Yazar "Aslan, Omer" 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 Comprehensive Review on Malware Detection Approaches
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Aslan, Omer; Samet, Refik
    According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques. In order to protect computer systems and the Internet from the malware, the malware needs to be detected before it affects a large number of systems. Recently, there have been made several studies on malware detection approaches. However, the detection of malware still remains problematic. Signature-based and heuristic-based detection approaches are fast and efficient to detect known malware, but especially signature-based detection approach has failed to detect unknown malware. On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches also emerge to detect some portion of known and unknown malware. However, no approach can detect all malware in the wild. This shows that to build an effective method to detect malware is a very challenging task, and there is a huge gap for new studies and methods. This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches. Paper goal is to help researchers to have a general idea of the malware detection approaches, pros and cons of each detection approach, and methods that are used in these approaches.
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    A New Malware Classification Framework Based on Deep Learning Algorithms
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Aslan, Omer; Yilmaz, Abdullah Asim
    Recent technological developments in computer systems transfer human life from real to virtual environments. Covid-19 disease has accelerated this process. Cyber criminals' interest has shifted in a real to virtual life as well. This is because it is easier to commit a crime in cyberspace rather than regular life. Malicious software (malware) is unwanted software which is frequently used by cyber criminals to launch cyber-attacks. Malware variants are continuing to evolve by using advanced obfuscation and packing techniques. These concealing techniques make malware detection and classification significantly challenging. Novel methods which are quite different from traditional methods must be used to effectively combat with new malware variants. Traditional artificial intelligence (AI) specifically machine learning (ML) algorithms are no longer effective in detecting all new and complex malware variants. Deep learning (DL) approach which is quite different from traditional ML algorithms can be a promising solution to the problem of detecting all variants of malware. In this study, a novel deep-learning-based architecture is proposed which can classify malware variants based on a hybrid model. The main contribution of the study is to propose a new hybrid architecture which integrates two wide-ranging pre-trained network models in an optimized manner. This architecture consists of four main stages, namely: data acquisition, the design of deep neural network architecture, training of the proposed deep neural network architecture, and evaluation of the trained deep neural network. The proposed method tested on Malimg, Microsoft BIG 2015, and Malevis datasets. The experimental results show that the suggested method can effectively classify malware with high accuracy which outperforms the state of the art methods in the literature. When proposed method tested on Malimg dataset, 97.78% accuracy is obtained which is outperformed most of the ML-based malware detection method.
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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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    An effective prediction method for network state information in SD-WAN
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Akin, Erdal; Sarac, Ferdi; Aslan, Omer
    In a software-defined wide area network (SD-WAN), a logically centralized controller is responsible for computing and installing paths in order to transfer packets among geographically distributed locations and remote users. Accordingly, this would necessitate obtaining the global view and dynamic network state information (NSI) of the network. Therefore, the centralized controller periodically collects link-state information from each port of each switch at fixed time periods. While collecting NSI in short periods causes protocol overhead on the controller, collecting in longer periods leads to obtaining inaccurate NSI. In both cases, packet losses are inevitable, which is not preferred for quality of service (QoS). Packet loss needs to be reduced by minimizing the protocol overload on the controller and collecting accurate NSI to provide better QoS. This work proposes an effective prediction method for collecting NSI (PM-NSI) that significantly reduces packet loss and controller protocol load allowing the controller to collect accurate NSI in longer periods. The proposed method is compared against the existing NSI collection method, which collects NSI periodically, in use on the RYU controller and the Mininet emulator by using a dynamic routing algorithm. The test results indicated that PM-NSI reduces controller load around 1000% by collecting NSI in longer periods and so outperforms the existing periodic NSI collection method in terms of packet loss, jitter, controller load, and thus QoS.
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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.
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    Using a Subtractive Center Behavioral Model to Detect Malware
    (Wiley-Hindawi, 2020) Aslan, Omer; Samet, Refik; Tanriover, Omer Ozgur
    In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a subtractive center behavior model (SCBM) to create a malware dataset that captures semantically related behaviors from sample programs. In the proposed model, system paths, where malware behaviors are performed, and malware behaviors themselves are taken into consideration. This way malicious behavior patterns are differentiated from benign behavior patterns. Features that could not exceed the specified score are removed from the dataset. The datasets created using the proposed model contain far fewer features than the datasets created by n-gram and other models that have been used in other studies. The proposed model can handle both known and unknown malware, and the obtained detection rate and accuracy of the proposed model are higher than those of the known models. To show the effectiveness of the proposed model, 2 datasets with score and without score are created by using SCBM. In total, 6700 malware samples and 3000 benign samples are tested. The results are compared with those derived from n-gram and models from other studies in the literature. The test results show that, by combining the proposed model with an appropriate machine learning algorithm, the detection rate, false positive rate, and accuracy are measured as 99.9%, 0.2%, and 99.8%, respectively.