standard test systems demonstrate the effectiveness of both schemes.

Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid

Aging power industries together with increase in the demand from industrial and residential customers are the main incentive for policy makers to define a road map to the next generation power system called smart grid. In smart grid, the overall monitoring costs will be decreased but at the same time, the risk of cyber attacks might be increased. Recently a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We devise two machine learning based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a support vector machine. The second method requires no training data and detects the deviation in measurement In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, and thus leads to lower computation complexities. The results of the proposed detection methods on the IEEE standard test systems demonstrate effectiveness of both schemes.

Detecting stealthy false data injection using machine learning in smart grid

Each wireless device has its unique fingerprint, which can be utilized for device identification and intrusion detection. Most existing literature employs supervised learning techniques and assumes the number of devices is known. In this paper, based on device-dependent channel-invariant radio-metrics, we propose a non-parametric Bayesian method to detect the number of devices as well as classify multiple devices in a unsupervised passive manner. Specifically, the infinite Gaussian mixture model is used and a modified collapsed Gibbs sampling method is proposed. Sybil attacks and Masquerade attacks are investigated. We have proven the effectiveness of the proposed method by both simulation data and experimental measurements obtained by USRP2 and Zigbee devices.

Device fingerprinting to enhance wireless security using nonparametric Bayesian method

Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next-generation wireless communication. Channel estimation is one of the key challenges in OFDM, since high-resolution channel estimation can significantly improve the equalization at the receiver and consequently enhance the communication performances. In this paper, we propose a system with an asymmetric digital-to-analog converter/analog-to-digital converter (DAC/ADC) pair and formulate OFDM channel estimation as a compressive sensing problem. By skillfully designing pilots and taking advantages of the sparsity of the channel impulse response, the proposed system realizes high-resolution channel estimation at a low cost. The pilot design, the use of a high-speed DAC and a regular-speed ADC, and the estimation algorithm tailored for channel estimation distinguish the proposed approach from the existing estimation approaches. We theoretically show that in the proposed system, a N-resolution channel can be faithfully obtained with an ADC speed at M=O(S2log(N/S)), where N is also the DAC speed and S is the channel impulse response sparsity. Since S is small and increasing the DAC speed to N >; M is relatively cheap, we obtain a high-resolution channel at a low cost. We also present a novel estimator that is both faster and more accurate than the typical l1 minimization. In the numerical experiments, we simulated various numbers of multipaths and different SNRs and let the transmitter DAC run at 16 times the speed of the receiver ADC for estimating channels at the 16 t resolution. While there is no similar approaches (for asymmetric DAC/ADC pairs) to compare with, we derive the Cramer-Rao lower bound.

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Compressive Sensing Based High-Resolution Channel Estimation for OFDM System

Primary user emulation (PUE) attacks, where attackers mimic the signals of primary users (PUs), can cause significant performance degradation in cognitive radio (CR) systems. Detection of the presence of PUE attackers is thus an important problem. In this paper, using device-specific features, we propose a passive, nonparametric classification method to determine the number of transmitting devices in the PU spectrum. Our method, called DECLOAK, is passive since the sensing device listens and captures signals without injecting any signal to the wireless environment. It is nonparametric because the number of active devices needs not to be known as a priori. Channel independent features are selected forming fingerprints for devices, which cannot be altered postproduction. The infinite Gaussian mixture model (IGMM) is adopted and a modified collapsed Gibbs sampling method is proposed to classify the extracted fingerprints. Due to its unsupervised nature, there is no need to collect legitimate PU fingerprints. In combination with received power and device MAC address, we show through simulation studies that the proposed method can efficiently detect the PUE attack. The performance of DECLOAK is also shown to be superior than that of the classical non-parametric mean shift (MS) based clustering method.

Nam Tuan Nguyen

Papers

Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid

Detecting stealthy false data injection using machine learning in smart grid

Device fingerprinting to enhance wireless security using nonparametric Bayesian method

Compressive Sensing Based High-Resolution Channel Estimation for OFDM System

On Identifying Primary User Emulation Attacks in Cognitive Radio Systems Using Nonparametric Bayesian Classification