scholarly journals Rogue device discrimination in ZigBee networks using wavelet transform and autoencoders

2020 ◽  
Author(s):  
Mohammad Amin Haji Bagheri Fard ◽  
Jean-Yves Chouinard ◽  
Bernard Lebel
Keyword(s):  
Data Security ◽  
Classification Accuracy ◽  
Wavelet Decomposition ◽  
Wireless Systems ◽  
Physical Layer ◽  
Sensitive Information ◽  
Model Classification ◽  
High Classification Accuracy ◽  
Original Dataset ◽  
Desktop Computers

Abstract In modern wireless systems such as ZigBee, sensitive information which is produced by the network is transmitted through different wired or wireless nodes. Providing the requisites of communication between diverse communication system types, such as mobiles, laptops, and desktop computers, does increase the risk of being attacked by outside nodes. Malicious (or unintentional) threats, such as trying to obtain unauthorized accessibility to the network, increase the requirements of data security against the rogue devices trying to tamper with the identity of authorized devices. In such manner, focusing on Radio Frequency Distinct Native Attributes (RF-DNA) of features extracted from physical layer responses (referred to as preambles) of ZigBee devices, a dataset of distinguishable features of all devices can be produced which can be exploited for the detection and rejection of spoofing/rogue devices. Through this procedure, distinction of devices manufactured by the different/same producer(s) can be realized resulting in an improvement of classification system accuracy. The two most challenging problems in initiating RF-DNA are (1) the mechanism of features extraction in the generation of a dataset in the most effective way for model classification and (2) the design of an efficient model for device discrimination of spoofing/rogue devices. In this paper, we analyze the physical layer features of ZigBee devices and present methods based on deep learning algorithms to achieve high classification accuracy, based on wavelet decomposition and on the autoencoder representation of the original dataset.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

scholarly journals Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1367
Author(s):  
Raghida El El Saj ◽  
Ehsan Sedgh Sedgh Gooya ◽  
Ayman Alfalou ◽  
Mohamad Khalil
Keyword(s):  
Neural Networks ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Encrypted Data ◽  
High Classification Accuracy ◽  
Hide Information ◽  
Network Methods ◽  
Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

Enhanced Backpropagation Approach for Identifying Genetic Disease

2014 ◽  
Vol 622 ◽  
pp. 75-80
Author(s):  
Baskar Nisha ◽  
B. Madasamy ◽  
J.Jebamalar Tamilselvi
Keyword(s):  
Machine Learning ◽  
Genetic Disease ◽  
Classification Accuracy ◽  
Gene Selection ◽  
New Classification ◽  
Backpropagation Algorithm ◽  
High Classification Accuracy ◽  
Disease Associations ◽  
Disease Analysis

Classification of data on genetic disease is a useful application in microarray analysis. The genetic disease data analysis has the potential for discovering the diseased genes which may be the signature of certain diseases. Machine learning methodologies and data mining techniques are used to predict genetic disease associations of bio informatics data. Among numerous existing methods for gene selection, Backpropagation algorithm has become one of the leading methods and it gives less classification accuracy. It aims to develop a new classification algorithm (Enhanced Backpropagation Algorithm) for genetic disease analysis. Knowledge derived by the Enhanced Backpropagation Algorithm has high classification accuracy with the ability to identify the most significant genes.

scholarly journals Privacy Preservation using (L, D) Inference Model Based on Dependency Identification Information Gain

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1170-1173
Keyword(s):  
Data Mining ◽  
Information Gain ◽  
Original Data ◽  
Perturbation Approach ◽  
Sensitive Information ◽  
Functional Dependencies ◽  
Inference Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Original Dataset

The improvement of an information processing and Memory capacity, the vast amount of data is collected for various data analyses purposes. Data mining techniques are used to get knowledgeable information. The process of extraction of data by using data mining techniques the data get discovered publically and this leads to breaches of specific privacy data. Privacypreserving data mining is used to provide to protection of sensitive information from unwanted or unsanctioned disclosure. In this paper, we analysis the problem of discovering similarity checks for functional dependencies from a given dataset such that application of algorithm (l, d) inference with generalization can anonymised the micro data without loss in utility. [8] This work has presented Functional dependency based perturbation approach which hides sensitive information from the user, by applying (l, d) inference model on the dependency attributes based on Information Gain. This approach works on both categorical and numerical attributes. The perturbed data set does not affects the original dataset it maintains the same or very comparable patterns as the original data set. Hence the utility of the application is always high, when compared to other data mining techniques. The accuracy of the original and perturbed datasets is compared and analysed using tools, data mining classification algorithm.

