Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Soft Estimates for Social Engineering Attack Propagation Probabilities Depending on Interaction Rates Among Instagram Users. / Khlobystova, Anastasiia O.; Abramov, Maxim V.; Tulupyev, Alexander L.
INTELLIGENT DISTRIBUTED COMPUTING XIII. ed. / Kotenko; C Badica; Desnitsky; D ElBaz; M Ivanovic. Springer Nature, 2020. p. 272-277 (Studies in Computational Intelligence; Vol. 868).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Soft Estimates for Social Engineering Attack Propagation Probabilities Depending on Interaction Rates Among Instagram Users
AU - Khlobystova, Anastasiia O.
AU - Abramov, Maxim V.
AU - Tulupyev, Alexander L.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The purpose of this article is to propose an approach to denoting the parameters of the model for assessing the probability of success of a multi-pass social engineering attack of an attacker on a user. These parameters characterize the evaluation of the probability of propagation of social engineering attacks from user to user in one type of interaction. These estimates are related to the intensity of user interaction, information about which is extracted from data obtained from social Media. The article proposes an approach to the conversion of information about the episodes of interaction between users in the social Media Instagram in assessing the probability of the spread of social engineering attack, based on the Khovanov method. The obtained results help produce social network analysis and serve as a basis for the subsequent analysis of possible trajectories of the spread of multi-pass social engineering attacks, allowing the simulation of social engineering attacks and automated calculation of estimates of the success of the attack on different trajectories. The novelty of the research is to the application quantification method to social links in the context of social engineering attacks.
AB - The purpose of this article is to propose an approach to denoting the parameters of the model for assessing the probability of success of a multi-pass social engineering attack of an attacker on a user. These parameters characterize the evaluation of the probability of propagation of social engineering attacks from user to user in one type of interaction. These estimates are related to the intensity of user interaction, information about which is extracted from data obtained from social Media. The article proposes an approach to the conversion of information about the episodes of interaction between users in the social Media Instagram in assessing the probability of the spread of social engineering attack, based on the Khovanov method. The obtained results help produce social network analysis and serve as a basis for the subsequent analysis of possible trajectories of the spread of multi-pass social engineering attacks, allowing the simulation of social engineering attacks and automated calculation of estimates of the success of the attack on different trajectories. The novelty of the research is to the application quantification method to social links in the context of social engineering attacks.
KW - Social engineering attacks
KW - Soft estimates
KW - Soft social computing
UR - http://www.scopus.com/inward/record.url?scp=85075555252&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/db94a887-d1c1-3150-b0ec-2171cf18ba7a/
U2 - 10.1007/978-3-030-32258-8_32
DO - 10.1007/978-3-030-32258-8_32
M3 - Conference contribution
AN - SCOPUS:85075555252
SN - 9783030322571
T3 - Studies in Computational Intelligence
SP - 272
EP - 277
BT - INTELLIGENT DISTRIBUTED COMPUTING XIII
A2 - Kotenko, null
A2 - Badica, C
A2 - Desnitsky, null
A2 - ElBaz, D
A2 - Ivanovic, M
PB - Springer Nature
T2 - 13th International Symposium on Intelligent Distributed Computing, IDC 2019
Y2 - 7 October 2019 through 9 October 2019
ER -
ID: 62789115