Yazar "Ozkan-Okay, Merve" seçeneğine göre listele

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    A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions
    (Mdpi, 2023) Aslan, Omer; Aktug, Semih Serkant; Ozkan-Okay, Merve; Yilmaz, Abdullah Asim; Akin, Erdal
    Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks.
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    A New Feature Selection Approach and Classification Technique for Current Intrusion Detection System
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ozkan-Okay, Merve; Samet, Refik; Asian, Omer
    These days, various devices including computers, smartphones, internet of things (IoT), and cloud services are using computer networks for data communications. As the computer network is being used extensively, it becomes the target of many attacks. It can be different attacks such as denial of service attack (DoS), remote to user attack (R2L), user to remote attack (U2R), and probing attack. To protect communication networks from network-based attacks, intrusion detection systems (IDSs) have been proposed in many studies. However, today IDSs are not good enough to detect new attack types in the communication networks. To increase the efficiency of the current IDSs, a subset of features needs to be obtained before performing the machine learning classifiers. In this study, a new feature selection method is proposed for current IDSs. In addition, the proposed method is combined with machine learning classifiers and tested on KDD '99 dataset and %99.81 accuracy rate was obtained. The obtained performance is pretty high to separate network attacks from the normal traffic. © 2021 IEEE
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    A Novel Blockchain-Based Scientific Publishing System
    (Mdpi, 2023) Bestas, Mansur; Tas, Ruhi; Akin, Erdal; Ozkan-Okay, Merve; Aslan, Omer; Aktug, Semih Serkant
    The scientific publishing industry is dominated by a few publishers that use centralized systems, which decrease the quality of studies and make the publication process longer. Traditional publication systems generally have high publication costs, slow and biased review processes, copyrights held by publishers, lack of rewards for contributors, lack of connection among researchers, etc. Accordingly, we propose a decentralized blockchain-based scientific publication platform to eliminate the traditional publication system deficiencies. The proposed system uses Ethereum smart contracts to accelerate the publication process and abate the biased evaluation process while reducing the publication cost. The proposed model also improves the quality of scientific studies by adding new features to the publication process. The proposed system increases the number of publishers, makes the publication process fully traceable, and makes scientific papers globally available to anyone with a small fee. In addition, the system provides journals with decentralized models and integrates scientific papers with related data or datasets. The editors, reviewers, and cited authors are also rewarded. The proposed system has been implemented using Ethereum Virtual Machine (EVM), which consists of a front-end, middleware, and back-end. When an author submits a manuscript for evaluation, the system automatically finds the most appropriate editors and reviewers for related fields. After the publication process finishes, editors, reviewers, cited authors, and other contributors are rewarded as a system token-based cryptocurrency.
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    Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Aslan, Omer; Ozkan-Okay, Merve; Gupta, Deepti
    These days, cloud computing is one of the most promising technologies to store information and provide services online efficiently. Using this rapidly developing technology to protect computer-based systems from cyber-related attacks can bring many advantages over traditional protection schemes. The protected assets can be any computer-based systems such as cyber-physical systems (CPS), critical systems, desktop and laptop computers, mobile devices, and Internet of Things (IoT). Malicious software (malware) is any software which targets the computer-based system to launch cyber-attacks to threaten the integrity, confidentiality and availability of the data. To detect the massively growing malware attacks surface, we propose an intelligent behavior-based detection system in the cloud environment. The proposed system first creates a malware dataset on different virtual machines which identify distinctive features efficiently. Then, selected features are given to the learning-based and rule-based detection agents to separate malware from benign samples. Totally, 10,000 program samples have been analyzed to evaluate the performance of the proposed system. The proposed system can detect both known and unknown malware efficiently with high detection and accuracy rate. Besides, the proposed method results have outperformed the leading methods' results in the literature. Our evaluation results show that the proposed algorithms along with machine learning (ML) classifiers achieve 99.8% detection rate, 0.4% false positive rate, and 99.7% accuracy. Our proposed system and algorithms may assist those who would like to develop a novel malware detection system in the cloud environment.