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http://elartu.tntu.edu.ua/handle/lib/47592| Tytuł: | Comparison of feature extraction tools for network traffic data |
| Authors: | Lypa, Borys Horyn, Ivan Zagorodna, Natalia Tymoshchuk, Dmytro Lechachenko, Taras |
| Affiliation: | Ternopil Ivan Puluj National Technical University, Ruska str. 56, Ternopil, 46001, Ukraine |
| Bibliographic description (Ukraine): | Lypa, B., Horyn, I., Zagorodna, N., Tymoshchuk, D., Lechachenko T., (2024). Comparison of feature extraction tools for network traffic data. CEUR Workshop Proceedings, 3896, pp. 1-11. |
| Bibliographic citation (APA): | Lypa, B., Horyn, I., Zagorodna, N., Tymoshchuk, D., Lechachenko T. (2024). Comparison of feature extraction tools for network traffic data. CEUR Workshop Proceedings, 3896, 1-11. |
| Journal/kolekcja: | CEUR Workshop Proceedings |
| Tom: | 3896 |
| Data wydania: | 23-paź-2024 |
| Date of entry: | 19-sty-2025 |
| Wydawca: | CEUR-WS |
| Place edycja: | Ternopil, Ukraine, Opole, Poland, October 23-25, 2024. |
| Słowa kluczowe: | cybersecurity big data intrusion detection system network traffic feature extraction artificial intelligence |
| Zakres stron: | 1-11 |
| Główna strona: | 1 |
| Strona końcowa: | 11 |
| Abstract: | The comparison analysis of the most popular tools to extract features from network traffic is conducted in this paper. Feature extraction plays a crucial role in Intrusion Detection Systems (IDS) because it helps to transform huge raw network data into meaningful and manageable features for analysis and detection of malicious activities. The good choice of feature extraction tool is an essential step in construction of Artificial Intelligence-based Intrusion Detection Systems (AI-IDS), which can help to enhance the efficiency, accuracy, and scalability of such systems. |
| URI: | http://elartu.tntu.edu.ua/handle/lib/47592 |
| ISBN: | 1613-0073 |
| Właściciel praw autorskich: | © Borys Lypa, Ivan Horyn, Natalia Zagorodna, Dmytro Tymoshchuk, Taras Lechachenko |
| Wykaz piśmiennictwa: | [1] Petliak, N., Klots, Y., Titova, V., Cheshun, V., Boyarchuk, A. Signature-based Approach to Detecting Malicious Outgoing Traffic. 4th International Workshop on Intelligent Information Technologies and Systems of Information Security, IntellTSIS 2023. CEUR Workshop Proceedings, 2023, 3373, pp. 486–506 [2] Ioulianou, Philokypros, Vasilakis, Vasileios orcid.org/0000-0003-4902-8226, Moscholios, Ioannis et al. (1 more author) (Accepted: 2018) A Signature-based Intrusion Detection System for the Internet of Things. In: Information and Communication Technology Form, 11-13 Jul 2018. (In Press) [3] Y. Klots, N. Petliak and V. Titova, "Evaluation of the efficiency of the system for detecting malicious outgoing traffic in public networks," 2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT), Athens, Greece, 2023, pp. 1-5, doi: 10.1109/DESSERT61349.2023.10416502. [4] Hashemi, M. J., Cusack, G., & Keller, E. (2019, December). Towards evaluation of nidss in adversarial setting. In Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (pp. 14- 21). [5] ZAGORODNA, N., STADNYK, M., LYPA, B., GAVRYLOV, M., & KOZAK, R. (2022). Network Attack Detection Using Machine Learning Methods. Challenges to national defence in contemporary geopolitical situation, 2022(1), 55-61. [6] Sarhan, M., Layeghy, S., & Portmann, M. (2022). Evaluating standard feature sets towards increased generalisability and explainability of ML-based network intrusion detection. Big Data Research, 30, 100359. [7] Nimbalkar, P., & Kshirsagar, D. (2021). Feature selection for intrusion detection system in Internet-of-Things (IoT). ICT Express, 7(2), 177-181. [8] Data never sleeps URL: https://www.domo.com/solution/data-never-sleeps-6 [9] Andreas, B., Dilruksha, J., & McCandless, E. (2020). Flow-based and packet-based intrusion detection using BLSTM. SMU Data Science Review, 3(3), 8. [10] CICFlowMeter (2017). Canadian institute for cybersecurity (cic). [11] Habibi Lashkari, A., Draper Gil, G., Mamun, M. S. I., and Ghorbani, A. A. (2017). Characterization of tor traffic using time based features. In In Proceedings of the 3rd International Conference on Information Systems Security and Privacy (ICISSP), pages 253–262. [12] Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1, 108-116. [13] CICFlowMeter GitHub. URL: https://github.com/ahlashkari/CICFlowMeter [14] Python CICFlowMeter. URL: https://github.com/hieulw/cicflowmeter [15] Wireshark. URL: https://www.wireshark.org/ [16] Argus. URL: https://openargus.org/ [17] Rodríguez, M., Alesanco, Á., Mehavilla, L., & García, J. (2022). Evaluation of machine learning techniques for traffic flow-based intrusion detection. Sensors, 22(23), 9326. [18] Sarhan, M., Layeghy, S., & Portmann, M. (2022). Evaluating standard feature sets towards increased generalisability and explainability of ML-based network intrusion detection. Big Data Research, 30, 100359. [19] Engelen, G.; Rimmer, V.; Joosen, W. Troubleshooting an intrusion detection dataset: The CICIDS2017 case study. In Proceedings of the 2021 IEEE Symposium on Security and Privacy Workshops, SPW, San Francisco, CA, USA, 27–27 May 2021; pp. 7–12. [20] Rosay, A.; Cheval, E.; Carlier, F.; Leroux, P. Network intrusion detection: A comprehensive analysis of CIC-IDS2017. In Proceedings of the 8th International Conference on Information Systems Security and Privacy (ICISSP 2022), Online, 9–11 February 2022; pp. 25–36. |
| Typ zawartości: | Article |
| Występuje w kolekcjach: | Наукові публікації працівників кафедри кібербезпеки |
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|---|---|---|---|---|
| ITTAP_2024_3896_paper1_tntu.pdf | 270,67 kB | Adobe PDF | Przeglądanie/Otwarcie |
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