Comparison of an Adaptive-Immunized and an Adversarial Deep Learning Control Laws to Increase Resiliency in Distributed Cyber-Physical Systems

2022 ◽  
Author(s):  
Diana F. ◽  
Hever Moncayo ◽  
Christoph Aoun ◽  
Tatiana Gutierrez
Keyword(s):  
Deep Learning ◽  
Learning Control ◽  
Cyber Physical Systems ◽  
Control Laws ◽  
Physical Systems

scholarly journals Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Quentin Cabanes ◽  
Benaoumeur Senouci ◽  
Amar Ramdane-Cherif
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computation Time ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Research Technology ◽  
Standard Design ◽  
Hardware Architectures ◽  
Voxel Grid

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

scholarly journals The Industry 4.0 technological and information processes cyber-modelling

2021 ◽  
Vol 2094 (4) ◽  
pp. 042062
Author(s):  
A V Gurjanov ◽  
D A Zakoldaev ◽  
I O Zharinov ◽  
O O Zharinov
Keyword(s):  
Production Systems ◽  
Cyber Physical Systems ◽  
Time Interval ◽  
Control Laws ◽  
Physical Systems ◽  
Production Processes ◽  
Information Models ◽  
Number Of Cycles ◽  
Cyber Physical Production Systems ◽  
Parameter Values

Abstract Cyber-modelling is the information models simulation process describing in a mathematical and formal logic languages (phenomenon models) how cyber-physical systems interaction mechanisms are united with different control laws and parameter values. The equation complexity represented in different levels of cyber-physical production systems hierarchy and non-equations of algebra, logic, end-subtraction, vector and matrices form in a discreet and uninterrupted times are defined with an aggregated number in the industrial automatics element control loop. The cyber-modelling is done for statistic and dynamic processes and equipment states being monitored in a virtual environment fixating actual in a time interval technological data. The cyber-modelling is done with integrated calculation equipment systems with parallel physical production processes of item manufacturing. The model time faster than physical processes let prognosticate the corrections modifying control signals and phase variables of cyber-physical systems united in an assembly conveyor. The cyber-modelling advantage is an expanded number of cycles to optimize the technological processes, which are calculated with integrated calculation systems using consecutive approximation method. They describe the cyber-modelling technology and propose the information models based on phenomenon cyber-physical production processes descriptions with general control theory terms, calculations and connection for hierarchy controlling structures.

A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems

2021 ◽  
pp. 137-159
Author(s):  
Srikanth Yadav M. ◽  
Kalpana R.
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Critical Infrastructure ◽  
Cyber Physical Systems ◽  
Industrial Applications ◽  
Network Intrusion Detection ◽  
Primary Concern ◽  
Physical Systems ◽  
Detection Systems ◽  
Network Intrusion

In the present computing world, network intrusion detection systems are playing a vital part in detecting malicious activities, and enormous attention has been given to deep learning from several years. During the past few years, cyber-physical systems (CPSs) have become ubiquitous in modern critical infrastructure and industrial applications. Safety is therefore a primary concern. Because of the success of deep learning (DL) in several domains, DL-based CPS security applications have been developed in the last few years. However, despite the wide range of efforts to use DL to ensure safety for CPSs. The major challenges in front of the research community are developing an efficient and reliable ID that is capable of handling a large amount of data, in analyzing the changing behavioral patterns of attacks in real-time. The work presented in this manuscript reviews the various deep learning generative methodologies and their performance in detecting anomalies in CPSs. The metrics accuracy, precision, recall, and F1-score are used to measure the performance.

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