MTDNNF: Building the Security Framework for Deep Neural Network by Moving Target Defense*

2020 ◽  
Author(s):  
Weiwei Wang ◽  
Xinli Xiong ◽  
Songhe Wang ◽  
Jingye Zhang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Moving Target ◽  
Security Framework ◽  
Moving Target Defense

Moving Target Detection for Single-Channel Csar Based on Deep Neural Network

2021 ◽  
Author(s):  
Xiaobo Zhang ◽  
Di Wu ◽  
Xifeng Zhang ◽  
Qinghao Yu ◽  
Daiyin Zhu
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Single Channel ◽  
Moving Target ◽  
Moving Target Detection

Video SAR Moving Target Indication Using Deep Neural Network

2020 ◽  
Vol 58 (10) ◽  
pp. 7194-7204 ◽  
Author(s):  
Jinshan Ding ◽  
Liwu Wen ◽  
Chao Zhong ◽  
Otmar Loffeld
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Moving Target

scholarly journals An Adaptive IP Hopping Approach for Moving Target Defense Using a Light-Weight CNN Detector

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xiaoyu Xu ◽  
Hao Hu ◽  
Yuling Liu ◽  
Hongqi Zhang ◽  
Dexian Chang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Input Data ◽  
Moving Target ◽  
Software Defined Network ◽  
Light Weight ◽  
Network Attacks ◽  
Moving Target Defense ◽  
System Overhead ◽  
Data Preprocess

Scanning attack is normally the first step of many other network attacks such as DDoS and propagation worm. Because of easy implementation and high returns, scanning attack especially cooperative scanning attack is widely used by hackers, which has become a serious threat to network security. In order to defend against scanning attack, this paper proposes an adaptive IP hopping in software defined network for moving target defense (MTD). In order to accurately respond to attacker’s behavior in real time, a light-weight convolutional neural network (CNN) detector composed of three convolutional modules and a judgment module is proposed to sense scanning attack. Input data of the detector is generated via designed packets sampling and data preprocess. The detection result of the detector is used to trigger IP hopping. In order to provide some fault tolerance for the CNN detector, IP hopping can also be triggered by a preset timer. The CNN driving adaptability is applied to a three-level hopping strategy to make the MTD system optimize its behavior according to real time attack. Experiments show that compared with existing technologies, our proposed method can significantly improve the defense effect to mitigate scanning attack and its subsequent attacks which are based on hit list. Hopping frequency of the proposed method is also lower than that of other methods, so the proposed method shows lower system overhead.

Study on Human Detection System Using Deep Neural Network and Alternative Learning for Autonomous Flying Drones

2019 ◽  
Vol 139 (2) ◽  
pp. 149-157
Author(s):  
Itaru Nagayama ◽  
Wakaki Uehara ◽  
Takaya Miyazato
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Detection System ◽  
Human Detection ◽  
Alternative Learning

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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