scholarly journals An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications

2021 ◽  
Vol 11 (4) ◽  
pp. 7326-7331
Author(s):  
N. K. Al-Shammari ◽  
T. H. Syed ◽  
M. B. Syed
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Medical Devices ◽  
Search Algorithm ◽  
Data Extraction ◽  
Access Time ◽  
Healthcare Applications ◽  
Detection Techniques ◽  
Smart Healthcare ◽  
Layer Extraction

The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.

scholarly journals INTRUSION RECOGNITION USING NEURAL NETWORKS

2014 ◽  
pp. 37-42
Author(s):  
Vladimir Golovko ◽  
Pavel Kochurko
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Computer Network ◽  
Anomalous Behavior ◽  
Intrusion Detection Systems ◽  
Data Set ◽  
Detection Techniques ◽  
Behavior Detection ◽  
Detection Systems

Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.

Water Moth Search Algorithm-based Deep Training for Intrusion Detection in IoT

2021 ◽  
Author(s):  
Rekha P. M. ◽  
Nagamani H. Shahapure ◽  
Punitha M. ◽  
Sudha P. R.
Keyword(s):  
Intrusion Detection ◽  
Water Wave ◽  
Search Algorithm ◽  
Detection Techniques ◽  
Redundant Data ◽  
Search Optimization ◽  
Specificity And Sensitivity ◽  
Wrapper Approach ◽  
Deep Recurrent Neural Network ◽  
Novel Method

The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.

scholarly journals NEURAL NETWORK APPROACHES FOR INTRUSION DETECTION AND RECOGNITION

2014 ◽  
pp. 118-125
Author(s):  
Vladimir Golovko ◽  
Leanid Vaitsekhovich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intrusion Detection ◽  
Data Extraction ◽  
Principal Component ◽  
Computational Time ◽  
Multilayer Perceptrons ◽  
Neural Network Models ◽  
Time Operation ◽  
Detection And Recognition

Most current Intrusion Detection Systems (IDS) examine all data features to detect intrusion. Also existing intrusion detection approaches have some limitations, namely impossibility to process large number of audit data for real-time operation, low detection and recognition accuracy. To overcome these limitations, we apply modular neural network models to detect and recognize attacks in computer networks. It is based on combination of principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). PCA networks are employed for important data extraction and to reduce high dimensional data vectors. We present two PCA neural networks for feature extraction: linear PCA (LPCA) and nonlinear PCA (NPCA). MLP is employed to detect and recognize attacks using feature-extracted data instead of original data. The proposed approaches are tested using KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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