Tundis, Andrea und Cauteruccio, Francesco (2025) On Machine Learning for Digital Forensics Investigation in Network Traffic. In: 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), Seiten 1027-1033. IEEE. 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), 2025-06-09 - 2025-06-11, Lucca, Italy. doi: 10.1109/DCOSS-IoT65416.2025.00155. ISBN 979-8-3315-4372-3. ISSN 2325-2944.
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Kurzfassung
Cybercrime is an ever increasing issue in the modern world. With the growing reliance of individuals, companies and countries on digital infrastructure, more people are exposed to potential attack vectors which cybercriminals can use to extort a ransom, steal data, commit fraud, or cause significant financial damage. To prevent such crimes from occurring, various security measures are being employed. One such measure is network forensics, which focuses on analyzing network traffic data to uncover evidence and information about attacks and detect intrusions. Network forensics has to deal with large, dynamic, and volatile data, which makes performing analysis a challenging task. Machine learning has been proposed to overcome some of the challenges associated with such analysis. This paper aims to give an overview of network forensics and machine learning, present some tools investigators use to perform network forensics, and introduce some results of recent research into the use of machine learning for network forensics. Finally, a brief discussion of current challenges and further research directions is provided.
elib-URL des Eintrags: | https://elib.dlr.de/214505/ | ||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||
Titel: | On Machine Learning for Digital Forensics Investigation in Network Traffic | ||||||||||||
Autoren: |
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Datum: | 2025 | ||||||||||||
Erschienen in: | 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025) | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Nein | ||||||||||||
In ISI Web of Science: | Nein | ||||||||||||
DOI: | 10.1109/DCOSS-IoT65416.2025.00155 | ||||||||||||
Seitenbereich: | Seiten 1027-1033 | ||||||||||||
Verlag: | IEEE | ||||||||||||
ISSN: | 2325-2944 | ||||||||||||
ISBN: | 979-8-3315-4372-3 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | Digital Forensics Investigation, Network Traffic Analysis, Machine Learning, Artificial Intelligence, Cybersecurity. | ||||||||||||
Veranstaltungstitel: | 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) | ||||||||||||
Veranstaltungsort: | Lucca, Italy | ||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||
Veranstaltungsbeginn: | 9 Juni 2025 | ||||||||||||
Veranstaltungsende: | 11 Juni 2025 | ||||||||||||
HGF - Forschungsbereich: | keine Zuordnung | ||||||||||||
HGF - Programm: | keine Zuordnung | ||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||
DLR - Schwerpunkt: | Digitalisierung | ||||||||||||
DLR - Forschungsgebiet: | D CPE - Cyberphysisches Engineering | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | D - urbanModel, D - Digitaler Atlas 2.0 | ||||||||||||
Standort: | Rhein-Sieg-Kreis | ||||||||||||
Institute & Einrichtungen: | Institut für den Schutz terrestrischer Infrastrukturen > Digitale Zwillinge von Infrastrukturen Institut für den Schutz terrestrischer Infrastrukturen | ||||||||||||
Hinterlegt von: | Tundis, Andrea | ||||||||||||
Hinterlegt am: | 13 Jun 2025 10:35 | ||||||||||||
Letzte Änderung: | 13 Jun 2025 10:35 |
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