With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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Various morphologies of ZnO nanostructures can be obtained through a novel method, incorporating electrochemical corrosion with three modes: liquid membrane and above and below the water line in partial immersion. X-ray diffraction (XRD) patterns, high-resolution transmission electron microscopy (HRTEM), and selected area electron diffraction (SAED) are employed to characterize their structure. The mechanism of the growth of NRs is proposed as electrochemical corrosion and oriented attachment, which occur in a liquid membrane or partial immersion in a vapor membrane. The evolution of ZnO nanostructures such as nanorods, nanowires, nanopins, and nanodentrites is observed, and the influence of concentration, reaction time, additives, state of substrate, membrane thickness, and solvent on the morphology of ZnO is investigated. Optical properties of ZnO nanostructures are studied by using UV-visible absorption spectra and photoluminescence (PL). Their optical gaps vary from different morphologies. Among the studied samples, short nanorods show the largest optical gap, while big nanorods present the smallest value of optical gap. PL properties demonstrate that peaks of near-band emission and defect-related luminescence are basically in the same position. However, intensities for different morphologies are of different values, and short nanorods exhibit the best near-band emissions.

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