الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
supervisory control and data acquisition (SCADA) is a tool set of software
applications for monitoring and controlling industrial processes, that is the
collecting of data in real time from remote locations to control equipment and
conditions. SCADA supports organizations with the tools which are required to
make and deploy data-driven decisions related to their industrial processes.
The basic structure of these systems consist of a “master” unit system (generally
fully redundant or “fault tolerant”) such as computers or servers that
communicates, by utilizing one or more of a multiple of possible
telecommunication modes using different communications protocols, to
multiple, remote, electronic units (called remote terminal units (RTUs)) which
are interacted with the field-based process equipment to gather information and
transfer it to the master unit to analyze these data then monitor this information
to the operator to make a right decision.
The beginning of SCADA systems were isolated networks with no remote
interference. SCADA systems network are propagated and interacted with multi
vendors to meet the requirements of industrial environments. The data transfers
using communications protocols which have a strong point and many
advantages but in other hand have vulnerabilities which attackers utilizing this
to exploit the industrial systems these attacks have a disaster effect in industrial
environments. so, this thesis introduces the vulnerabilities of the
communications protocols and how the attacker exploits this to destroy system
then introduce multiple techniques that used to detect the behavior of the attacks
comparing the normality of the industrial system especially using deep learning
neural networks using simulation of dataset and applies ensemble algorithm
with decision tree (DT) and support vector machine (SVM) multiple
classification in this dataset using Python 3.0 programming language and Google Collab editor to simulate this two classifications models in the dataset,
and compare the results between them with known characteristics such as
accuracy , Precision , recall ,and F1 score. And comprises these results with
Long-Short Term Memory (LSTM) algorithm.
This study could be applied in the industrial environment to achieve the benefit
to the industrial application for protecting them from the electronic attacks.