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Hu, Lei, Li, Guyue, Luo, Hongyi, Hu, Aiqun.  2021.  On the RIS Manipulating Attack and Its Countermeasures in Physical-Layer Key Generation. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). :1–5.
Reconfigurable Intelligent Surface (RIS) is a new paradigm that enables the reconfiguration of the wireless environment. Based on this feature, RIS can be employed to facilitate Physical-layer Key Generation (PKG). However, this technique could also be exploited by the attacker to destroy the key generation process via manipulating the channel features at the legitimate user side. Specifically, this paper proposes a new RIS-assisted Manipulating attack (RISM) that reduces the wireless channel reciprocity by rapidly changing the RIS reflection coefficient in the uplink and downlink channel probing step in orthogonal frequency division multiplexing (OFDM) systems. The vulnerability of traditional key generation technology based on channel frequency response (CFR) under this attack is analyzed. Then, we propose a slewing rate detection method based on path separation. The attacked path is removed from the time domain and a flexible quantization method is employed to maximize the Key Generation Rate (KGR). The simulation results show that under RISM attack, when the ratio of the attack path variance to the total path variance is 0.17, the Bit Disagreement Rate (BDR) of the CFR-based method is greater than 0.25, and the KGR is close to zero. In addition, the proposed detection method can successfully detect the attacked path for SNR above 0 dB in the case of 16 rounds of probing and the KGR is 35 bits/channel use at 23.04MHz bandwidth.
Liu, Fuwen, Su, Li, Yang, Bo, Du, Haitao, Qi, Minpeng, He, Shen.  2021.  Security Enhancements to Subscriber Privacy Protection Scheme in 5G Systems. 2021 International Wireless Communications and Mobile Computing (IWCMC). :451–456.
Subscription permanent identifier has been concealed in the 5G systems by using the asymmetric encryption scheme as specified in standard 3GPP TS 33.501 to protect the subscriber privacy. The standardized scheme is however subject to the SUPI guess attack as the public key of the home network is publicly available. Moreover, it lacks the inherent mechanism to prevent SUCI replay attacks. In this paper, we propose three methods to enhance the security of the 3GPP scheme to thwart the SUPI guess attack and replay attack. One of these methods is suggested to be used to strengthen the security of the current subscriber protection scheme.
Hamouid, Khaled, Omar, Mawloud, Adi, Kamel.  2021.  A Privacy-Preserving Authentication Model Based on Anonymous Certificates in IoT. 2021 Wireless Days (WD). :1–6.
This paper proposes an anonymity based mechanism for providing privacy in IoT environment. Proposed scheme allows IoT entities to anonymously interacting and authenticating with each other, or even proving that they have trustworthy relationship without disclosing their identities. Authentication is based on an anonymous certificates mechanism where interacting IoT entities could unlinkably prove possession of a valid certificate without revealing any incorporated identity-related information, thereby preserving their privacy and thwarting tracking and profiling attacks. Through a security analysis, we demonstrate the reliability of our solution.
Buccafurri, Francesco, Angelis, Vincenzo De, Francesca Idone, Maria, Labrini, Cecilia.  2021.  WIP: An Onion-Based Routing Protocol Strengthening Anonymity. 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). :231–235.
Anonymous Communication Networks (ACNs) are networks in which, beyond data confidentiality, also traffic flow confidentiality is provided. The most popular routing approach for ACNs also used in practice is Onion. Onion is based on multiple encryption wrapping combined with the proxy mechanism (relay nodes). However, it offers neither sender anonymity nor recipient anonymity in a global passive adversary model, simply because the adversary can observe (at the first relay node) the traffic coming from the sender, and (at the last relay node) the traffic delivered to the recipient. This may also cause a loss of relationship anonymity if timing attacks are performed. This paper presents Onion-Ring, a routing protocol that improves anonymity of Onion in the global adversary model, by achieving sender anonymity and recipient anonymity, and thus relationship anonymity.
