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Title:Uporaba strojnega učenja za zaznavo kibernetskih napadov : magistrsko delo
Authors:ID Steiner, Benjamin (Author)
ID Vrhovec, Simon (Mentor) More about this mentor... New window
Files:.pdf MAG_Steiner_Benjamin_2024.pdf (1,41 MB)
MD5: B9A33C4FE710322065683E5EEC232E05
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FVV - Faculty of Criminal Justice and Security
Abstract:Strojno učenje se vse več uporablja v povezavi z zaznavo kibernetskih napadov, saj lahko s tem načinom zaznamo tudi bolj napredne kibernetske napade kot z drugimi načini zaznave. Glavni element učenja algoritmov nadzorovanega strojnega učenja za katero koli domeno uporabe so ustrezno označeni učni podatki, prek katerih se algoritem uči in kasneje tudi deluje. Ne vemo pa, točno koliko podatkov ti algoritmi potrebujejo za učenje, da postanejo učinkoviti. V magistrskem delu je bila uporabljena podatkovna zbirka UNSW-NB15, ki vsebuje več milijonov paketkov simuliranega omrežnega prometa in devet različnih kibernetskih napadov, kjer je vsak paketek že označen kot običajen mrežni promet ali kibernetski napad. Izvedeno je bilo učenje sedmih najpogostejših algoritmov strojnega učenja pri različnih deležih uporabljenih podatkov, da se je lahko določilo, pri katerih deležih učnih podatkov se merjene metrike normalizirajo. Rezultati so pokazali, da se je v povprečju metrike izbranih algoritmov normaliziralo okoli 10 % uporabljenih učnih podatkov (8233 vnosov), kar lahko pripomore k izdelavi bolj učinkovitih algoritmov za zaznavo kibernetskih napadov.
Keywords:kibernetski napadi, strojno učenje, lokalna omrežja, Python, UNSW-NB15, magistrska dela
Place of publishing:Ljubljana
Place of performance:Ljubljana
Publisher:B. Steiner
Year of publishing:2024
Year of performance:2024
Number of pages:IX f., [69] str.
PID:20.500.12556/DKUM-89203 New window
UDC:004.056.53:004.85(043.2)
COBISS.SI-ID:200834051 New window
Publication date in DKUM:05.07.2024
Views:494
Downloads:85
Metadata:XML DC-XML DC-RDF
Categories:FVV
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:26.06.2024

Secondary language

Language:English
Title:Using machine learning to detect cyber attacks
Abstract:Machine learning is increasingly being used in conjunction with cyber attack detection, as it can detect more advanced cyber attacks than other detection methods. A key element of supervised machine learning algorithms for any application domain is appropriately labelled training data, which the algorithm learns from and subsequently operates on. However, we do not know exactly how much learning data the aforementioned algorithms need to become effective. We have used the UNSW-NB15 dataset, which contains millions of packets of simulated network traffic and nine different cyber-attacks, where each packet is already labelled as normal network traffic, or a cyber-attack. We then performed training of the seven most common machine learning algorithms on different proportions of the data used to determine at which proportions of the training data the measured metrics normalize. We found that, on average, the metrics of the selected algorithms normalized around 10% of the training data used (8233 entries), which can help to produce more effective algorithms for detecting cyber-attacks.
Keywords:cyber attack detection, local area network, machine learning, Python, UNSW-NB15


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