scholarly journals False Sequential Command Attack of Large-Scale Cyber-Physical Systems

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 176 ◽  
Author(s):  
Yinqiao Xiong ◽  
Ziyu Yang ◽  
Baoyao Wang ◽  
Peng Xun ◽  
Tiantian Deng
Keyword(s):  
Physical Process ◽  
Large Scale ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Attack Model ◽  
Command Sequence ◽  
Proposed Model ◽  
Feedback Data ◽  
Bad Data

Previous studies have demonstrated that false commands can cause severe damage to large-scale cyber-physical systems (CPSs). We focus on a kind of threat called false sequential command attack, with which attackers can generate false sequential commands, resulting in the illegal control of the physical process. We present a feasible attack model. Attackers delay the disaggregation of former commands by manipulating maliciously sub-controllers. Simultaneously, bad feedback data is injected to defeat the controller to issue latter commands. Thus, false command sequence is executed and the disruption of physical process can be obtained. It is also difficult for the detector to identify such attacks as injecting bad data. We also discuss other possible attack paths and analyze the corresponding disadvantages. Compared with other paths, the proposed model is more feasible and has more difficulties to be detected. A case study is given to validate the feasibility and effectiveness of proposed false sequential command attack model. Finally, we discuss the possible countermeasure.

scholarly journals Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 941 ◽  
Author(s):  
Wenping Deng ◽  
Ziyu Yang ◽  
Peng Xun ◽  
Peidong Zhu ◽  
Baosheng Wang
Keyword(s):  
Machine Learning ◽  
Pattern Mining ◽  
Heterogeneous Data ◽  
Cyber Physical Systems ◽  
Normal System ◽  
Physical Systems ◽  
Sensory Data ◽  
Attack Model ◽  
False Data Injection ◽  
Bad Data

False data injection (FDI) attack is a hot topic in cyber-physical systems (CPSs). Attackers inject bad data into sensors or return false data to the controller to cause the inaccurate state estimation. Although there exists many detection approaches, such as bad data detector (BDD), sequence pattern mining, and machine learning methods, a smart attacker still can inject perfectly false data to go undetected. In this paper, we focus on the advanced false data injection (AFDI) attack and its detection method. An AFDI attack refers to the attack where a malicious entity accurately and successively changes sensory data, making the normal system state continuously evaluated as other legal system states, causing wrong outflow of controllers. The attack can lead to an automatic and long-term system failure/performance degradation. We first depict the AFDI attack model and analyze limitations of existing detectors for detecting AFDI. Second, we develop an approach based on machine learning, which utilizes the k-Nearest Neighbor (KNN) technique and heterogeneous data including sensory data and system commands to implement a classifier for detecting AFDI attacks. Finally, simulation experiments are given to demonstrate AFDI attack impact and the effectiveness of the proposed method for detecting AFDI attacks.

Data quality challenges in large-scale cyber-physical systems: A systematic review

2021 ◽  
pp. 101951
Author(s):  
Ahmed Abdulhasan Alwan ◽  
Mihaela Anca Ciupala ◽  
Allan J. Brimicombe ◽  
Seyed Ali Ghorashi ◽  
Andres Baravalle ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Quality ◽  
Large Scale ◽  
Cyber Physical Systems ◽  
Physical Systems

Generating metamorphic relations for cyber-physical systems with genetic programming: an industrial case study

2021 ◽  
Author(s):  
Jon Ayerdi ◽  
Valerio Terragni ◽  
Aitor Arrieta ◽  
Paolo Tonella ◽  
Goiuria Sagardui ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Cyber Physical Systems ◽  
Industrial Case Study ◽  
Physical Systems

scholarly journals Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry

2020 ◽  
Vol 12 (21) ◽  
pp. 8905
Author(s):  
Wen Yi ◽  
Robyn Phipps ◽  
Hans Wang
Keyword(s):  
Construction Industry ◽  
Large Scale ◽  
Transportation Cost ◽  
Sustainable Transportation ◽  
Construction Site ◽  
Integer Optimization ◽  
Construction Sites ◽  
Capacity Limits ◽  
Proposed Model

This paper focuses on sustainable transportation of prefab products from factories to construction sites by ship. Since the transportation cost for all the prefab products of a construction site is mainly dependent on the number of cargo holds used on ships, a loading plan for prefab products that minimizes the number of holds required is highly desirable. This paper is therefore devoted to the development of an optimal loading plan that decides which prefab products are loaded into each cargo hold and how to pack these prefab products into the holds so that as few holds as possible are used. We formulate the problem as a large-scale integer optimization model whose objective function is to minimize the total number of cargo holds used and whose constraints represent the cargo hold capacity limits. We develop a heuristic to solve the problem and obtain a high-quality solution. We have tested the model and algorithm on a case study that includes 20 prefab products. We find that different cargo holds carry prefab products that have quite different densities. Moreover, the orientations of many prefab products are different from their default orientations. The results demonstrate the applicability of the proposed model and algorithm.

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