Sliding cardinality estimation is crucial in many data analysis scenarios, e.g., detecting abnormal network behav-iors by monitoring unique connections in real time, detecting fraud in online transactions by monitoring unique user behavior patterns, and improving inventory management in supply chains by analyzing unique buyer behaviors. However, existing sliding cardinality estimation methods suffer from a cardinality barrel-down problem caused by unexpired item elimination in advance and item excessive removal, which remains unresolved so far. In this paper, we propose TardySketch, a sketch framework to make sliding cardinality estimation accurate and efficient by solving the above problem. The cornerstone of TardySketch is a Bidirectional Pointer-based Bitmap (BP-Bitmap), which stores the arrival sequence of items without timestamps. To prevent the premature elimination of unexpired items, we propose a Gap mechanism to enhance the accuracy of BP-Bitmap for identifying truly expired items through intermittent monitoring. To ensure an appropriate number of items are eliminated as the window moves, we design a Slow-Down mechanism to slacken the reset rate of bucket in BP- Bitmap to prevent over removal of items. Experimental results based on real-world datasets demonstrate that TardySketch significantly outperforms state-of-the-art methods, achieving a performance improvement of 5–40 times. The source code of TardySketch is available on GitHub.

Sketch demonstrates good properties in spread estimation over network measurements, providing fast processing and accurate estimation under limited memory usage. However, most current methods remain limited to single-flow spread estimation, resulting in suboptimal performance when applied to range spread estimation that requires measuring the spread of a range of flows. In this paper, we propose LocalSketch, a novel sketch that achieves both high estimation accuracy and memory efficiency for range spread estimation with provable theoretical guarantees. LocalSketch has two key innovations: (1) local key aggregation within predefined ranges that eliminates duplicate spread information through locality correlation, and (2) adaptive counter sizing that dynamically allocates memory resources for large-spread ranges while maintaining compact representations for low-spread ranges. LocalSketch also features an efficient abnormal bucket detection mechanism by comparing identification sign, avoiding exhaustive bucket traversal during super range detection. Moreover, the main idea of LocalSketch can be adapted to existing plug-in spread counters, which has been experimentally proved. We provide a theoretical analysis of estimation accuracy and conduct comprehensive evaluations using real-world network traffic datasets. Experimental results demonstrate that LocalSketch outperforms state-of-the-art methods by achieving 76× higher estimation accuracy for range spread estimation, while showing 15× better accuracy and 39× faster detection speed for super range identification across all datasets.

Backscatter communication (BC) is an emerging radio technology for achieving sustainable wireless communications. However, the literature still lacks an effective secret group key generation scheme for safeguarding communications among multiple resource-constrained backscatter devices (BDs). In this paper, we propose a novel physical layer group key generation framework, BGKey, for securing backscatter communications among multiple BDs. BGKey contains three schemes: Centralized Group Key Generation (CGKG), Decentralized Group Key Generation (DGKG), and Decentralized Hierarchical Group Key Generation (DHGKG). Each scheme has its own advantages, applicable in different scenarios. We analyze the performance of BGKey schemes regarding computation and communication complexity and security under eavesdropping and three active attacks. We conduct extensive simulations with different system parameters to evaluate their performance. CGKG is the most efficient and accurate for generating a group key, but it depends on a trusted radio frequency source (RFS) and is the least secure under eavesdropping and three active attacks among three schemes. DGKG exhibits better security and higher key generation rate (KGR) against eavesdropping and three active attacks compared with CGKG. However, the bit disagreement ratio (BDR) of group key increases when the size of BD group increases. DHGKG dramatically enhances the performance of group key generation compared with DGKG and retains its excellent security against eavesdropping and three active attacks.