Data Security in Wired and Wireless Systems

2019 ◽  
pp. 1213-1240
Author(s):  
Abhinav Prakash ◽  
Dharma Prakash Agarwal
Keyword(s):  
Wireless Network ◽  
Data Security ◽  
Security Protocols ◽  
Wireless Systems ◽  
Main Tool ◽  
Security Protocol ◽  
Network Data ◽  
Wireless Technologies ◽  
Secured Communication ◽  
Different Parts

The issues related to network data security were identified shortly after the inception of the first wired network. Initial protocols relied heavily on obscurity as the main tool for security provisions. Hacking into a wired network requires physically tapping into the wire link on which the data is being transferred. Both these factors seemed to work hand in hand and made secured communication somewhat possible using simple protocols. Then came the wireless network which radically changed the field and associated environment. How do you secure something that freely travels through the air as a medium? Furthermore, wireless technology empowered devices to be mobile, making it harder for security protocols to identify and locate a malicious device in the network while making it easier for hackers to access different parts of the network while moving around. Quite often, the discussion centered on the question: Is it even possible to provide complete security in a wireless network? It can be debated that wireless networks and perfect data security are mutually exclusive. Availability of latest wideband wireless technologies have diminished predominantly large gap between the network capacities of a wireless network versus a wired one. Regardless, the physical medium limitation still exists for a wired network. Hence, security is a way more complicated and harder goal to achieve for a wireless network (Imai, Rahman, & Kobara, 2006). So, it can be safely assumed that a security protocol that is robust for a wireless network will provide at least equal if not better level of security in a similar wired network. Henceforth, we will talk about security essentially in a wireless network and readers should assume it to be equally applicable to a wired network.

scholarly journals Wireless physical layer security

2016 ◽  
Vol 114 (1) ◽  
pp. 19-26 ◽  
Author(s):  
H. Vincent Poor ◽  
Rafael F. Schaefer
Keyword(s):  
Wireless Networks ◽  
Physical Layer Security ◽  
Secure Communication ◽  
Radio Channel ◽  
Wireless Systems ◽  
Data Encryption ◽  
Physical Layer ◽  
Fundamental Limits ◽  
Security Information ◽  
Networking Architectures

Security in wireless networks has traditionally been considered to be an issue to be addressed separately from the physical radio transmission aspects of wireless systems. However, with the emergence of new networking architectures that are not amenable to traditional methods of secure communication such as data encryption, there has been an increase in interest in the potential of the physical properties of the radio channel itself to provide communications security. Information theory provides a natural framework for the study of this issue, and there has been considerable recent research devoted to using this framework to develop a greater understanding of the fundamental ability of the so-called physical layer to provide security in wireless networks. Moreover, this approach is also suggestive in many cases of coding techniques that can approach fundamental limits in practice and of techniques for other security tasks such as authentication. This paper provides an overview of these developments.

scholarly journals Satellite Constellation Reveals Crop Growth Patterns and Improves Mapping Accuracy of Cropping Practices for Subtropical Small-Scale Fields in Japan

2020 ◽  
Vol 12 (15) ◽  
pp. 2419
Author(s):  
Asahi Sakuma ◽  
Hiroya Yamano
Keyword(s):  
Classification Accuracy ◽  
Vegetation Indices ◽  
Temporal Variations ◽  
Crop Growth ◽  
Growth Patterns ◽  
Small Scale ◽  
Satellite Constellation ◽  
High Classification Accuracy ◽  
Crop Types ◽  
Spatio Temporal

