Detecting network attacks in information security using machine learning techniques
[ X ]
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
DDoS attacks are usually ranked higher than other networks and IDS threats (IDS). Customers
that seek services that are dependent on the servers in question will experience service delays,
resulting in monetary and reputational losses for the company. in this thesis, we applied eight
defiant machine learning approaches to distinguish between two distinct attacks. DoS AttacksHulk and DoS Attacks-SlowHTTPTest were the names of these attacks. The algorithms DT,
RF, GB, AdaBoost, NB, XGB, and Ridge are some examples of MLP is one more. The
CICIDS2017 data set was utilized as the basis for analysis. This dataset was located in the
Information Security section of Kaggle. This dataset includes millions of cases, each
representing one of 80 attributes across 15 distinct attacks. By Using Python Environment
version 3.10 we applied these methods to the dataset's DoS attacks to categorize them as DoS
or benign attacks in two distinct Experiments: A first experiment is Binary Classification
Experiment in short, we will apply and train the eight ML techniques used in this study this
Experiment between normal and malicious (Benign and DoS Attack). Second Experience is Multiclass Classification Experiments, In this Experiment two malicious attack types (DoS
Attacks-Hulk) and (DoS Attacks-Slow HTTP Test). The purpose of these Experiments is to
propose a detection model based on ML algorithms that can enhance the Accuracy reported in
previous works. in this thesis, we applied two different experiments, and we obtained terrific
results: in the first experiment, we had a success rate of over 99% and in the second we had a
100% success rate, the results of this thesis are more efficient compared to the results of previous
work used in Chapter2 of this thesis.
Açıklama
Anahtar Kelimeler
Network Attacks, Information Security, Machine Learning, CICIDS2017, DoS attacks
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Ahmed, Ali Saadoon Ahmed. (2022). Detecting network attacks in information security using machine learning techniques . (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