Yang, Yuhan, Zhou, Yong, Wang, Ting, Shi, Yuanming.  2021.  Reconfigurable Intelligent Surface Assisted Federated Learning with Privacy Guarantee. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
In this paper, we consider a wireless federated learning (FL) system concerning differential privacy (DP) guarantee, where multiple edge devices collaboratively train a shared model under the coordination of a central base station (BS) through over-the-air computation (AirComp). However, due to the heterogeneity of wireless links, it is difficult to achieve the optimal trade-off between model privacy and accuracy during the FL model aggregation. To address this issue, we propose to utilize the reconfigurable intelligent surface (RIS) technology to mitigate the communication bottleneck in FL by reconfiguring the wireless propagation environment. Specifically, we aim to minimize the model optimality gap while strictly meeting the DP and transmit power constraints. This is achieved by jointly optimizing the device transmit power, artificial noise, and phase shifts at RIS, followed by developing a two-step alternating minimization framework. Simulation results will demonstrate that the proposed RIS-assisted FL model achieves a better trade-off between accuracy and privacy than the benchmarks.
Sahay, Rajeev, Brinton, Christopher G., Love, David J..  2021.  Frequency-based Automated Modulation Classification in the Presence of Adversaries. ICC 2021 - IEEE International Conference on Communications. :1–6.
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning to achieve robust AMC performance using raw in-phase and quadrature (IQ) time samples. Yet, deep learning models are highly susceptible to adversarial interference, which cause intelligent prediction models to misclassify received samples with high confidence. Furthermore, adversarial interference is often transferable, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification network. In this work, we present a novel receiver architecture consisting of deep learning models capable of withstanding transferable adversarial interference. Specifically, we show that adversarial attacks crafted to fool models trained on time-domain features are not easily transferable to models trained using frequency-domain features. In this capacity, we demonstrate classification performance improvements greater than 30% on recurrent neural networks (RNNs) and greater than 50% on convolutional neural networks (CNNs). We further demonstrate our frequency feature-based classification models to achieve accuracies greater than 99% in the absence of attacks.
Umar, Sani, Felemban, Muhamad, Osais, Yahya.  2021.  Advanced Persistent False Data Injection Attacks Against Optimal Power Flow in Power Systems. 2021 International Wireless Communications and Mobile Computing (IWCMC). :469–474.
Recently, cyber security in power systems has captured significant interest. This is because the world has seen a surge in cyber attacks on power systems. One of the prolific cyber attacks in modern power systems are False Data Injection Attacks (FDIA). In this paper, we analyzed the impact of FDIA on the operation cost of power systems. Also, we introduced a novel Advanced Persistent Threat (APT) based attack strategy that maximizes the operating costs when attacking specific nodes in the system. We model the attack strategy using an optimization problem and use metaheuristics algorithms to solve the optimization problem and execute the attack. We have found that our attacks can increase the power generation cost by up to 15.6%, 60.12%, and 74.02% on the IEEE 6-Bus systems, 30-Bus systems, and 118-Bus systems, respectively, as compared to normal operation.
Petrenkov, Denis, Agafonov, Anton.  2021.  Anomaly Detection in Vehicle Platoon with Third-Order Consensus Control. 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0463–0466.
The development of autonomous connected vehicles, in particular, moving as a platoon formation, has received great attention in recent years. The autonomous movement allows to increase the efficiency of the transportation infrastructure usage, reduce the fuel consumption, improve road safety, decrease traffic congestion, and others. To maintain an optimal spacing policy in a platoon formation, it is necessary to exchange information between vehicles. The Vehicular ad hoc Network (VANET) is the key component to establish wireless vehicle-to-vehicle communications. However, vehicular communications can be affected by different security threats. In this paper, we consider the third-order consensus approach as a control strategy for the vehicle platoon. We investigate several types of malicious attacks (spoofing, message falsification) and propose an anomaly detection algorithm that allows us to detect the malicious vehicle and enhance the security of the vehicle platoon. The experimental study of the proposed approach is conducted using Plexe, a vehicular network simulator that permits the realistic simulation of platooning systems.