Ambient backscatter communication (AmBC) enables ultra-low-power communications by backscattering ambient radio frequency (RF) signals and harvesting energy simultaneously. It has emerged as a cutting-edge technology for supporting a variety of Internet of Things (IoT) applications. However, existing research lacks effective secret key sharing schemes for safeguarding communications between resource-constrained backscatter devices (BDs) in AmBC systems. In this paper, we present, Tri-Channel, a novel physical layer key generation scheme between two BDs by multiplying downlink signals and backscatter signals to obtain the information of a triangle channel as a shared random secret source for key generation. In particular, we analyze the security of our scheme under both passive and active attacks, concretely Eavesdropping Attack (EA), Control Channel Attack (CCA), Signal Manipulative Attack (SMA), and Untrusted RF-Source Attack (URSA). Through theoretical analysis and simulations by comparing with a traditional scheme (named Tradi-Channel), we found that our scheme consistently outperforms the Tradi-Channel under the EA and two active attacks (CCA and SMA). In addition, it shows better security performance under URSA, which is proposed based on the unauthenticated characteristic of BDs in Tri-Channel, even though URSA is more vital than SMA. Concretely, Tri-Channel’s secret key rate (SKR) outperforms Tradi-Channel’s under the above four passive and active attacks. This implies that our scheme is advanced in terms of both security and efficiency of key generation. Numerous extensive simulations further prove our theoretical analysis results.

Backscatter communication (BC) enables ultra-low-power communications and allows devices to harvest energy simultaneously. But its practical deployment faces severe security threats caused by its nature of openness and broadcast. Authenticating backscatter devices (BDs) is treated as the first line of defense. However, complex cryptographic approaches are not desirable due to the limited computation capability of BDs. Existing physical layer authentication schemes cannot effectively support BD mobility, multiple attacker identification and attacker location tracing in an integrated way. To tackle these problems, this paper proposes BCAuth, a multi-stage authentication and attack tracing scheme based on the physical spatial information of BDs to realize enhanced BD authentication security for both static and mobile BDs. After initial authentication based on BD identity with its position information registration, preemptive authentication and re-authentication are performed according to spatial correlation of backscattered signal source locations associated with the BD. By exploiting clustering-based analysis on spacial information, BCAuth is capable of determining the number of attackers and localizing their positions. In addition, we propose a reciprocal channel-based method for BD re-authentication with better authentication performance than the clustering-based method for mobile BDs when the BDs is able to measure received signal strength (RSS), which also enables mutual authentication. We theoretically analyze BCAuth security and conduct extensive numerical simulations with various settings to show its desirable performance.

Recently, networks operate at frequencies over 28 GHz (mmWave) have emerged as a viable solution for 5G mobile networks to provide Gbps data rate. Due to the high directivity and attenuation of mmWave signals, mmWave communication links are highly vulnerable to the frequent mmWave channel blockages, which can trigger excessive handovers. Thanks to its ability to enrich the scattering environment and create reflective signal multipaths, Reconfigurable Intelligent Surface (RIS) has great potential to counter the blockage effect and thus greatly reduce the number of unnecessary handovers. However, this potential has not been well explored. In this paper, we propose a RIS-assisted handover scheme by leveraging deep reinforcement learning (DRL). Under various channel blockage conditions, the DRL agent manages to reduce the cumulative handover overhead by jointly adjusting beamformers and RIS phase shifts. Compared with the existing schemes without considering RIS, the RIS-assisted handover scheme significantly reduces the number of handovers and achieves higher spectrum efficiency. Besides, to alleviate the impact from the limited observations of the fast fading channels, we propose a lightweight algorithm to sense the blockage status and such sensing results can be utilized to improve the performance of model training. Numerical results show that DRL agent is able to further improve the performance when integrated with the blockage status sensing algorithm.

Wirelessly powered backscatter communications (WPBC) is an emerging technology for providing continuous energy and ultra-low power communications. Despite some progress in WPBC systems, resource allocation for multiple devices towards secure backscatter communications (BC) and efficient-energy harvesting (EH) requests a deep-insight investigation. In this paper, we consider a WPBC system in which a full-duplex access point (AP) transmits multi-sinewave signals to power backscatter devices (BDs) and injects artificial noise (AN) to secure their backscatter transmissions. To maximize the minimum harvested energy and ensure fairness and security of all BDs, we formulate an optimization problem by jointly considering the backscatter time, power splitting ratio between multi-sinewave and AN, and signal power allocation. For a single-BD system, we characterize the achievable secrecy rate-energy region with a non-linear energy harvester and propose two algorithms to solve an energy maximization problem. We then analyze the effect of multi-sinewave and AN signals on BD’s secrecy rate and harvested energy through simulations and proof-of-concept experiments. For a multi-BD system, we propose an iterative algorithm by leveraging block successive upper-bound minimization (BSUM) techniques to solve the non-convex problem of fair resource allocation and show its convergence and complexity. Numerical results show the proposed algorithm achieves optimal and equitable harvested energy for all BDs with satisfying the security constraint.

