Monarch-EWA: Monarch-Earthworm-Based Secure Routing Protocol in IoT

Abstract

Routing in the Internet of Things (IoT) renders the protection against various network attacks as any attacker intrudes the routing mechanism for establishing the destructive mechanisms against the network, which insists the essentiality of the security protocols in IoT. Thus, the paper proposes a secure protocol based on an optimization algorithm, Monarch-Earthworm Algorithm (Monarch-EWA), which is the modification of the Monarch Butterfly algorithm using the Earthworm Optimization Algorithm (EWA) in order to render effective security to the network. Initially, the effective nodes are selected using the Deep Convolutional Neural Network (deep CNN) classifier based on the factors, trust and energy of the node, and stochastic gradient descent algorithm trains the deep CNN classifier. The secure nodes are involved in routing for which the secure multipath is chosen optimally using the proposed Monarch-EWA, which chooses the secure multipath based on the factors, energy and trust. The analysis of the proposed method in the presence of attacks, such as black hole, message replicate and distributed denial of service, reveals that the proposed method outperformed the existing methods. The proposed Monarch-EWA protocol acquired the maximal energy, throughput and detection rate of 0.2268 J, 48.2759% and 82.6231%, respectively, with the minimal delay of 0.0959 ms.

Fuzzy inference based feature selection and optimized deep learning for Advanced Persistent Threat attack detection

“…The various algorithms, such as Deer Hunting Optimization Algorithm (DHOA) + DeepCNN, 36,37 SSOA + DeepCNN, 36,38 SCA + DeepCNN, 34,36 and SFOA‐DeepCNN 33,36 are utilized for the comparative analysis.…”

“…The various algorithms, such as Deer Hunting Optimization Algorithm (DHOA) + DeepCNN, 36,37 SSOA + DeepCNN, 36,38 SCA + DeepCNN, 34,36 and SFOA-DeepCNN 33,36 are utilized for the comparative analysis. Hence, the improved percentage of SFCA + DeepCNN with the existing methods is 5.65%, 3.84%, 2.60%, and 1.17%.…”

“…The testing accuracy of SFCA_DeepCNN is assessed with different population size, which is exhibited in Figure6A. The testing accuracy of DHOA + DeepCNN,36,37 SSOA + DeepCNN,36,38 SCA + DeepCNN,34,36 SFOA-DeepCNN33,36 and proposed SFCA + DeepCNN is 0.895, 0.904, 0.912, 0.924, and 0.938 when 50 as the population size. The percentage improvement of testing accuracy is 4.57%, 3.66%, 2.79%, and 1.50%.…”

JASBO: Jaya Average Subtraction Based Optimization with Deep Learning Model for Multi-Classification of Infectious Disease from Unstructured Data

“…Initially, an input text sentence is acquired from the dataset 22 , which is then fed to the first part, the ID-Net, for the multi-classification of infectious diseases. This ID-Net is the new DL architecture based on the integration of CNN 23,24 and Bi-LSTM 25 , where text input is given to the tokenization layer. The obtained tokens are then fed to CNN, where character-level features are acquired.…”

Simultaneous wireless information and power transmission‐based power transfer and energy prediction for efficient communication with golden Taylor sea lion optimization in wireless sensor network

“…The architecture of Deep CNN [49,50] composes of three layers, namely, convolutional, pooling, and fully connected layers. The functioning of each layer is the extraction of features, subsampling, and classification.…”