Hasan, Md. Mahmudul, Jahan, Mosarrat, Kabir, Shaily, Wagner, Christian.  2021.  A Fuzzy Logic-Based Trust Estimation in Edge-Enabled Vehicular Ad Hoc Networks. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
Trust estimation of vehicles is vital for the correct functioning of Vehicular Ad Hoc Networks (VANETs) as it enhances their security by identifying reliable vehicles. However, accurate trust estimation still remains distant as existing works do not consider all malicious features of vehicles, such as dropping or delaying packets, altering content, and injecting false information. Moreover, data consistency of messages is not guaranteed here as they pass through multiple paths and can easily be altered by malicious relay vehicles. This leads to difficulty in measuring the effect of content tampering in trust calculation. Further, unreliable wireless communication of VANETs and unpredictable vehicle behavior may introduce uncertainty in the trust estimation and hence its accuracy. In this view, we put forward three trust factors - captured by fuzzy sets to adequately model malicious properties of a vehicle and apply a fuzzy logic-based algorithm to estimate its trust. We also introduce a parameter to evaluate the impact of content modification in trust calculation. Experimental results reveal that the proposed scheme detects malicious vehicles with high precision and recall and makes decisions with higher accuracy compared to the state-of-the-art.
Najafi, Maryam, Khoukhi, Lyes, Lemercier, Marc.  2021.  A Multidimensional Trust Model for Vehicular Ad-Hoc Networks. 2021 IEEE 46th Conference on Local Computer Networks (LCN). :419–422.
In this paper, we propose a multidimensional trust model for vehicular networks. Our model evaluates the trustworthiness of each vehicle using two main modes: 1) Direct Trust Computation DTC related to a direct connection between source and target nodes, 2) Indirect Trust Computation ITC related to indirectly communication between source and target nodes. The principal characteristics of this model are flexibility and high fault tolerance, thanks to an automatic trust scores assessment. In our extensive simulations, we use Total Cost Rate to affirm the performance of the proposed trust model.
Cheng, Xia, Shi, Junyang, Sha, Mo, Guo, Linke.  2021.  Launching Smart Selective Jamming Attacks in WirelessHART Networks. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications. :1–10.
As a leading industrial wireless standard, WirelessHART has been widely implemented to build wireless sensor-actuator networks (WSANs) in industrial facilities, such as oil refineries, chemical plants, and factories. For instance, 54,835 WSANs that implement the WirelessHART standard have been deployed globally by Emerson process management, a WirelessHART network supplier, to support process automation. While the existing research to improve industrial WSANs focuses mainly on enhancing network performance, the security aspects have not been given enough attention. We have identified a new threat to WirelessHART networks, namely smart selective jamming attacks, where the attacker first cracks the channel usage, routes, and parameter configuration of the victim network and then jams the transmissions of interest on their specific communication channels in their specific time slots, which makes the attacks energy efficient and hardly detectable. In this paper, we present this severe, stealthy threat by demonstrating the step-by-step attack process on a 50-node network that runs a publicly accessible WirelessHART implementation. Experimental results show that the smart selective jamming attacks significantly reduce the network reliability without triggering network updates.
Yao, Li, Liu, Youjiang.  2020.  A Novel Optimization Scheme for the Beamforming Method Selection in Artificial-Noise-Aid MU-MISOME Broadcast Secure Communication System. 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). :175–179.
This article investigates the beamforming method selection in artificial-noise-aid (AN-aid) multiuser multiple-input-single-output (MU-MISO) broadcast wiretap systems in slow fading channel environment. We adopt beamforming pre-coding matrix with artificial noise to achieve secure multiuser communication and optimize system performance, and compare the secure transmission performance of two beamforming methods. To overcome the complexity of this model, a novel optimization scheme expressed using semi-closed-form expressions and Monte Carlo method is employed to derive the relationship between transmission parameters and secure transmission performance. This scheme would help us to analyses performance of different beamforming methods.
