GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet

This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)measurements.We propose a detection method based on improved Capsule Neural Network(CapsNet)to handle this attack.In the improved CapsNet,the gated recurrent unit(GRU)is added to the front of the full connection layer of the CapsNet.The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid.The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously,which can accurately distinguish the attacked data from the normal data,to improve the detection accuracy.Finally,simulation experiments are carried out on IEEE 14-,IEEE 118-bus systems.The experimental results show that compared with other detection methods,our method is proved to be more efficient.

DeepPOSE:Detecting GPS spoofing attack via deep recurrent neural network

The Global Positioning System(GPS)has become a foundation for most location-based services and navigation systems,such as autonomous vehicles,drones,ships,and wearable devices.However,it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools.Pervasive tools,such as Fake GPS,Lockito,and software-defined radio,enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected.Furthermore,it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors.To this end,we propose DeepPOSE,a deep learning model,to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms.Our design uses a convolutional and recurrent neural network to reduce the noise,to recover a vehicle's real-time trajectory from multiple sensor inputs.We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map,to smartly eliminate the accumulation of errors on the trajectory estimation.The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack.Compared with the existing method,the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.

Online Pattern Recognition and Data Correction of PMU Data Under GPS Spoofing Attack

Smart grids are increasingly dependent on data with the rapid development of communication and measurement.As one of the important data sources of smart grids,phasor measurement unit(PMU)is facing the high risk from attacks.Compared with cyber attacks,global position system(GPS)spoofing attacks(GSAs)are easier to implement because they can be exploited by portable devices,without the need to access the physical system.Therefore,this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference(PAD)deviation.Specifically,this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA,i.e.,the step attack and the ramp attack.Then,the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed,which does not require the line parameters.After obtaining the estimated PAD deviations,the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations.Finally,with the case studies,the effectiveness of the proposed method is demonstrated,and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.

Rethinking multi-spatial information for transferable adversarial attacks on speaker recognition systems

Adversarial attacks have been posing significant security concerns to intelligent systems,such as speaker recognition systems(SRSs).Most attacks assume the neural networks in the systems are known beforehand,while black-box attacks are proposed without such information to meet practical situations.Existing black-box attacks improve trans-ferability by integrating multiple models or training on multiple datasets,but these methods are costly.Motivated by the optimisation strategy with spatial information on the perturbed paths and samples,we propose a Dual Spatial Momentum Iterative Fast Gradient Sign Method(DS-MI-FGSM)to improve the transferability of black-box at-tacks against SRSs.Specifically,DS-MI-FGSM only needs a single data and one model as the input;by extending to the data and model neighbouring spaces,it generates adver-sarial examples against the integrating models.To reduce the risk of overfitting,DS-MI-FGSM also introduces gradient masking to improve transferability.The authors conduct extensive experiments regarding the speaker recognition task,and the results demonstrate the effectiveness of their method,which can achieve up to 92%attack success rate on the victim model in black-box scenarios with only one known model.

Physiological-physical feature fusion for automatic voice spoofing detection

Biometric speech recognition systems are often subject to various spoofing attacks,the most common of which are speech synthesis and speech conversion attacks.These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks,which can compromise the security of this system.Researchers have made many efforts to address this problem,and the existing studies have used the physical features of speech to identify spoofing attacks.However,recent studies have shown that speech contains a large number of physiological features related to the human face.For example,we can determine the speaker's gender,age,mouth shape,and other information by voice.Inspired by the above researches,we propose a spoofing attack recognition method based on physiological-physical features fusion.This method involves feature extraction,a densely connected convolutional neural network with squeeze and excitation block(SE-DenseNet),and feature fusion strategies.We first extract physiological features in audio from a pretrained convolutional network.Then we use SE-DenseNet to extract physical features.Such a dense connection pattern has high parameter efficiency,and squeeze and excitation blocks can enhance the transmission of the feature.Finally,we integrate the two features into the classification network to identify the spoofing attacks.Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection.In the logical access scenario,our model improves the tandem decision cost function and equal error rate scores by 5%and 7%,respectively,compared to existing methods.