Mapping of agricultural crop types and practices is important for setting up agricultural production plans and environmental conservation measures. Sugarcane is a major tropical and subtropical crop; in general, it is grown in small fields with large spatio-temporal variations due to various crop management practices, and satellite observations of sugarcane cultivation areas are often obscured by clouds. Surface information with high spatio-temporal resolution obtained through the use of emerging satellite constellation technology can be used to track crop growth patterns with high resolution. In this study, we used Planet Dove imagery to reveal crop growth patterns and to map crop types and practices on subtropical Kumejima Island, Japan (lat. 26°21′01.1″ N, long. 126°46′16.0″ E). We eliminated misregistration between the red-green-blue (RGB) and near-infrared band imagery, and generated a time series of seven vegetation indices to track crop growth patterns. Using the Random Forest algorithm, we classified eight crop types and practices in the sugarcane. All the vegetation indices tested showed high classification accuracy, and the normalized difference vegetation index (NDVI) had an overall accuracy of 0.93 and Kappa of 0.92 range of accuracy for different crop types and practices in the study area. The results for the user’s and producer’s accuracy of each class were good. Analysis of the importance of variables indicated that five image sets are most important for achieving high classification accuracy: Two image sets of the spring and summer sugarcane plantings in each year of a two-year observation period, and one just before harvesting in the second year. We conclude that high-temporal-resolution time series images obtained by a satellite constellation are very effective in small-scale agricultural mapping with large spatio-temporal variations.

EXPOSING DIGITAL VIDEO LOGO-REMOVAL FORGERY BY INCONSISTENCY OF BLUR

2010 ◽  
Vol 24 (07) ◽  
pp. 1027-1046 ◽  
Author(s):  
YUTING SU ◽  
JING ZHANG ◽  
YU HAN ◽  
JING CHEN ◽  
QINGZHONG LIU
Keyword(s):  
Classification Accuracy ◽  
Digital Video ◽  
Support Vector ◽  
Forgery Detection ◽  
Computation Cost ◽  
Statistical Property ◽  
High Classification Accuracy ◽  
Novel Approach ◽  
Fine Classification ◽  
Reference Areas

A novel approach for detecting video logo-removal forgery is proposed by measuring inconsistency of blur. Our approach is based on the assumption that if a digital video undergoes logo-removal forgery; the blurriness of the forged region is expected to be different as compared to the nontampered parts of the video. Blurriness is first estimated by analyzing the spatial and temporal statistical property of logo areas, and suspicious areas are roughly located; then features are extracted and a fine classification is implemented by applying support vector machine (SVM) to extract features. If the suspicious areas and the reference areas are classified into different classes, the video is judged as a forged video. Experimental results show that our method is robust to video lossy compression for logo-removal forgery detection with the advantages of high classification accuracy and low computation cost.

Can otolith chemistry be used for identifying essential seagrass habitats for juvenile spotted seatrout, Cynoscion nebulosus, in Chesapeake Bay?

2005 ◽  
Vol 56 (5) ◽  
pp. 645 ◽  
Author(s):  
Emmanis Dorval ◽  
Cynthia M. Jones ◽  
Robyn Hannigan ◽  
Jacques van Montfrans
Keyword(s):  
Trace Elements ◽  
Chesapeake Bay ◽  
Classification Accuracy ◽  
Spatial Scales ◽  
Juvenile Fish ◽  
Spotted Seatrout ◽  
Otolith Chemistry ◽  
Seagrass Beds ◽  
Cynoscion Nebulosus ◽  
High Classification Accuracy

We investigated the variability of otolith chemistry in juvenile spotted seatrout from Chesapeake Bay seagrass habitats in 1998 and 2001, to assess whether otolith elemental and isotopic composition could be used to identify the most essential seagrass habitats for those juvenile fish. Otolith chemistry (Ca, Mn, Sr, Ba, and La; δ13C, δ18O) of juvenile fish collected in the five major seagrass habitats (Potomac, Rappahannock, York, Island, and Pocomoke Sound) showed significant variability within and between years. Although the ability of trace elements to allocate individual fish may vary between years, in combination with stable isotopes, they achieve high classification accuracy averaging 80–82% in the Pocomoke Sound and the Island, and 95–100% in the York and the Potomac habitats. The trace elements (Mn, Ba, and La) provided the best discrimination in 2001, a year of lower freshwater discharge than 1998. This is the first application of a rare earth element measured in otoliths (La) to discriminate habitats, and identify seagrass habitats for juvenile spotted seatrout at spatial scales of 15 km. Such fine spatial scale discrimination of habitats has not been previously achieved in estuaries and will distinguish fish born in individual seagrass beds in the Bay.

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