Khiadani, Nadia.  2020.  Vision, Requirements and Challenges of Sixth Generation (6G) Networks. 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). :1–4.
The use of wireless connectivity has increased exponentially in recent years. Fifth generation (5G) communications will soon be deployed worldwide. Six-generation (6G) communications vision and planning have begun, and the use of 6G communications is expected to begin in the 2030s. The 6G system has higher capacity, higher data rates, lower latency, higher security and better quality of service (QoS) compared to the 5G system. This paper presents a brief overview on the vision and requirements of 6G wireless communications and networks. Finally, some of the challenges in launching the 6G are also explained.
Gao, Jianbang, Yuan, Zhaohui, Qiu, Bin.  2020.  Artificial Noise Projection Matrix Optimization Method for Secure Multi-Cast Wireless Communication. 2020 IEEE 8th International Conference on Information, Communication and Networks (ICICN). :33–37.
Transmit beamforming and artificial noise (AN) methods have been widely employed to achieve wireless physical layer (PHY) secure transmissions. While most works focus on transmit beamforming optimization, little attention is paid to the design of artificial noise projection matrix (ANPM). In this paper, compared with traditional ANPM obtained by zero-forcing method, which only makes AN power uniform distribution in free space outside legitimate users (LU) locations, we design ANPM to maximize the interference on eavesdroppers without interference on LUs for multicast directional modulation (MCDM) scenario based on frequency diverse array (FDA). Furthermore, we extend our approach to the case of with imperfect locations of Eves. Finally, simulation results show that Eves can be seriously affected by the AN with perfect/imperfect locations, respectively.
Nait-Abdesselam, Farid, Darwaish, Asim, Titouna, Chafiq.  2020.  An Intelligent Malware Detection and Classification System Using Apps-to-Images Transformations and Convolutional Neural Networks. 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–6.
With the proliferation of Mobile Internet, handheld devices are facing continuous threats from apps that contain malicious intents. These malicious apps, or malware, have the capability of dynamically changing their intended code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses, which typically use signature-based techniques, and make them unable to detect the previously unknown malware. However, the variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns, obtained either statically or dynamically, can be exploited to detect and classify unknown malware into their known families using machine learning techniques. In this paper, we propose a new approach for detecting and analyzing a malware. Mainly focused on android apps, our approach adopts the two following steps: (1) performs a transformation of an APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping, and (2) trains a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The results obtained using the Androzoo dataset show that our system classifies both legacy and new malware apps with high accuracy, low false-negative rate (FNR), and low false-positive rate (FPR).
Van Rompaey, Robbe, Moonen, Marc.  2021.  Distributed Adaptive Acoustic Contrast Control for Node-specific Sound Zoning in a Wireless Acoustic Sensor and Actuator Network. 2020 28th European Signal Processing Conference (EUSIPCO). :481–485.
This paper presents a distributed adaptive algorithm for node-specific sound zoning in a wireless acoustic sensor and actuator network (WASAN), based on a network-wide acoustic contrast control (ACC) method. The goal of the ACC method is to simultaneously create node-specific zones with high signal power (bright zones) while minimizing power leakage in other node-specific zones (dark zones). To obtain this, a network-wide objective involving the acoustic coupling between all the loudspeakers and microphones in the WASAN is proposed where the optimal solution is based on a centralized generalized eigenvalue decomposition (GEVD). To allow for distributed processing, a gradient based GEVD algorithm is first proposed that minimizes the same objective. This algorithm can then be modified to allow for a fully distributed implementation, involving in-network summations and simple local processing. The algorithm is referred to as the distributed adaptive gradient based ACC algorithm (DAGACC). The proposed algorithm outperforms the non-cooperative distributed solution after only a few iterations and converges to the centralized solution, as illustrated by computer simulations.
