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Öğe AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks(Springer, 2022) Cakmak, Muhammet; Albayrak, ZaferWith the dramatic increase in the number of users and the widespread use of smartphones, most of the internet content today is provided by cellular connections. The purpose of many active queue management algorithms developed for the cellular Long-Term Evolution network is to prevent forced packet drops in the Evolved Node B (eNodeB) Radio Link Control buffer and to improve delay and end-to-end throughput values. Although the algorithms developed in the literature improve some of the end-to-end throughput, delay, and packet data fraction values during bottleneck and congestion, they cannot balance these values. The proposed virtual queue management algorithm recalculates the average queue value and the packet dropping probability according to different traffic loads to solve the queue delay and queue overflow problem providing a balance between throughput, delay, and packet data fraction. Simulation results illustrate that the proposed algorithm reduces the delay of the packets and increases fairness among users compared to the Drop-tail, Random Early Drop, Controlled Delay, Proportional Integral Controller Enhanced, and Packet Limited First In First Out Queue algorithms.Öğe Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering(Mdpi, 2022) Alarbi, Abdalraouf; Albayrak, ZaferMachine learning classification algorithms vary drastically in their approaches, and researchers have always been trying to reduce the common boundaries of nonlinear classification, overlapping, or noise. This study summarizes the steps of hybridizing a new algorithm named Core Classify Algorithm (CCA) derived from K-nearest neighbor (KNN) and an unsupervised learning partitioning algorithm (K-means), aiming to avoid the unrepresentative Cores of the clusters while finding the similarities. This hybridization step is meant to harvest the benefits of combining two algorithms by changing results through iteration to obtain the most optimal results and classifying the data according to the labels with two or more clusters with higher accuracy and better computational efficiency. Our new approach was tested on a total of five datasets from two different domains: one phishing URL, three healthcare, and one synthetic dataset. Our results demonstrate that the accuracy of the CCA model in non-linear experiments representing datasets two to five was lower than that of dataset one which represented a linear classification and achieved an accuracy of 100%, equal in rank with Random Forest, Support Vector Machine, and Decision Trees. Moreover, our results also demonstrate that hybridization can be used to exploit flaws in specific algorithms to further improve their performance.Öğe DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM(Budapest Tech, 2023) Issa, Ahmet Sardar Ahmed; Albayrak, ZaferA 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.Öğe Designing a New Data Encryption Algorithm Using a Genetic Code Method(Budapest Tech, 2022) Zengin, Mustafa; Albayrak, ZaferToday, 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.Öğe Detecting Cyber Attacks with High-Frequency Features using Machine Learning Algorithms(Budapest Tech, 2022) Ozalp, Ahmet Nusret; Albayrak, ZaferIn computer networks, intrusion detection systems are used to detect cyber-attacks 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.Öğe Kablosuz vücut alan ağları için enerji verimli mac protokolleri (kvaa)(2017) Musa, Hatem; Albayrak, ZaferKablosuz Vücut Ağları (KVAA), sürekli izleme ve bakıma ihtiyaç duyan ve özellikle yaşlı insanların sağlık yönünden takipleri için insan vücuduna yerleştirilerek bu hastaların takiplerinin ve sağlık hizmetlerinin güvenli bir şekilde yapılabilmesi amacıyla sıklıkla kullanılmaktadır. Bu küçük sensörlerin enerji ihtiyaçları piller tarafından sağlanır. Ancak bu piller sensörlerdeki protokoller tarafından verimli bir şekilde kullanılmalıdır. Bu çalışmada, enerji verimliliğini artırmaya ve kayıplarını azaltmaya çalışan ve KVAA'larda kullanılan enerji verimli MAC protokollerinden olan SensorMAC, Timeout-MAC, WiseMAC, DSMAC ve Zebra-MAC protokollerinden bahsedilecektir.Öğe Lte ağlarda remote-host ile pg-w arasındaki kuyruk yönetim algoritmalarının performans analizi(2020) Çakmak, Muhammet; Albayrak, ZaferMobil iletişiminin hızlı gelişmesiyle internet içeriğinin çoğu günümüzde hücresel ağlar ile sağlanmaktadır. Hücresel ağlarda yaşanan tıkanıklık sırasında kullanılan algoritmalar paket gecikmesi, kuyruk taşması ve darboğaz problemlerini çözmeye çalışmaktadırlar. LTE ağlarında remote-host ile PG-W düğümü arasındaki veri transferi yüksek hız gerektirmekte bu da hücresel ağın çalışma hızını doğrudan etkilemektedir. Doğru bir kuyruk yönetim algoritmasının seçilmesi LTE hücresel ağı için kritik bir önem kazanmaktadır. Bu çalışmada LTE ağlarında remote-host ile PG-W arasında çalışan aktif kuyruk yönetim algoritmaları olan RED, CoDel, Pie ve pFIFO’nun performanslarının, uçtan uca ortalama verim, gecikme ve paket düşürme oranları üzerindeki etkisi karşılaştırmalı olarak incelenmiştir ve sonuçları değerlendirilmiştir.