This paper proposes a novel architecture based on blockchain technology to enhance the dependability and safety of wireless sensor networks (WSN) by authenticating WSN nodes. In a WSN, sensor nodes collect and transmit data to cluster heads (CHs) for further processing. The proposed model employs the distance and residual energy-based low-energy adaptive clustering hierarchy (ECO-LEACH) protocol to replace CHs with ordinary nodes and the Interplanetary File System (IPFS) for storing data. In addition, consensus based on proof of authority (PoA) is used to validate transactions, reducing the computational cost associated with proof of work. The proposed system was evaluated using simulations with 300 sensor nodes and compared with other protocols, including LEACH, DDR-LEACH, PEGASIS, and LEACH-PSO. The simulation results showed that the proposed ECO-LEACH outperformed the other protocols in terms of energy consumption, throughput achieved, and network lifetime improvement. Specifically, the proposed system consumed 23.5J for 300 sensor nodes, achieved 687.5 kbps, and improved the network's lifetime by 4.12 seconds for 50 rounds. Overall, this paper provides a reliable and secure solution for authenticating WSN nodes, enhancing data transfer safety, and dependability. The proposed architecture offers a promising approach for addressing the challenges of WSN design using blockchain technology and PoA consensus. The comparative analysis shows that the proposed ECO-LEACH protocol outperforms other protocols in terms of energy consumption, throughput achieved, and network lifetime improvement for 300 sensor nodes.

The Domain Name System (DNS) is an essential component of the internet infrastructure. Due to its importance, securing DNS becomes a necessity for current and future networks. Various DNS security architecture have been developed in order to provide security services; such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Unfortunately, these security architectures, especially DoT, are limited and are open to a number of performance issues. In this paper, we evaluate the present state of DNS security architecture, and we would see clearly that existing DNS security architectures are insufficient to secure DNS data transiting over the network; considering the growing cybersecurity landscape. On this note, we propose the need and adoption of a security architecture named Adaptive Security Architecture. Adaptive Security Architecture is devised to guard against identified threats, and anticipate unidentified threats in a manner similar to the immune-response system of human. Basically, mimicking nature’s biodiversity as the fundamental means of effective attack responses. Finally, we conclude by an analysis to prove the need to improve DNS security architecture.

Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' quality of life. Computer-aided diagnosis (CAD) systems that accurately classify tumor images have been challenging to develop. Deep convolutional neural network (DCNN) models have shown significant potential for tumor detection, and outperform traditional deep neural network models. In this study, a novel framework based on two pre-trained deep convolutional architectures (VGG16 and EfficientNetB0) is proposed for classifying different types of brain tumors, including meningioma, glioma, and pituitary tumors. Features are extracted from MR images using each architecture and merged before feeding them into machine learning algorithms for tumor classification. The proposed approach achieves a training accuracy of 98% and a test accuracy of 99% on the brain-tumor-classification-mri dataset available on Kaggle and btc_navoneel. The model shows promise to improve the accuracy and generalizability of medical image classification for better clinical decision support, ultimately leading to improved patient outcomes.