Fathelbab, Wael M..  2021.  Novel Acoustic Wave Networks Comprising Resonators Achieving Prescribed Coupling. 2021 IEEE 21st Annual Wireless and Microwave Technology Conference (WAMICON). :1–4.
Novel acoustic wave networks comprising resonators achieving prescribed coupling are proposed. The design methodology is based on classic network synthesis of doubly- and/or singly-terminated networks. The synthesis of LTE Band 25 contiguous duplexer prototype is performed and its electrical characteristics are presented.
Nair, Devika S, BJ, Santhosh Kumar.  2021.  Identifying Rank Attacks and Alert Application in WSN. 2021 6th International Conference on Communication and Electronics Systems (ICCES). :798–802.
Routing protocol for low power and lossy networks (RPL) is a fundamental routing protocol of 6LoWPAN, a centre correspondence standard for the Internet of Things. RPL outplay other wireless sensor and ad hoc routing protocols in the aspect of service (QoS), device management, and energy-saving performance. The Rank definition in RPL addresses several issues, such as path optimization, loop avoidance, and power overhead management. RPL rank and version number attacks are two types of the most common forms of RPL attacks, may have crucial ramification for RPL networks. The research directed upon these attacks includes considerable vulnerabilities and efficiency issues. The rank attack on sensor networks is perhaps the utmost common, posing a challenge to network connectivity by falling data or disrupting routing routes. This work presents a rank attack detection system focusing on RPL. Considering many of such issues a method has been proposed using spatial correlation function (SCF) and Dijkstra's algorithm considering parameters like energy and throughput.
Bettoumi, Balkis, Bouallegue, Ridha.  2021.  Efficient Reduction of the Transmission Delay of the Authentication Based Elliptic Curve Cryptography in 6LoWPAN Wireless Sensor Networks in the Internet of Things. 2021 International Wireless Communications and Mobile Computing (IWCMC). :1471–1476.
Wireless Sensor Network (WSN) is considered as the backbone of Internet of Things (IoT) networks. Authentication is the most important phase that guarantees secure access to such networks but it is more critical than that in traditional Internet because the communications are established between constrained devices that could not compute heavy cryptographic primitives. In this paper, we are studying with real experimentation the efficiency of HIP Diet EXchange header (HIP DEX) protocol over IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) in IoT. The adopted application layer protocol is Constrained Application Protocol (CoAP) and as a routing protocol, the Routing Protocol for Low power and lossy networks (RPL). The evaluation concerns the total End-to-End transmission delays during the authentication process between the communicating peers regarding the processing, propagation, and queuing times' overheads results. Most importantly, we propose an efficient handshake packets' compression header, and we detailed a comparison of the above evaluation's criteria before and after the proposed compression. Obtained results are very encouraging and reinforce the efficiency of HIP DEX in IoT networks during the handshake process of constrained nodes.
Hörmann, Leander B., Pichler-Scheder, Markus, Kastl, Christian, Bernhard, Hans-Peter, Priller, Peter, Springer, Andreas.  2020.  Location-Based Trustworthiness of Wireless Sensor Nodes Using Optical Localization. 2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). :1–4.
A continually growing number of sensors is required for monitoring industrial processes and for continuous data acquisition from industrial plants and devices. The cabling of sensors represent a considerable effort and potential source of error, which can be avoided by using wireless sensor nodes. These wireless sensor nodes form a wireless sensor network (WSN) to efficiently transmit data to the destination. For the acceptance of WSNs in industry, it is important to build up networks with high trustworthiness. The trustworthiness of the WSN depends not only on a secure wireless communication but also on the ability to detect modifications at the wireless sensor nodes itself. This paper presents the enhancement of the WSN's trustworthiness using an optical localization system. It can be used for the preparation phase of the WSN and also during operation to track the positions of the wireless sensor nodes and detect spatial modification. The location information of the sensor nodes can also be used to rate their trustworthiness.
Zhu, Tian, Tong, Fei.  2020.  A Cluster-Based Cooperative Jamming Scheme for Secure Communication in Wireless Sensor Network. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). :1–5.
