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.

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

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/ß3 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.

Mechanisms of Regulation of the Chemokine-Receptor Network.

The interactions of chemokines with their G protein-coupled receptors promote the migration of leukocytes during normal immune function and as a key aspect of the inflammatory response to tissue injury or infection. This review summarizes the major cellular and biochemical mechanisms by which the interactions of chemokines with chemokine receptors are regulated, including: selective and competitive binding interactions; genetic polymorphisms; mRNA splice variation; variation of expression, degradation and localization; down-regulation by atypical (decoy) receptors; interactions with cell-surface glycosaminoglycans; post-translational modifications; oligomerization; alternative signaling responses; and binding to natural or pharmacological inhibitors.