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Research on effective finding of network anomalies in Intrusion Detection Systems using ML/DL methods

Dmitrijs Bulaks

ABSTRACT

In this paper, the process of testing datasets and ML/DL methods using the Weka platform in order to obtain results of their effectiveness and how high the accuracy of detecting network anomalies they provide is described. During the experiment, many ML/DL algorithms and the most common datasets (KDD99, NSL-KDD, UNSW-NB15) were analyzed, as a result of which it was revealed that the best result of detection accuracy and time required on the experiment was produced by the NSL-KDD dataset.
Author: Dmitrijs Bulaks
Degree: Bachelor
Year: 2024
Work Language: English
Supervisor: Mg. sc. ing., Jeļena Baranova
Faculty: Engineering Faculty
Study programme: Computer Engineering and Electronics

KEYWORDS

MACHINE AND DEEP LEARNING, INTRUSION DETECTION SYSTEM, NETWORK ANOMALIES, ML/DL METHODS EFFECTIVENESS