Yazar "Ozalp, A.N." seçeneğine göre listele

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    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.
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    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.
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    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.