IoT-based smart healthcare system (SHS) allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the SHS generates enormous data which contains sensitive personal information. To protect patients’ privacy, we propose a secure blockchain (BC)-assisted access control scheme for SHS in fog computing. All the operations of users are recorded on the BC by smart contract in order to ensure transparency and reliability of the system. We present a BC-assisted multiauthority attribute-based encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the BC-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient.

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

Recent developments in intelligence technologies have led to an explosion in the use of connected and automated vehicles (CAVs). Unfortunately, these autonomous vehicles face an increasing risk of vulnerability due to various attacks. Vehicle intrusion detection mechanisms are widely employed to mitigate the threats. Although some works have addressed this issue, few works consider situations that the attack detection may be fooled via simultaneous attacking multiple sensors. In this paper, we propose a novel intrusion detection system integrated Space Dimension Model and Time Dimension Model based on sensor data fusion for countering both independent attack and confederate attack in automated vehicles. In Space Dimension Model, the correlations of multivariate in-vehicle sensor data among multiple sensors are utilized as the input of our optimized convolutional neural network (CNN) model. Especially, we construct vehicle state matrices to characterize the underlying correlation of data between each sensor and other sensors, and then input them into our network for classification. To describe the abrupt deviation caused by anomalous multivariate sensor data itself, we design Time Dimension Model to capture the sensor behaviors of vehicle state vectors at adjacent time by utilizing the Mahalanobis distance (MD) metric. Extensive empirical studies based on a real-world vehicular dataset demonstrate the effectiveness of our integrated anomaly detection mechanisms by a comparative analysis with two detection models that consider only Space Dimension and Time Dimension respectively. The method can outperform the related work under different scenarios 1) with the gain of up to 3.01% in accuracy and 3.04% in F1 score; 2) with ability of defending against confederate attack effectively.

Connected and automated vehicles (CAVs) involving massive advanced sensors and electronic control units (ECUs) bring intelligentization to the transportation system and conveniences to human mobility. Unfortunately, these automated vehicles face security threats due to complexity and connectivity. Especially, the existing in-vehicle network protocols (e.g., controller area network) lack security consideration, which is vulnerable to malicious attacks and puts people at large-scale severe risks. In this paper, we propose a novel anomaly detection model that integrates a continuous wavelet transform (CWT) and convolutional neural network (CNN) for an in-vehicle network. By transforming in-vehicle sensor signals in different segments, we adopt CWT to calculate wavelet coefficients for vehicle state image construction so that the model exploits both the time and frequency domain characteristics of the raw data, which can demonstrate more hidden patterns of vehicle events and improve the accuracy of the follow-up detection process. Our model constructs a two-dimensional continuous wavelet transform scalogram (CWTS) and utilizes it as an input into our optimized CNN. The proposed model is able to provide local transient characteristics of the signals so that it can detect anomaly deviations caused by malicious behaviors, and the model is effective for coping with various vehicle anomalies. The experiments show the superior performance of our proposed model under different anomaly scenarios. Compared with related works, the average accuracy and F1 score are improved by 2.51% and 2.46%.

Vehicular ad hoc networks (VANETs) in which vehicles act as the mobile nodes provide a wide variety of services, such as audio and video surveillance. However, such networks suffer from an important problem of service delivery reliability as the network performance degrades significantly in the presence of misbehaving vehicles. Most of the existing works either assume that vehicles' misbehaviors are constant or ignore heterogeneous traffic for multiservice (a mix of video, voice, and data traffic). In this paper, we investigate the problem of trust-based multiservice delivery via integrated misbehavior detection and tolerance for fault-aware VANETs. To model the effects of time-varying misbehaviors, modern fog computing could help to analyze and to store related data in VANETs while evaluating the dynamic trust weights based on attribute parameters of each vehicle. Then, we incorporate these weights into our service delivery framework that integrates trustworthy vehicle selection for misbehavior detection and uses differential resource allocation to achieve misbehavior tolerance. We present a multi-path selection criterion based on the trust evaluation and design a trust-aware heterogeneous traffic allocation algorithm over multiple routing paths. Finally, our scheme is evaluated using extensive simulations where we show that (i) approximately 12% - 40% higher successful service delivery can be achieved by our routing algorithm at the expense of acceptable delay loss compared to other three routing protocols; (ii) our trust-aware traffic allocation algorithm can gain 10% - 20% higher effective network utility and better fairness than that of the baseline solution; and (iii) the integrated scheme significantly improves both effective utility ratio and the path-usage proportion by comparing it with only routing scheme and only traffic allocation scheme.

Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly.