With the integration of the Internet of Things (IoT) in the field of transportation, the Internet of Vehicles (IoV) turned to be a vital method for designing Smart Transportation Systems (STS). STS consist of various interconnected vehicles and transportation infrastructure exposed to cyber intrusion due to the broad usage of software and the initiation of wireless interfaces. This study proposes a federated deep learning-based intrusion detection framework (FED-IDS) to efficiently detect attacks by offloading the learning process from servers to distributed vehicular edge nodes. FED-IDS introduces a context-aware transformer network to learn spatial-temporal representations of vehicular traffic flows necessary for classifying different categories of attacks. Blockchain-managed federated training is presented to enable multiple edge nodes to offer secure, distributed, and reliable training without the need for centralized authority. In the blockchain, miners confirm the distributed local updates from participating vehicles to stop unreliable updates from being deposited on the blockchain. The experiments on two public datasets (i.e., Car-Hacking, TON_IoT) demonstrated the efficiency of FED-IDS against state-of-the-art approaches. It reveals the credibility of securing networks of intelligent transportation systems against cyber-attacks.

The extensive propagation of industrial Internet of Things (IIoT) technologies has encouraged intruders to initiate a variety of attacks that need to be identified to maintain the security of end-user data and the safety of services offered by service providers. Deep learning (DL), especially recurrent approaches, has been applied successfully to the analysis of IIoT forensics but their key challenge of recurrent DL models is that they struggle with long traffic sequences and cannot be parallelized. Multihead attention (MHA) tried to address this shortfall but failed to capture the local representation of IIoT traffic sequences. In this article, we propose a forensics-based DL model (called Deep-IFS) to identify intrusions in IIoT traffic. The model learns local representations using local gated recurrent unit (LocalGRU), and introduces an MHA layer to capture and learn global representation (i.e., long-range dependencies). A residual connection between layers is designed to prevent information loss. Another challenge facing the current IIoT forensics frameworks is their limited scalability, limiting performance in handling Big IIoT traffic data produced by IIoT devices. This challenge is addressed by deploying and training the proposed Deep-IFS in a fog computing environment. The intrusion identification becomes scalable by distributing the computation and the IIoT traffic data across worker fog nodes for training the model. The master fog node is responsible for sharing training parameters and aggregating worker node output. The aggregated classification output is subsequently passed to the cloud platform for mitigating attacks. Empirical results on the Bot-IIoT dataset demonstrate that the developed distributed Deep-IFS can effectively handle Big IIoT traffic data compared with the present centralized DL-based forensics techniques. Further, the results validate the robustness of the proposed Deep-IFS across various evaluation measures.

The rapid growth of the Internet of Things (IoT) technologies has generated a huge amount of traffic that can be exploited for detecting intrusions through IoT networks. Despite the great effort made in annotating IoT traffic records, the number of labeled records is still very small, increasing the difficulty in recognizing attacks and intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (SS-Deep-ID), in which we propose a multi-scale residual temporal convolutional (MS-Res) module to finetune the network capability in learning Spatio-temporal representations. An improved traffic attention (TA) mechanism is introduced to estimate the importance score that helps the model to concentrate on important information during learning. Furthermore, a hierarchical semi-supervised training method is introduced which takes into account the sequential characteristics of the IoT traffic data during training. The proposed SS-Deep-ID easily integrated into Fog-enabled IoT network to offer efficient real-time intrusion detection. Lastly, empirical evaluations on two recent datasets (CIC-IDS2017 and CIC-IDS2018) demonstrate that SS-Deep-ID improves the efficiency of intrusion detection and increases the robustness of performance while maintaining computational efficiency.

Human activity recognition (HAR) has been regarded as an indispensable part of many smart home systems and smart healthcare applications. Specifically, HAR is of great importance in the Internet of Healthcare Things (IoHT), owing to the rapid proliferation of Internet of Things (IoT) technologies embedded in various smart appliances and wearable devices (such as smartphones and smartwatches) that have a pervasive impact on an individual’s life. The inertial sensors of smartphones generate massive amounts of multidimensional time-series data, which can be exploited effectively for HAR purposes. Unlike traditional approaches, deep learning techniques are the most suitable choice for such multivariate streams. In this study, we introduce a supervised dual-channel model that comprises long short-term memory (LSTM), followed by an attention mechanism for the temporal fusion of inertial sensor data concurrent with a convolutional residual network for the spatial fusion of sensor data. We also introduce an adaptive channel-squeezing operation to fine-tune convolutional a neural network feature extraction capability by exploiting multichannel dependency. Finally, two widely available and public HAR datasets are used in experiments to evaluate the performance of our model. The results demonstrate that our proposed approach can overcame state-of-the-art methods.