Standard
Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State. / Semenov, Viktor V.; Lebedev, Ilya S.; Sukhoparov, Mikhail E.; Salakhutdinova, Kseniya I.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. ed. / Olga Galinina; Sergey Andreev; Yevgeni Koucheryavy; Sergey Balandin. Springer Nature, 2019. p. 104-112 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11660 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Semenov, VV
, Lebedev, IS, Sukhoparov, ME & Salakhutdinova, KI 2019,
Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State. in O Galinina, S Andreev, Y Koucheryavy & S Balandin (eds),
Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11660 LNCS, Springer Nature, pp. 104-112, 19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019, St. Petersburg, Russian Federation,
26/08/19.
https://doi.org/10.1007/978-3-030-30859-9_9APA
Semenov, V. V.
, Lebedev, I. S., Sukhoparov, M. E., & Salakhutdinova, K. I. (2019).
Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State. In O. Galinina, S. Andreev, Y. Koucheryavy, & S. Balandin (Eds.),
Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings (pp. 104-112). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11660 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-30859-9_9Vancouver
Semenov VV
, Lebedev IS, Sukhoparov ME, Salakhutdinova KI.
Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State. In Galinina O, Andreev S, Koucheryavy Y, Balandin S, editors, Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. Springer Nature. 2019. p. 104-112. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-30859-9_9Author
Semenov, Viktor V.
; Lebedev, Ilya S. ; Sukhoparov, Mikhail E. ; Salakhutdinova, Kseniya I. /
Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State. Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. editor / Olga Galinina ; Sergey Andreev ; Yevgeni Koucheryavy ; Sergey Balandin. Springer Nature, 2019. pp. 104-112 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{00c97733d57644008ea5bbd24740df3f,
title = "Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State",
abstract = "This paper considers the issues of ensuring the cybersecurity of autonomous objects. Prerequisites that determine the application of additional independent methods for assessing the state of autonomous objects were identified. Side channels were described, which enable the monitoring of the state of individual objects. A transition graph was proposed to show the current state of the object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The autonomous object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of autonomous object cybersecurity with probabilities that were, on average, more than 0.8.",
keywords = "Acoustic channel, Data processing, Information security, Neural networks",
author = "Semenov, {Viktor V.} and Lebedev, {Ilya S.} and Sukhoparov, {Mikhail E.} and Salakhutdinova, {Kseniya I.}",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-30859-9_9",
language = "English",
isbn = "9783030308582",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "104--112",
editor = "Olga Galinina and Sergey Andreev and Yevgeni Koucheryavy and Sergey Balandin",
booktitle = "Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings",
address = "Germany",
note = "19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019 ; Conference date: 26-08-2019 Through 28-08-2019",
}
RIS
TY - GEN
T1 - Application of an Autonomous Object Behavior Model to Classify the Cybersecurity State
AU - Semenov, Viktor V.
AU - Lebedev, Ilya S.
AU - Sukhoparov, Mikhail E.
AU - Salakhutdinova, Kseniya I.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper considers the issues of ensuring the cybersecurity of autonomous objects. Prerequisites that determine the application of additional independent methods for assessing the state of autonomous objects were identified. Side channels were described, which enable the monitoring of the state of individual objects. A transition graph was proposed to show the current state of the object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The autonomous object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of autonomous object cybersecurity with probabilities that were, on average, more than 0.8.
AB - This paper considers the issues of ensuring the cybersecurity of autonomous objects. Prerequisites that determine the application of additional independent methods for assessing the state of autonomous objects were identified. Side channels were described, which enable the monitoring of the state of individual objects. A transition graph was proposed to show the current state of the object based on data from side channels. The type of sound signals used to analyze and classify the state of information security was also shown. An experiment intended to accumulate statistical information on the various types of unmanned object maneuvers was conducted using two audio recorders. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. The autonomous object behavior model can be used as an additional element to determine the state of cybersecurity. Using a segmented model, it was possible to improve the accuracy of determining the cybersecurity state. The proposed model enabled the identification of differences in the states of autonomous object cybersecurity with probabilities that were, on average, more than 0.8.
KW - Acoustic channel
KW - Data processing
KW - Information security
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85072969849&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30859-9_9
DO - 10.1007/978-3-030-30859-9_9
M3 - Conference contribution
AN - SCOPUS:85072969849
SN - 9783030308582
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 112
BT - Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings
A2 - Galinina, Olga
A2 - Andreev, Sergey
A2 - Koucheryavy, Yevgeni
A2 - Balandin, Sergey
PB - Springer Nature
T2 - 19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019
Y2 - 26 August 2019 through 28 August 2019
ER -
