Obrada i analiza mrežnih tokova iz snimljenog mrežnog prometa ; Processing and analysis of network flows from recorded network traffic
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| Title: | Obrada i analiza mrežnih tokova iz snimljenog mrežnog prometa ; Processing and analysis of network flows from recorded network traffic |
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| Authors: | Kekez, Lovro |
| Contributors: | Mikuc, Miljenko |
| Publisher Information: | Sveučilište u Zagrebu. Fakultet elektrotehnike i računarstva. University of Zagreb. Faculty of Electrical Engineering and Computing. |
| Publication Year: | 2025 |
| Collection: | Croatian Digital Theses Repository (National and University Library in Zagreb) |
| Subject Terms: | Mrežni tokovi, Analiza mrežnog prometa, Sigurnost mreža, NetFlow, Python, Scapy, Network flows, Network traffic analysis, Network security, TEHNIČKE ZNANOSTI. Računarstvo, TECHNICAL SCIENCES. Computing |
| Description: | Ovaj rad istražuje metode skupljanja informacija o mrežnim tokovima iz snimljenog mrežnog prometa u svrhu stvaranja skupa podataka za učenje modela detekcija zloćud- nog prometa na temelju anomalija. Uspoređivali su se rezultati alata nProbe i Python skripta koja koristi biblioteku Scapy. Rezultati su pokazali da nProbe, alat kojemu je namjena prikupljanje mrežnih tokova, točnije, brže i efikasnije generira mrežne tokove iz snimljenog prometa. Zaključak je da je u svrhu analize mrežnog prometa generalno isplativije koristiti alat nProbe. ; This paper investigates methods for collecting network flow information from recorded network traffic in order to create a dataset for training anomaly-based malicious traffic detection models. The results created by nProbe and a Python script using the Scapy library were compared. The results showed that nProbe, a tool designed for collecting network flows, generates network flows from recorded traffic more accurately, faster, and more efficiently. The conclusion is that it is generally more worthwhile to use nProbe for network traffic analysis. |
| Document Type: | bachelor thesis |
| File Description: | application/pdf |
| Language: | Croatian |
| Relation: | https://zir.nsk.hr/islandora/object/fer:13888; https://urn.nsk.hr/urn:nbn:hr:168:984742; https://repozitorij.unizg.hr/islandora/object/fer:13888; https://repozitorij.unizg.hr/islandora/object/fer:13888/datastream/PDF |
| Availability: | https://zir.nsk.hr/islandora/object/fer:13888 https://urn.nsk.hr/urn:nbn:hr:168:984742 https://repozitorij.unizg.hr/islandora/object/fer:13888 https://repozitorij.unizg.hr/islandora/object/fer:13888/datastream/PDF |
| Rights: | http://rightsstatements.org/vocab/InC/1.0/ ; info:eu-repo/semantics/openAccess |
| Accession Number: | edsbas.22CE2C58 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Ovaj rad istražuje metode skupljanja informacija o mrežnim tokovima iz snimljenog mrežnog prometa u svrhu stvaranja skupa podataka za učenje modela detekcija zloćud- nog prometa na temelju anomalija. Uspoređivali su se rezultati alata nProbe i Python skripta koja koristi biblioteku Scapy. Rezultati su pokazali da nProbe, alat kojemu je namjena prikupljanje mrežnih tokova, točnije, brže i efikasnije generira mrežne tokove iz snimljenog prometa. Zaključak je da je u svrhu analize mrežnog prometa generalno isplativije koristiti alat nProbe. ; This paper investigates methods for collecting network flow information from recorded network traffic in order to create a dataset for training anomaly-based malicious traffic detection models. The results created by nProbe and a Python script using the Scapy library were compared. The results showed that nProbe, a tool designed for collecting network flows, generates network flows from recorded traffic more accurately, faster, and more efficiently. The conclusion is that it is generally more worthwhile to use nProbe for network traffic analysis. |
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