Ambient backscatter communication (AmBC), which enables energy harvesting and ultra-low-power communication by utilizing ambient radio frequency (RF) signals, has emerged as a cutting-edge technology to realize numerous Internet of Things (IoT) applications. However, the current literature lacks efficient secret key sharing solutions for resource-limited devices in AmBC systems to protect the backscatter communications, especially for private data transmission. Thus, we propose a novel physical layer key generation scheme between backscatter devices (BDs) by exploiting received superposed ambient signals. Based on the repeated patterns (i.e., cyclic prefix in OFDM symbols) in ambient RF signals, we present a joint transceiver design of BD backscatter waveform and BD receiver to extract the downlink signal and the backscatter signal from the superposed signals. By multiplying the downlink signal and the backscatter signal, we can actually obtain the triangle channel information as a shared random secret source for key generation. Besides, we study the trade-off between the rate of secret key generation and harvested energy by modeling it as a joint optimization problem. Finally, extensive numerical simulations are provided to evaluate the key generation performance, energy harvesting performance, and their trade-offs under various system settings.

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, to achieve efficient PL-SAD in 5G networks, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. To achieve spoofing attack detection, we provide two methods based on different machine learning models. For the first method, the detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). Another method involves a Siamese network, in which the similarity between sample pairs from one device is used to achieve PL-SAD. It can tackle the training problem that the historical data cannot support the identification of the same device in a new communication session. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD schemes. Experimental results confirm the effectiveness of the proposed PL-SAD schemes, and the detection accuracy can reach 99% using small sample sizes under different scenarios.

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process, to achieve efficient PL-SAD. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. The detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). It can tackle small sample learning and allow for quick model construction. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD scheme. Experimental results confirm the effectiveness of the proposed PL-SAD scheme, and the detection accuracy can reach 98% using small sample sizes under different scenarios.

The consumer unmanned aerial vehicle (UAV) market has grown significantly over the past few years. Despite its huge potential in spurring economic growth by supporting various applications, the increase of consumer UAVs poses potential risks to public security and personal privacy. To minimize the risks, efficiently detecting and identifying invading UAVs is in urgent need for both invasion detection and forensics purposes. Aiming to complement the existing physical detection mechanisms, we propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use Wi-Fi links for control and video streaming. The proposed framework extracts features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can efficiently detect UAVs and identify their operation modes. In order to reduce the online identification time, our framework adopts a re-weighted ℓ1-norm regularization, which considers the number of samples and computation cost of different features. This framework jointly optimizes feature selection and prediction performance in a unified objective function. To tackle the packet inter-arrival time uncertainty when optimizing the trade-off between the detection accuracy and delay, we utilize maximum likelihood estimation (MLE) method to estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of eight types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method. Evaluation results show that our proposed method can detect and identify tested UAVs within 0.15-0.35s with high accuracy of 85.7-95.2%. The UAV detection range is within the physical sensing range of 70m and 40m in the line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively. The operation mode of UAVs can be identified with high accuracy of 88.5-98.2%.

In this paper, we consider a wireless powered backscatter communication (WPBC) network in which a full-duplex access point (AP) simultaneously transmits information and energy signals by injecting artificial noise (AN) to secure the backscatter transmission from multiple backscatter devices (BDs). To maximize the minimum throughput and ensure fairness and security, we formulate an optimization problem by jointly considering the power splitting ratio between dedicated information signals and AN, backscatter time and signal power allocation among multiple BDs. For a single BD network, we obtain a closed-form solution and evaluate its validity through proof-of-concept experiments. For the general case with multiple BDs, we present an iterative algorithm by leveraging block coordinate descent (BCD) and successive convex approximation optimization to solve a non-convex problem incurred in WPBC. We further show the convergence of the proposed algorithm and analyze its complexity. Finally, extensive simulation results show that the proposed algorithm achieves an optimal and equitable throughput for all BDs, and our work provides a good perspective of resource allocation to improve the performance of WPBC networks.

