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Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models. / Petrenko, S.; Grigorieva, N.; Petrenko, A.; Taran, V.

International Workshop on Advanced in Information Security Management and Applications, AISMA 2024. Vol. 863 LNNS Springer Nature, 2024. p. 226-236 (Lecture Notes in Networks and Systems; Vol. 863).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Petrenko, S, Grigorieva, N, Petrenko, A & Taran, V 2024, Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models. in International Workshop on Advanced in Information Security Management and Applications, AISMA 2024. vol. 863 LNNS, Lecture Notes in Networks and Systems, vol. 863, Springer Nature, pp. 226-236. https://doi.org/10.1007/978-3-031-72171-7_23

APA

Petrenko, S., Grigorieva, N., Petrenko, A., & Taran, V. (2024). Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models. In International Workshop on Advanced in Information Security Management and Applications, AISMA 2024 (Vol. 863 LNNS, pp. 226-236). (Lecture Notes in Networks and Systems; Vol. 863). Springer Nature. https://doi.org/10.1007/978-3-031-72171-7_23

Vancouver

Petrenko S, Grigorieva N, Petrenko A, Taran V. Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models. In International Workshop on Advanced in Information Security Management and Applications, AISMA 2024. Vol. 863 LNNS. Springer Nature. 2024. p. 226-236. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-031-72171-7_23

Author

Petrenko, S. ; Grigorieva, N. ; Petrenko, A. ; Taran, V. / Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models. International Workshop on Advanced in Information Security Management and Applications, AISMA 2024. Vol. 863 LNNS Springer Nature, 2024. pp. 226-236 (Lecture Notes in Networks and Systems).

BibTeX

@inproceedings{d374886fef0340d98b0adac405cc57e3,
title = "Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models",
abstract = "The article presents the results of a study of the interpretability of artificial intelligence models, which have found wide application in solving problems of predictive detection of intrusions and anomalies during cyberattacks. Random forest models are presented on the well-known KDD99 data set, which was used to train and test the mentioned approach. The results of practical experiments are presented, during which estimates of the significance of informative features were obtained based on the well-known methods of SHAP, Boruta, Random Forest, etc. This made it possible to determine possible ways to achieve high accuracy in predicting the detection of intrusions and anomalies, as well as to develop appropriate algorithms for interpreting the identified informative features. {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
keywords = "Artificial Intelligence, Computer Security, Features of Decision Making, Intrusion and Anomaly Detection, Machine Learning, Random Forest Algorithm, Adversarial machine learning, Anomaly detection, Cyber-attacks, Decisions makings, Feature of decision making, Intelligence models, Interpretability, Intrusion-Detection, Machine-learning, Random forest algorithm, Random forest modeling, Network intrusion",
author = "S. Petrenko and N. Grigorieva and A. Petrenko and V. Taran",
note = "Код конференции: 321489 Export Date: 10 November 2024",
year = "2024",
doi = "10.1007/978-3-031-72171-7_23",
language = "Английский",
isbn = "9783031721700 (ISBN)",
volume = "863 LNNS",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "226--236",
booktitle = "International Workshop on Advanced in Information Security Management and Applications, AISMA 2024",
address = "Германия",

}

RIS

TY - GEN

T1 - Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models

AU - Petrenko, S.

AU - Grigorieva, N.

AU - Petrenko, A.

AU - Taran, V.

N1 - Код конференции: 321489 Export Date: 10 November 2024

PY - 2024

Y1 - 2024

N2 - The article presents the results of a study of the interpretability of artificial intelligence models, which have found wide application in solving problems of predictive detection of intrusions and anomalies during cyberattacks. Random forest models are presented on the well-known KDD99 data set, which was used to train and test the mentioned approach. The results of practical experiments are presented, during which estimates of the significance of informative features were obtained based on the well-known methods of SHAP, Boruta, Random Forest, etc. This made it possible to determine possible ways to achieve high accuracy in predicting the detection of intrusions and anomalies, as well as to develop appropriate algorithms for interpreting the identified informative features. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

AB - The article presents the results of a study of the interpretability of artificial intelligence models, which have found wide application in solving problems of predictive detection of intrusions and anomalies during cyberattacks. Random forest models are presented on the well-known KDD99 data set, which was used to train and test the mentioned approach. The results of practical experiments are presented, during which estimates of the significance of informative features were obtained based on the well-known methods of SHAP, Boruta, Random Forest, etc. This made it possible to determine possible ways to achieve high accuracy in predicting the detection of intrusions and anomalies, as well as to develop appropriate algorithms for interpreting the identified informative features. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

KW - Artificial Intelligence

KW - Computer Security

KW - Features of Decision Making

KW - Intrusion and Anomaly Detection

KW - Machine Learning

KW - Random Forest Algorithm

KW - Adversarial machine learning

KW - Anomaly detection

KW - Cyber-attacks

KW - Decisions makings

KW - Feature of decision making

KW - Intelligence models

KW - Interpretability

KW - Intrusion-Detection

KW - Machine-learning

KW - Random forest algorithm

KW - Random forest modeling

KW - Network intrusion

UR - https://www.mendeley.com/catalogue/c17e434c-a24b-3751-99c2-842ca13f0570/

U2 - 10.1007/978-3-031-72171-7_23

DO - 10.1007/978-3-031-72171-7_23

M3 - статья в сборнике материалов конференции

SN - 9783031721700 (ISBN)

VL - 863 LNNS

T3 - Lecture Notes in Networks and Systems

SP - 226

EP - 236

BT - International Workshop on Advanced in Information Security Management and Applications, AISMA 2024

PB - Springer Nature

ER -

ID: 127215960