A federated learning framework for cyberattack detection in vehicular sensor networks

Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors

A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

The Industrial Internet of Things (IIoT) refers to the use of traditional Internet of Things (IoT) concepts in industrial sectors and applications. IIoT has several applications in smart homes, smart cities, smart grids, connected cars, and supply chain management. However, these systems are being more frequently targeted by cybercriminals. Deep learning and big data analytics have great potential in designing and developing robust security mechanisms for IIoT networks. In this paper, a novel hybrid deep random neural network (HDRaNN) for cyberattack detection in the IIoT is presented. The HDRaNN combines a deep random neural network and a multilayer perceptron with dropout regularization. The proposed technique is evaluated using two IIoT security-related datasets: (i) DS2OS and (ii) UNSW-NB15. The performance of the proposed scheme is analyzed through a number of performance metrics such as accuracy, precision, recall, F1 score, log loss, Region of Convergence (ROC), and Area Under the Curve (AUC). The HDRaNN classified 16 different types of cyberattacks using with higher accuracy of 98% and 99% for DS2OS and UNSW-NB15, respectively. To measure the effectiveness of the proposed scheme, the performance metrics are also compared with several state-of-the-art attack detection algorithms. The findings of HDRaNN proved its superior performance over other DL-based schemes. The deployment perspective of the proposed work is also highlighted in this work

Evaluation and extension of the two-site, two-step model for binding and activation of the chemokine receptor CCR1

© 2019 Sanchez et al. Published under exclusive license by The American Society for Biochemistry and Molecular Biology, Inc. Interactions between secreted immune proteins called chemokines and their cognate G protein– coupled receptors regulate the trafficking of leukocytes in inflammatory responses. The two-site, two-step model describes these interactions. It involves initial binding of the chemokine N-loop/3 region to the receptor’s N-terminal region and subsequent insertion of the chemokine N-terminal region into the transmembrane helical bundle of the receptor concurrent with receptor activation. Here, we test aspects of this model with C-C motif chemokine receptor 1 (CCR1) and several chemokine ligands. First, we compared the chemokine-binding affinities of CCR1 with those of peptides corresponding to the CCR1 N-terminal region. Relatively low affinities of the peptides and poor correlations between CCR1 and peptide affinities indicated that other regions of the receptor may contribute to binding affinity. Second, we evaluated the contributions of the two CCR1-interacting regions of the cognate chemokine ligand CCL7 (formerly monocyte chemoattractant protein-3 (MCP-3)) using chimeras between CCL7 and the non-cognate ligand CCL2 (formerly MCP-1). The results revealed that the chemokine N-terminal region contributes significantly to binding affinity but that differences in binding affinity do not completely account for differences in receptor activation. On the basis of these observations, we propose an elaboration of the two-site, two-step model—the “three-step” model—in which initial interactions of the first site result in low-affinity, nonspecific binding; rate-limiting engagement of the second site enables high-affinity, specific binding; and subsequent conformational rearrangement gives rise to receptor activation

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework

Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network

The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, IoT applications and networks demand lightweight, fast, and flexible security solutions to overcome these challenges. In this regard, artificial-intelligence-based solutions with Big Data analytics can produce promising results in the field of cybersecurity. This article proposes a lightweight dense random neural network (DnRaNN) for intrusion detection in the IoT. The proposed scheme is well suited for implementation in resource-constrained IoT networks due to its inherent improved generalization capabilities and distributed nature. The suggested model was evaluated by conducting extensive experiments on a new generation IoT security dataset ToN_IoT. All the experiments were conducted under different hyperparameters and the efficiency of the proposed DnRaNN was evaluated through multiple performance metrics. The findings of the proposed study provide recommendations and insights in binary class and multiclass scenarios. The proposed DnRaNN model attained attack detection accuracy of 99.14% and 99.05% for binary class and multiclass classifications, respectively

Key determinants of selective binding and activation by the monocyte chemoattractant proteins at the chemokine receptor CCR2

Chemokines and their receptors collectively orchestrate the trafficking of leukocytes in normal immune function and inflammatory diseases. Different chemokines can induce distinct responses at the same receptor. In comparison to monocyte chemoattractant protein-1 (MCP-1; also known as CCL2), the chemokines MCP-2 (CCL8) and MCP-3 (CCL7) are partial agonists of their shared receptor CCR2, a key regulator of the trafficking of monocytes and macrophages that contribute to the pathology of atherosclerosis, obesity, and type 2 diabetes. Through experiments with chimeras of MCP-1 and MCP-3, we identified the chemokine amino-terminal region as being the primary determinant of both the binding and signaling selectivity of these two chemokines at CCR2. Analysis of CCR2 mutants showed that the chemokine amino terminus interacts with the major subpocket in the transmembrane helical bundle of CCR2, which is distinct fromthe interactions of some other chemokines with the minor subpockets of their receptors. These results suggest the major subpocket as a target for the development of small-molecule inhibitors of CCR2. 2017 © The Authors

Quantifying and mitigating couples’ co-located smartphone usage

There is a growing concern that many individuals overuse smartphones in the presence of their family members, which sometimes creates frustration and relationship stress among them. Though couples remain co-located for a substantial portion of each day, very little effort has been made in quantifying couples’ co-located smartphone usage and exploring ways to reduce it. As such, I first conduct a two-week study quantifying couples' co-located smartphone usage. Results show that couples spend a considerable amount of time on smartphones while being co-located than when they are not with their partners. Inspired by the results, I conducted a four-week long study exploring smartphone notifications as a medium to motivate couples to limit their co-located smartphone use. The results suggest that notification has potential to help couples in reducing their co-located usage. Based on the findings, I present a set of recommendations for designing solutions to limit smartphone usage among couples.Science, Irving K. Barber Faculty of (Okanagan)Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)Graduat