Bi̇lgi̇sayar Si̇stemleri̇ne Yöneli̇k Ağ Tabanli Tehdi̇tleri̇n/saldirilarin Deneti̇mli̇ Yapay Öğrenme İle Siniflandirilmasi
Year: 2021
Type: article
Abstract: With the developing technology, number of people who use computers are increasing nowadays. This increase in computer usage causes an increase in the variety of attacks and the number of attacks against computer systems. This situation reveals the importance of the protection of data processed on the computers and the concept of information security. Thanks to the intrusion detection systems, which have an important place in the protection of computer systems, attacks against computers and computer networks can be detected before they affect systems. Considering the increasing variety of attacks, the development of machine learning-based attack detection systems has been the subject of many studies recently. Although supervised and unsupervised machine learning have separate features, they make different contributions to the areas in which they are used. Within the scope of this study, NSL KDD data set, one of the most frequently used data sets in previous studies to simulate network traffic, was applied to a number of supervised and unsupervised learning algorithms in the WEKA application. When the results are evaluated under certain criteria, it has been determined that supervised learning algorithms give more accurate results, where unsupervised learning algorithms give faster results in the detection of attacks.
Source: DergiPark (Istanbul University)
Institutions: Milli Savunma Üniversitesi, Okan University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4291461688