Modulation Classification of Active Attack Signals for Internet of Things Using GP-CNN Network

In recent decades, the communication of electronic equipment and physical resources is combined. Internet of Things (IoT) distributes things widely in the network. The IoT is Internet-based pervasive computing, which created significant development in the recent disposition of IoT infrastructures. The IoT infrastructures lead, manage and generate large amounts of data across various applications including environmental, transportation and healthcare monitoring. In this regard, there are salient uncertainties about the use of security concepts that are frequently measured as a major concern of IoT distributed architecture design. This paper mainly focuses on the classification of IoT, novel architecture considering the sensitivity of data, IoT security layers, review of security issues and acclaimed countermeasures.

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Modulation Classification of Analog and Digital Signals Using Neural Network and Support Vector Machine

Advances in Neural Networks – ISNN 2007 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72395-0_47 ◽

2007 ◽

pp. 368-373 ◽

Cited By ~ 3

Author(s):

Cheol-Sun Park ◽

Dae Young Kim

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Support Vector ◽

Modulation Classification ◽

Digital Signals

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Time-Series Data Analysis for Classification of Noisy and Incomplete Internet-of-Things Datasets

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) ◽

10.1109/icmla.2019.00255 ◽

2019 ◽

Author(s):

Michael Postol ◽

Candace Diaz ◽

Robert Simon ◽

Drew Wicke

Keyword(s):

Time Series ◽

Data Analysis ◽

Internet Of Things ◽

Time Series Data ◽

Series Data ◽

Time Series Data Analysis

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Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar

Mathematical Problems in Engineering ◽

10.1155/2020/9148096 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Feng Wang ◽

Shanshan Huang ◽

Chao Liang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Pulse Compression ◽

Modulation Classification ◽

Linear Kernel ◽

Cognitive Radar ◽

Feature Map ◽

Time Frequency ◽

Dimensional Plane

Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed. In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system. The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal. Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture. A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification. The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation. A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.

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