<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The rapid deployment of technologies that can share data brings, in addition to the positive aspects, also technologies that an attacker can use to misuse personal data. Vulnerabilities in the network can be divided according to the type of attack into horizontal attacks from the perspective of the attacker and vertical attacks from the perspective of the victim. An important goal of organizations is to successfully defend against such an attack. The search for attacks on the network infrastructure can be ensured by implementing machine learning that can capture the current attack. This work describes methods of searching for attacks using graphical representation of data and also using machine learning of the high count type at different sizes of the investigated segments.</p>
</abstract>

The rapid deployment of technologies that can share data brings, in addition to the positive aspects, also technologies that an attacker can use to misuse personal data. Vulnerabilities in the network can be divided according to the type of attack into horizontal attacks from the perspective of the attacker and vertical attacks from the perspective of the victim. An important goal of organizations is to successfully defend against such an attack. The search for attacks on the network infrastructure can be ensured by implementing machine learning that can capture the current attack. This work describes methods of searching for attacks using graphical representation of data and also using machine learning of the high count type at different sizes of the investigated segments.

Analysis and Collection Data from IP Network

Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics

Acta Electrotechnica et Informatica

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis and Collection Data from IP Network"}

The rapid deployment of technologies that can share data brings, in addition to the positive aspects, also technologies that an attacker can use to misuse...

Analysis and Collection Data from IP Network

Online veröffentlicht: 14. Okt. 2022

Seitenbereich: 18 - 23

Eingereicht: 29. Apr. 2022

Akzeptiert: 20. Aug. 2022

DOI: https://doi.org/10.2478/aei-2022-0013

Schlüsselwörter
Cybersecurity, nfstream, database, ndpi, anomaly detection, machine learning

© 2022 Martin Hasin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis and Collection Data from IP Network

Online veröffentlicht: 14. Okt. 2022

Seitenbereich: 18 - 23

Eingereicht: 29. Apr. 2022

Akzeptiert: 20. Aug. 2022

DOI: https://doi.org/10.2478/aei-2022-0013

SchlüsselwörterCybersecurity, nfstream, database, ndpi, anomaly detection, machine learning

© 2022 Martin Hasin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Cybersecurity, nfstream, database, ndpi, anomaly detection, machine learning