scholarly journals Identifying Nodes with Maladaptive Behavior in WSN’s by Using Hybrid Incursion Identification Approach for Secure Wireless

2021 ◽  
Author(s):  
G. Amudha
Keyword(s):  
Neural Network ◽  
Detection Rate ◽  
Maladaptive Behavior ◽  
Sensor Nodes ◽  
Hybrid Technique ◽  
Exploratory Research ◽  
Sybil Attack ◽  
Feed Forward Neural Network ◽  
Wormhole Attack ◽  
Identification Approach

Abstract In this study, to detect attacks of WSNs, a Hybrid Incursion Identification Approach (HIIA) is proposed. To reduce the amount of Energy Consumption (EC) of the sensor nodes, the HIIA mechanism utilizes a cluster-oriented approach with the LEACH protocol. For misuse observation and anomaly recognition, with MPNN (Multilayer Perceptron Neural Network) depended on fuzzy rule sets, HIIA structure is utilized. To refer to various varieties of attackers and to harmonize the identification results, with appendicle NN, FFNN (Feed Forward Neural Network) is utilized, that means Sybil Attack (SA), Hello Flood Attack (HFA) and Wormhole Attack (WA). To detect a SA, Improved SA Algorithm developed. Similarly, to detect a WA, that particular method is developed by Wormhole Anti-Hybrid Technique. Using the distance and power of the signal, HFA is detected. An exploratory research is conveyed out in a group of nodes. The nodes that misbehave in them are all determined. This proposed method, detects the performance of the accuracy, precision-recall and EC. This proposed method also finds the WA Detection Rate, HFA detection rate and the SA Detection Rate, respectively.

scholarly journals Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rupinder Singh ◽  
Jatinder Singh ◽  
Ravinder Singh
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Hybrid Technique ◽  
Sybil Attack ◽  
Wormhole Attack ◽  
Positive Rate ◽  
Hello Flood

In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS) that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack). For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

scholarly journals Delta Ruled First Order Iterative Deep Neural Learning for Sybil and Wormhole Attacks Detection in Healthcare Wireless Sensor Network

2021 ◽  
Author(s):  
John Clement Sunder A ◽  
K.P. Sampoornam KP ◽  
R.Vinodkumar R
Keyword(s):  
Packet Loss ◽  
Detection Rate ◽  
Loss Rate ◽  
Attack Detection ◽  
Packet Loss Rate ◽  
Sensor Nodes ◽  
Wormhole Attack ◽  
First Order ◽  
Wormhole Attacks ◽  
Routing Performance

Abstract Detection and isolation of Sybil and wormhole attack nodes in healthcare WSN is a significant problem to be resolved. Few research works have been designed to identify Sybil and wormhole attack nodes in the network. However, the detection performance of Sybil and wormhole attack nodes was not effectual as the false alarm rate was higher. In order to overcome such limitations, Delta Ruled First Order Iterative Deep Learning based Intrusion Detection (DRFOIDL-ID) Technique is proposed. The DRFOIDL-ID Technique includes two main phase namely attack detection and isolation. The DRFOIDL-ID Technique constructs Delta Ruled First Order Iterative Deep Learning in attack detection phase with aim of detecting the occurrence of Sybil and wormhole attacks in healthcare WSN. After detecting the attack nodes, DRFOIDL-ID Technique carried outs isolation process with the objective of increasing the routing performance. During the isolation phase, DRFOIDL-ID Technique keep always the identified Sybil and wormhole attack nodes through transmitting the isolation messages to all sensor nodes in healthcare WSN. Hence, DRFOIDL-ID Technique improves the routing performance with lower packet loss rate. The DRFOIDL-ID Technique conducts the simulation process using factors such as attack detection rate, attack detection time, false alarm rate and packet loss rate with respect to a diverse number of sensor nodes and data packets. The simulation result proves that the DRFOIDL-ID Technique is able to improve the attack detection rate and also reduces the attack detection time as compared to state-of-the-art works.

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

scholarly journals A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

2021 ◽  
Vol 118 ◽  
pp. 103766
Author(s):  
Ahmed J. Aljaaf ◽  
Thakir M. Mohsin ◽  
Dhiya Al-Jumeily ◽  
Mohamed Alloghani
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Feed Forward Neural Network ◽  
Feed Forward

scholarly journals Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Minyi Dai ◽  
Mehmet F. Demirel ◽  
Yingyu Liang ◽  
Jia-Mian Hu
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Polycrystalline Materials ◽  
Feed Forward Neural Network ◽  
Microscopic Interactions ◽  
Physical Interactions ◽  
Graph Neural Networks ◽  
Individual Grains ◽  
Physical Features ◽  
Machine Learning Models

AbstractVarious machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline materials.

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