Blockchain-enabled secure communication for unmanned aerial vehicle (UAV) networks

While 5G can provide high-speed Internet connectivity and over-the-horizon control for Unmanned Aerial Vehicles (UAVs), authentication becomes a key security component in 5G-enabled UAVs. This is due to fact that the communicating entities in the network mostly uses unsecured communication channel to exchange critical surveillance data. Authentication thus plays a crucial role in the 5G-enabled UAV network, providing a range of security services such as credential privacy, Session-Key (SK) security, and secure mutual authentication. However, transparency, anonymity, traceability and centralized control are few major security requirements that cannot be fulfilled by the traditional authentication schemes. One of the upcoming technologies that can provide a solution for present centralized 5G-enabled UAV network is blockchain-based authentication scheme. Motivated from aforementioned discussion, this paper presents a Permissioned Blockchain empowered Secure Authentication and Key Agreement framework in 5G-enabled UAVs. In this framework, first an authentication phase between UAV-to-UAV, UAV-to-Edge Server (ES) and Edge-to-Cloud Server (CS) supporting mutual authentication and key agreement is proposed. The authenticated surveillance data collected from UAV is used by the peer-to-peer CS for transaction verification, block creation and addition using smart contract-based consensus mechanism. The practical implementation of framework shows the effectiveness of the proposed approach. © 2022 ACM

Deep learning-based intrusion detection approach for securing industrial Internet of Things

The widespread deployment of the Internet of Things (IoT) into critical sectors such as industrial and manufacturing has resulted in the Industrial Internet of Things (IIoT). The IIoT consists of sensors, actuators, and smart devices that communicate with one another to optimize manufacturing and industrial processes. Although IIoT provides various benefits to both service providers and consumers, security and privacy remain a big challenge. An intrusion detection system (IDS) has been utilized to mitigate cyberattacks in such a connected network. However, many existing solutions for IDS in IIoT suffer from the lack of comprehensiveness of the types of attack the network is exposed to, high feature dimension, models built on out-of-date datasets, and a lack of focus on the problem of imbalanced datasets. To address the aforementioned issues, we propose an intelligent detection system for identifying cyberattacks in Industrial IoT networks. The proposed model uses the singular value decomposition (SVD) technique to reduce data features and improve detection results. We use the synthetic minority over-sampling (SMOTE) technique to avoid over-fitting and under-fitting issues that result in biased classification. Several machine learning and deep learning algorithms have been implemented to classify data for binary and multi-class classification. We evaluate the efficacy of the proposed intelligent model on ToN_IoT dataset. The proposed approach achieved an accuracy rate of 99.99% and a reduced error rate of 0.001% for binary classification, and an accuracy rate of 99.98% and a reduced error rate of 0.016% for multi-class classification