Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their...

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Published inSensors (Basel, Switzerland) Vol. 22; no. 4; p. 1335
Main Authors Kotenko, Igor, Izrailov, Konstantin, Buinevich, Mikhail
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 10.02.2022
MDPI
Subjects
Algorithms
analytic model
Automation
cyber security
Cyberterrorism
Data security
Information systems
Internet of Things
Investment analysis
IoT systems
Machine learning
Performance evaluation
Review
Security management
Software
static analysis
survey model
Surveys
Taxonomy
New York
cyber security
IoT systems
static analysis
survey model
formalization
analytic model
machine learning
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Abstract Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.
AbstractList Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.
Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts' work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of "Form and Content" data transformations.Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts' work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of "Form and Content" data transformations.
Audience Academic
Author Izrailov, Konstantin
Kotenko, Igor
Buinevich, Mikhail
AuthorAffiliation 2 Department of Secure Communication Systems, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint-Petersburg, Russia; konstantin.izrailov@mail.ru
3 Department of Applied Mathematics and Information Technologies, Saint-Petersburg University of State Fire Service of EMERCOM of Russia, 196105 Saint-Petersburg, Russia; bmv1958@yandex.ru
1 Computer Security Problems Laboratory, St. Petersburg Federal Research Center of the Russian Academy of Sciences, 199178 Saint-Petersburg, Russia
AuthorAffiliation_xml – name: 1 Computer Security Problems Laboratory, St. Petersburg Federal Research Center of the Russian Academy of Sciences, 199178 Saint-Petersburg, Russia
– name: 2 Department of Secure Communication Systems, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint-Petersburg, Russia; konstantin.izrailov@mail.ru
– name: 3 Department of Applied Mathematics and Information Technologies, Saint-Petersburg University of State Fire Service of EMERCOM of Russia, 196105 Saint-Petersburg, Russia; bmv1958@yandex.ru
Author_xml – sequence: 1
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  orcidid: 0000-0001-6859-7120
  surname: Kotenko
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  orcidid: 0000-0001-8146-0022
  surname: Buinevich
  fullname: Buinevich, Mikhail
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35214237$$D View this record in MEDLINE/PubMed
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survey model
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machine learning
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Snippet Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly...
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SubjectTerms Algorithms
analytic model
Automation
cyber security
Cyberterrorism
Data security
Information systems
Internet of Things
Investment analysis
IoT systems
Machine learning
Performance evaluation
Review
Security management
Software
static analysis
survey model
Surveys
Taxonomy
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Title Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches
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https://www.proquest.com/docview/2633857337
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https://doaj.org/article/502ae1d5ff79489ca33afa525461dea5
Volume 22
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