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Öğe Analysis of Anomaly Detection Approaches Performed through Deep Learning Methods in SCADA Systems(Institute of Electrical and Electronics Engineers Inc., 2021) Altunay, H.C.; Albayrak, Z.; Ozalp, A.N.; Cakmak, M.Supervisory control and data acquisition (SCADA) systems are used with monitoring and control purposes for the process not to fail in industrial control systems. Today, the increase in the use of standard protocols, hardware, and software in the SCADA systems that can connect to the internet and institutional networks causes these systems to become a target for more cyber-attacks. Intrusion detection systems are used to reduce or minimize cyber-attack threats. The use of deep learning-based intrusion detection systems also increases in parallel with the increase in the amount of data in the SCADA systems. The unsupervised feature learning present in the deep learning approaches enables the learning of important features within the large datasets. The features learned in an unsupervised way by using deep learning techniques are used in order to classify the data as normal or abnormal. Architectures such as convolutional neural network (CNN), Autoencoder (AE), deep belief network (DBN), and long short-term memory network (LSTM) are used to learn the features of SCADA data. These architectures use softmax function, extreme learning machine (ELM), deep belief networks, and multilayer perceptron (MLP) in the classification process. In this study, anomaly-based intrusion detection systems consisting of convolutional neural network, autoencoder, deep belief network, long short-term memory network, or various combinations of these methods on the SCADA networks in the literature were analyzed and the positive and negative aspects of these approaches were explained through their attack detection performances. © 2021 IEEE.Öğe BGP Anomali Tespitinde Hibrit Model Yaklaşimi(Institute of Electrical and Electronics Engineers Inc., 2022) Uluer, A.F.; Albayrak, Z.; Ozalp, A.N.; Cakmak, M.; Altunay, H.C.Border Gateway Protocol (BGP) is important for the quality of the connection between autonomous systems and the domains it is connected to. With attacks made at this level, any anomaly in the network will cause connection failures at the border gateways. In this study, a classification model is proposed by using machine learning and deep learning algorithms for the detection of BGP anomalies. The proposed model is developed based on decision trees and random forest and multilayer perceptron algorithms. Indirect BGP anomalies and connection failure anomalies in the model were evaluated with accuracy and F1-score. In the tests performed on the Slammer dataset, it was seen that the best result was obtained with 99,47 accuracy, and 98,85 F1-Score value in the model studied with the Hybrit Model. © 2022 IEEE.Öğe CLSTMNet: A Deep Learning Model for Intrusion Detection(IOP Publishing Ltd, 2021) Ahmed, Issa, A.S.; Albayrak, Z.Intrusion detection as well distributed denial of service (DDoS) are vital in ensuring computer network security. Some researchers claim that current approaches cannot meet the requirements of today's networks are either not workable or sustainable. In a more specific sense, these concerns are related to an increasing number of human interactions, along with reducing levels of detection ability. With our study, a novel deep learning model for intrusion detection is developed for addressing these issues. We proposed a novel deep learning classification algorithm constructed using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) named CLSTMNet. Our proposed model has been implemented and evaluated using the benchmark NSL-KDD datasets. Compared with many conventional machine learning algorithms, the satisfied outcomes have been obtained from our model. © Published under licence by IOP Publishing Ltd.Öğe DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM(Budapest Tech Polytechnical Institution, 2023) Issa, A.S.A.; Albayrak, Z.A distributed denial-of-service (DDoS) attack is one of the most pernicious threats to network security. DDoS attacks are considered one of the most common attacks among all network attacks. These attacks cause servers to fail, causing users to be inconvenienced when requesting service from those servers. Because of that, there was a need for a powerful technique to detect DDoS attacks. Deep learning and machine learning are effective methods that researchers have used to detect DDoS attacks. So, in this study, a novel deep learning classification method was proposed by mixing two common deep learning algorithms, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The NSL-KDD dataset was used to test the model. This method architecture consists of seven layers to achieve higher performance compared with traditional CNN and LSTM. The proposed model achieved the highest accuracy of 99.20% compared with previous work. © 2023, Budapest Tech Polytechnical Institution. All rights reserved.Öğe Designing a new data encryption algorithm using a genetic code method(Budapest Tech Polytechnical Institution, 2022) Zengin, M.; Albayrak, Z.Today, the widespread use of information and communication tools along with the developing technology has facilitated access to information. These developments have revealed the importance of data security. Many encryption algorithms have been developed to ensure secure data transfer. In this article, we have developed a new Genetic Encryption Algorithm (GEA) inspired by the DNA structure. The GEA is compared to a DES (Standard Encryption Algorithm), an AES (Advanced Encryption Algorithm) and a RSA encryption algorithm. A short evaluation is made, presenting the results, along with tables and graphs. © 2022, Budapest Tech Polytechnical Institution. All rights reserved.Öğe Detecting Cyber Attacks with High-Frequency Features using Machine Learning Algorithms(Budapest Tech Polytechnical Institution, 2022) Özalp, A.N.; Albayrak, Z.In computer networks, intrusion detection systems are used to detect cyberattacks and anomalies. Feature selection is important for intrusion detection systems to scan the network quickly and accurately. On the other hand, analyzes performed using data with many attributes cause significant resource and time loss. In this study, unlike the literature studies, the frequency effects of the features in the data set are analyzed in detecting cyber-attacks on computer networks. Firstly, the frequencies of the features in the NSL-KDD data set were determined. Then, the effect of high-frequency features in detecting cyber-attacks has been examined with the widely used machine learning algorithms of Random Forest, J48, Naive Bayes, and Multi-Layer Perceptron. The performance of each algorithm is evaluated by considering Precision, False Positive Rate, Accuracy, and True Positive Rate statistics. Detection performances of different types of cyberattacks in the NSL-KDD dataset were analyzed with machine learning algorithms. Precision, Receiver Operator Characteristic, F1 score, recall, and accuracy statistics were chosen as success criteria of machine learning algorithms in attack detection. The results showed that features with high frequency are effective in detecting attacks. © 2022, Budapest Tech Polytechnical Institution. All rights reserved.Öğe Layer-based examination of cyber-attacks in IoT(Institute of Electrical and Electronics Engineers Inc., 2022) Ozalp, A.N.; Albayrak, Z.; Cakmak, M.; Ozdogan, E.The Internet of Things (IoT) is a network of millions of smart devices and sensors connected to a network. These devices are used in smart cities, public transportation, smart grids and power transmission lines. Considering IoT devices as a sensor that can be connected to a computer network, it has been seen that they are under many cyber threats. In this study, the concept of security in IoT devices is expressed according to layer architectures, and security requirements in IoT devices cloud layer, application layer, network layer, data layer, and physical layer are analyzed. Possible vulnerabilities and attacks against IoT devices have been examined by layers and next, IoT attacks are classified and layer-based security requirements are explained. © 2022 IEEE.