The environment of wireless sensor networks (WSNs) makes the communication not only have the broadcast nature of wireless transmission, but also be limited to the low power and communication capability of sensor equipment. Both of them make it hard to ensure the confidentiality of communication. In this paper, we propose a cluster-based cooperative jamming scheme based on physical layer security for WSNs. The mathematical principle of the scheme is based on the design principle of code division multiple access. By using the orthogonality of orthogonal vectors, the legitimate receiver can effectively eliminate the noise, which is generated by the cooperative jamming nodes to disturb the eavesdropper. This scheme enables the legitimate receiver to ensure a strong communication confidentiality even if there is no location or channel advantage comparing with eavesdroppers. Through extensive simulations, the security performance of the proposed scheme is investigated in terms of secrecy rate.
You, Guoping, Zhu, Yingli.  2020.  Structure and Key Technologies of Wireless Sensor Network. 2020 Cross Strait Radio Science Wireless Technology Conference (CSRSWTC). :1–2.
With the improvement of scientific and technological level in China, wireless sensor network technology has been widely promoted and applied, which has now been popularized to various fields of society from military defense. Wireless sensor network combines sensor technology, communication technology and computer technology together, and has the ability of information collection, transmission and processing. In this paper, the structure of wireless sensor network and node localization technology are briefly introduced, and the key technologies of wireless sensor network development are summarized from the four aspects of energy efficiency, node localization, data fusion and network security. As a detection system of perceiving the physical world, WSN is also facing challenges while developing rapidly.
JOUINI, Oumeyma, SETHOM, Kaouthar.  2020.  Physical Layer Security Proposal for Wireless Body Area Networks. 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME). :1–5.
Over the last few decades, and thanks to the advancement of embedded systems and wireless technologies, the wireless sensors network (WSN) are increasingly used in many fields. Many researches are being done on the use of WSN in Wireless body Area Network (WBAN) systems to facilitate and improve the quality of care and remote patient monitoring.The broadcast nature of wireless communications makes it difficult to hide transmitted signals from unauthorized users. To this end, Physical layer security is emerging as a promising paradigm to protect wireless communications against eavesdropping attacks. The primary contribution of this paper is achieving a minimum secrecy outage probability by using the jamming technique which can be used by the legitimate communication partner to increase the noise level of the eavesdropper and ensure higher secure communication rate. We also evaluate the effect of additional jammers on the security of the WBAN system.
El-Sobky, Mariam, Sarhan, Hisham, Abu-ElKheir, Mervat.  2020.  Security Assessment of the Contextual Multi-Armed Bandit - RL Algorithm for Link Adaptation. 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES). :514–519.
Industry is increasingly adopting Reinforcement Learning algorithms (RL) in production without thoroughly analyzing their security features. In addition to the potential threats that may arise if the functionality of these algorithms is compromised while in operation. One of the well-known RL algorithms is the Contextual Multi-Armed Bandit (CMAB) algorithm. In this paper, we explore how the CMAB can be used to solve the Link Adaptation problem - a well-known problem in the telecommunication industry by learning the optimal transmission parameters that will maximize a communication link's throughput. We analyze the potential vulnerabilities of the algorithm and how they may adversely affect link parameters computation. Additionally, we present a provable security assessment for the Contextual Multi-Armed Bandit Reinforcement Learning (CMAB-RL) algorithm in a network simulated environment using Ray. This is by demonstrating CMAB security vulnerabilities theoretically and practically. Some security controls are proposed for CMAB agent and the surrounding environment. In order to fix those vulnerabilities and mitigate the risk. These controls can be applied to other RL agents in order to design more robust and secure RL agents.
Lalouani, Wassila, Younis, Mohamed.  2020.  Machine Learning Enabled Secure Collection of Phasor Data in Smart Power Grid Networks. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :546–553.
In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time; thus it increases the PMU network resilience against message replay and FDI attempts since the adversary's messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.