Öğe Performance Comparison of Queue Management Algorithms in LTE Networks using NS-3 Simulator(Univ Osijek, Tech Fac, 2021) Cakmak, Muhammet; Albayrak, Zafer; Torun, CumhurOne of the most important issues accepted by researchers in LTE cellular systems is to develop Queue Management Algorithms for RLC (Radio Link Control). The performance of queue-management algorithms depends on parameters such as latency, packet dropping, and bandwidth usage. Simulation software is used to evaluate the queue-management algorithms developed and to test their performance. In the literature, active queue management algorithms have been compared with wired and wireless networks. In contrast to prior works, in this study, we have analyzed active queue management algorithms using the LTE model in the NS-3 network simulator. When the data and the results obtained from the simulations have been evaluated, it is concluded that the RED algorithm using probabilistic methods and the threshold value is more successful than the other algorithms in LTE networks.Öğe Performance evaluation of wbans mac protocols in different dbm and omnet++(2021) Albayrak, Zafer; Musa, Hatem; Çakmak, MuhammetIn present days, wireless sensor networks (WSN) have involved considerable attention of both academy and industry because of thevaried range of contexts in which they could be used. The has wireless body area network (WBAN) become the most important standardfor WSN, and several software and hardware platforms are built on it. The implementation and performance analysis of this standardis essential to understand the important limits of it. The simulation is one of the greatest valuable tools for protocol evaluation andprototyping design. Furthermore, network simulators play an important part to test new algorithms and other protocols built on thisspecification. In this paper, the performance of the WBAN MAC standard protocols has been tested. The performance of the protocolsregarding power consumption, delay and packets congestion are compared using OMNet++ simulator.Öğe Performance of Ad-Hoc Networks Using Smart Technology Under DDoS Attacks(Springer International Publishing Ag, 2022) Said, Aden Ali; Cakmak, Muhammet; Albayrak, ZaferThe networks used in many areas such as location-based services, robotics, smart building assessment, smart water management, smart mobile learning, medical image analysis and processing, wearable technologies have to deal with various security problems. Active queue management algorithms are used to manage network resources and solve problems in the network. DDoS attacks prevent the effective use of network resources and cause network services to be disrupted or dropout. In this study, we classify the performance of queue management algorithms such as RED, SRED, BLUE, SFB, REM and CoDel under DDoS attacks according to delay, throughput, jitter, fairness index values. As a result of the comparison, thanks to the flexible structure of the CoDel algorithm, it gives better results in terms of packet loss and fairness index value under DDoS attacks.Öğe Q-Learning for Securing Cyber-Physical Systems : A survey(Ieee, 2020) Alabadi, Montdher; Albayrak, ZaferA cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.Öğe Security Classification of Smart Devices Connected to LTE Network(Springer International Publishing Ag, 2022) Ali, Samatar Mohamed; Cakmak, Muhammet; Albayrak, ZaferToday, cellular wireless communication has been widely used in many intelligent automation systems, embedded technologies, robotic smart building, climate monitoring, e-learning, decision support systems, wearable devices for e-health, image, video, and speech processing. The Long-Term Evolution (LTE) network, which is a cellular wireless network technology, is one of the most important parts of the spread of smart systems. Attacks on IP-based LTE networks cause all smart systems to be affected. Attacks and security issues on the LTE network cause the network to slow down or be completely disabled. It also prevents users from receiving the desired Quality of Service (QoS) service. Thus, it cannot serve all smart systems using the LTE network. In this study, problems such as DoS, DDoS, mobile botnet, signalling amplification attacks, network access issues, and IMS security Issues that can be encountered in the cellular LTE network are classified.Öğe Spreading in scale-free computer networks with improved clustering(World Scientific Publ Co Pte Ltd, 2018) Turker, Ilker; Albayrak, ZaferIn this study, we investigated data spreading in computer networks with scale-free topology under various levels of improved clustering. Starting from a pure Barabasi-Albert (BA) network topology, we applied a Poisson-based rewiring procedure with increasing rewiring probability, which promotes local connections. We then performed wired computer network simulations in NS2 simulator for these topologies. We found that for pure BA network, data transfer (throughput) is maximum, where time required for establishing routing scheme, end-to-end delays in data transmission and number of nodes acting in data transfer are at their minimum levels. Improving clustering increases these parameters those are at their minima. A noteworthy finding of this study is that, for moderate levels of clustering, total throughput remains close to its maximum yielding stable transfer rates, although number of infected nodes and end-to-end delay increase. This indicates that clustering promotes spreading phenomena in networks, although it increases average separation. As a result, clustering property emerges as a catalyzer in data spreading with minimal effects on the total amount of transmission.
