TY - GEN

T1 - Approach to side channel-based cybersecurity monitoring for autonomous unmanned objects

AU - Semenov, Viktor

AU - Sukhoparov, Mikhail

AU - Lebedev, Ilya

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In this paper, problematic issues in ensuring the cybersecurity of autonomous unmanned objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an autonomous object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of autonomous unmanned objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the autonomous object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned object and, consequently, the cybersecurity condition of the object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of autonomous objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.

AB - In this paper, problematic issues in ensuring the cybersecurity of autonomous unmanned objects were considered. Moreover, prerequisites that determine the need for external monitoring systems were identified. The type and statistical characteristics used for the analysis and classification of sound signals were also shown. The proposed approach to the analysis of the cybersecurity condition of an autonomous object is based on classification methods and allows the identification of the current status based on digitized acoustic information processing. An experiment aimed at obtaining statistical information on various types of unmanned object maneuvers with various arrangements of an audio recorder was conducted. The data obtained was processed using two-layer feed-forward neural networks with sigmoid hidden neurons. Hence, the problem of identifying the cybersecurity condition of autonomous unmanned objects on the basis of processing acoustic signal information obtained through side channels was solved. Digitized information from an acoustic sensor (microphone) located statically in the experiment area was classified more accurately than from the microphone located directly on the autonomous object. With a minimum time of statistical information accumulation using the proposed approach, it becomes possible to identify differences in maneuvers performed by the unmanned object and, consequently, the cybersecurity condition of the object with a probability close to 0.7. The proposed approach for processing signal information can be used as an additional independent element to determine the cybersecurity condition of autonomous objects of unmanned systems. This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment.

KW - Autonomous unmanned objects

KW - Cybersecurity

KW - Cybersecurity monitoring systems

KW - Data processing

KW - Neural networks

UR - http://www.scopus.com/inward/record.url?scp=85071489461&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-26118-4_27

DO - 10.1007/978-3-030-26118-4_27

M3 - Conference contribution

AN - SCOPUS:85071489461

SN - 9783030261177

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 278

EP - 286

BT - Interactive Collaborative Robotics - 4th International Conference, ICR 2019, Proceedings

A2 - Ronzhin, Andrey

A2 - Meshcheryakov, Roman

A2 - Rigoll, Gerhard

PB - Springer Nature

T2 - 4th International Conference on Interactive Collaborative Robotics, ICR 2019

Y2 - 20 August 2019 through 25 August 2019

ER -