Yapay Sinir Ağı Kullanılarak Anomali Tabanlı Saldırı Tespit Modeli Uygulaması

Karaman, Mehmet; Turan, Meti̇n; Aydın, Muhammed Ali

doi:10.31590/ejosat.778789

Cited by 4 publications

(3 citation statements)

References 18 publications

(17 reference statements)

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“…The results showed an achievement rate of 98.32% on the CIC IDS2018 dataset and a rate of 98.8% on the CIC IDS2018+Synthetic dataset. In another study by Karaman et al [5], where they implemented the Artificial Neural Networks approach on the CIC IDS2018 dataset. The study yielded impressive results, with threat detection accuracy reaching 99.11%, botnet attack detection accuracy at 93.23%, DDoS attack detection accuracy at 99.31%, DoS attack detection accuracy at 92.26%, and BruteForce attack detection accuracy at 99.26%.…”

Section: Related Workmentioning

confidence: 99%

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

FİRAT,

BAKAL,

AKBAŞ

2023

Journal of Polytechnic

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With the development and expansion of computer networks day by day and the diversity of software developed, the damage that possible attacks can cause is increasing beyond the predictions. Intrusion Detection Systems (STS/IDS) are one of the effective defense tools against these potential attacks that are constantly increasing and diversifying. The ultimate goal is to train these systems with various artificial intelligence methods, to detect subsequent attacks in real time and to take the necessary precautions. In this study, classical feature selection methods and Frequent Item Set Mining were used in feature selection in a hybrid model, and it was aimed to classify network traffic data for normal and attack by using many machine learning methods, including Logistic Regression, with the final features obtained. The method uses a data set originally containing 85 features to make a decision while making this classification. These attributes are extracted using CICFlowMeter from a PCAP file where network traffic is recorded. The results show that the proposed method in the study classifies more than 225000 records in the data set with a success rate of 97.68%.

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Section: Related Workmentioning

confidence: 99%

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

FİRAT,

BAKAL,

AKBAŞ

2023

Journal of Polytechnic

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“…In another implementation performed by using artificial neural networks, an anomaly-based intrusion detection system was designed on the CSE-CIC-IDS2018 dataset. The detection accuracy of the recommended system was revealed at the end of the study according to the types of attacks within the dataset (Karaman, Turan, & Aydın, 2020). In the hybrid model developed by Sun et al by using convolution neural network and long short-term memory (LSTM), category weight optimization method was used.…”

Section: Related Workmentioning

confidence: 99%

Network Intrusion Detection Approach Based on Convolutional Neural Network

Altunay¹,

Albayrak²

2021

European Journal of Science and Technology

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The probability of encountering cyber-attacks increases with the proliferation of internet usage and the increase in the number of network devices. Intrusion detection systems are used in order to prevent the damages caused by cyber-attacks. In this study, an intrusion detection implementation based on feature selection was performed by using a convolutional neural network in order to prevent cyber-attacks. CSE-CIC-IDS2018 dataset was used during the training and testing stages. Attributes of the dataset were trained on the preprocessing layer, classification layer, and two-layer convolutional neural network. The implementation performance was assessed through accuracy, precision, and recall metrics. A retraining stage was performed in order to resolve the over-learning problem of the network. Intrusion detection was performed through synthetic data generation within the dataset. SMOTE (Synthetic Minority Over Sampling Technique) was used for synthetic data generation. In the study, Brute Force, SQL Injection, Botnet, and DoS attacks were selected as the types of threat. Attack detection accuracy of the intrusion detection system was found 98.32% and the detection accuracy obtained after retraining was found 98.8%. Following the training performed with synthetic data added into the dataset, the neural network carried out a binary classification of the data. The performance rate of detection and classification of the data as a threat was determined as 98.7% for Brute Force, 98.5% for DoS, 98.9% for Botnet, and 99.1% for SQL Injection.

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“…Çalışmalarında DDOS, Botnet, DOS, BruteForce türündeki saldırıları ele alınmış olup inceledikleri veri setinde DDOS ve BruteFroce saldırıların diğer iki türe göre daha yüksek oranda geldiği tespit edilmiştir. Kurdukları sistemin başarısının ise %99, 26 gibi bir yüksek tahmin oranına sahip olduğu görülmüştür [26].…”

Section: Türki̇ye' De Si̇ber Suçlar Ve çöZüm Yöntemleri̇ üZeri̇ne Li̇teratür Taramasiunclassified

Cyber Attacks and Detection Method in Turkey: A Literature Review

Hatipoğlu

Tunacan

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Siber suç, bir bilgisayar ve internet ağı içeren herhangi bir suçu ifade eden bilgisayar suçu olarak da bilinir. Bireyler, şirketler veya hükümetler hakkındaki bilgilere yapılan bir saldırıdır. Birey siber suçun ana hedefi olduğunda bilgisayar, siber suçta bir araç olarak düşünülebilir. Ayrıca siber suç, internet erişimiyle işlenen geleneksel suçları da içerir. Örneğin, telefonla pazarlama İnternet dolandırıcılığı, kimlik hırsızlığı ve kredi kartı hesap hırsızlığı. Basit bir ifadeyle siber suç, internet erişimi olan bilgisayar veya diğer cihazlar kullanılarak gerçekleştirilen her türlü şiddet eylemi olarak tanımlanabilir. Bu eylem başkalarına zararlı etkiler verebilir.Bu çalışmanın amacı, Türkiye' de siber suç konusunda durum tespiti ve siber saldırı türlerine karşılık üretilen çözüm yöntemlerini araştırmaktır. Ancak siber zorbalık ve siber suçlar ile ilgili kanun, yasa vs. konularında yapılmış olan çalışmalar ile 2016 öncesi yapılmış olan literatür çalışmaları kapsam dışında bırakılmıştır. Bu çalışmada metodoloji olarak; literatür taraması yöntemi seçilmiştir. Siber saldırı tiplerine baktığımızda en çok incelenen ve çalışmalara konu olan saldırı tiplerinin DoS ve DDoS saldırılar olduğu ve tespit yöntemlerinde ise Random Forest karar ağacı yönteminin kullanıldığı gözlemlenmektedir

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Yapay Sinir Ağı Kullanılarak Anomali Tabanlı Saldırı Tespit Modeli Uygulaması

Cited by 4 publications

References 18 publications

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

Hibrit Sık Kullanılan Öğe Kümeleme ile Makine Öğrenmesi Tabanlı Ağ Sızma Tespiti

Network Intrusion Detection Approach Based on Convolutional Neural Network

Cyber Attacks and Detection Method in Turkey: A Literature Review

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