The fifth generation (5G) wireless technologies serve as a key propellent to meet the increasing demands of the future Internet of Things (IoT) networks. For wireless communication security in 5G IoT networks, physical-layer security (PLS) has recently received growing interest. This paper aims to provide a comprehensive survey of the PLS techniques in 5G IoT communication systems. The investigation consists of four hierarchical parts. In the first part, we review the characteristics of 5G IoT under typical application scenarios. We then introduce the security threats from the 5G IoT physical-layer and categorize them according to the different purposes of the attacker. In the third part, we examine the 5G communication technologies in 5G IoT systems and discuss their challenges and opportunities when coping with physical-layer threats, including massive multiple-input-multiple-output (MIMO), millimeter wave (mmWave) communications, nonorthogonal multiple access (NOMA), full-duplex technology, energy harvesting (EH), visible light communication (VLC), and unmanned aerial vehicle (UAV) communications. Finally, we discuss open research problems and future works about PLS in the IoT system with technologies of 5G and beyond.

Physical layer security of non-orthogonal multiple access (NOMA) systems has attracted great attentions. However, the impact of mobility on physical layer security of NOMA systems has not been well studied. In this paper, to fill this gap, we investigate the impact of random mobility on physical layer security of NOMA systems. Considering scenarios where a base station (BS) or access point (AP) communicates to two random mobile users with a passive eavesdropper in two concentric circles, we study the secrecy performance with combinations of two typical random mobility models: random waypoint (RWP) and random direction (RD). A general analytical framework to numerically calculate the average secrecy rates of NOMA mobile users under steady state is provided. By comparing secrecy performance of mobile users with static users, we find that RWP mobile users can achieve higher average secrecy rates than the users with other mobility combinations. Meanwhile, two types of secrecy fairness for mobile users are fully considered and we propose a novel sum average secrecy rate maximization problem, subject to average power limits and users' QoS (quality of service) requirements. Considering eavesdropper's channel state information (CSI) is unknown to BS, we propose a threshold power allocation strategy to improve the sum average secrecy rate of NOMA mobile users. Extensive numerical simulations are conducted to validate our model and theoretical analysis.

In many IoT (Internet-of-Things) applications, a large number of low-cost IoT devices are connected to the Internet through an access point (AP) or gateway via wireless communication. Due to the resource constraints on IoT devices and broadcast nature of wireless medium, identity spoofing attacks are easy to launch but hard to defend in an IoT wireless access network. In this paper, under the context of 5G communication, we propose an efficient physical layer identity spoofing attack detection scheme for IoT. By harnessing the sparsity of the virtual channel in mmWave and Massive MIMO 5G communication, we propose a two- step detection scheme. In the first step, our scheme detects anomalies by examining the virtual angles of arrival (AoA) and path gains of all the IoT devices simultaneously in a virtual channel space (VCS). In the second step, we introduce a machine learning based detection scheme to detect the actual attack. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme. The minimum Bayes risk of the proposed scheme can be less than 0.5\% even in the presence of 100 IoT devices.

Pervasive social networking (PSN) supports instant social activities anywhere and at any time with the support of heterogeneous networks. In order to preserve privacy and achieve trustworthy PSN, anonymous authentication on node trust is expected in PSN. However, the literature still lacks serious studies on this issue. In this paper, we propose an anonymous authentication scheme for authenticating both pseudonyms and trust levels to support trustworthy PSN with privacy preservation. The scheme achieves secure anonymous authentication with anonymity and conditional traceability on the basis of a trusted authority (TA). By applying a back-up solution, it can guarantee communications among nodes for an extended time period even when the TA is not available. In addition, the use of batch-signature verification further reduces the cost of authenticity verification of a large number of messages. Performance analysis and evaluation further prove that the proposed scheme is effective with regard to privacy preservation, computation complexity, communication cost, flexibility, reliability, and scalability.