Yazar "Akleylek, Sedat" seçeneğine göre listele

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    Password authenticated key exchange-based on Kyber for mobile devices
    (Peerj Inc, 2024) Seyhan, Kubra; Akleylek, Sedat; Dursun, Ahmet Faruk
    In this article, a password-authenticated key exchange (PAKE) version of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) public-key encryption and key-establishment standard is constructed. We mainly focused on how the PAKE version of PQC standard Kyber with mobile compatibility can be obtained by using simple structured password components. In the design process, the conventional password-based authenticated key exchange (PAK) approach is updated under the module learning with errors (MLWE) assumptions to add passwordbased authentication. Thanks to the following PAK model, the proposed Kyber.PAKE provides explicit authentication and perfect forward secrecy (PFS). The resistance analysis against the password dictionary attack of Kyber.PAKE is examined by using random oracle model (ROM) assumptions. In the security analysis, the cumulative distribution function (CDF) Zipf (CDF-Zipf) model is also followed to provide realistic security examinations. According to the implementation results, Kyber.PAKE presents better run-time than lattice-based PAKE schemes with similar features, even if it contains complex key encapsulation mechanism (KEM) components. The comparison results show that the proposed PAKE scheme will come to the fore for the future security of mobile environments and other areas.
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    SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm
    (Peerj Inc, 2023) Mohammadi, Ramin; Akleylek, Sedat; Ghaffari, Ali
    The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%.
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    SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Cevik, Nursah; Akleylek, Sedat
    This paper focuses on the vulnerabilities of ADS-B, one of the avionics systems, and the countermeasures taken against these vulnerabilities proposed in the literature. Among the proposed countermeasures against the vulnerabilities of ADS-B, anomaly detection methods based on machine learning and deep learning algorithms were analyzed in detail. The advantages and disadvantages of using an anomaly detection system on ADS-B data are investigated. Thanks to advances in machine learning and deep learning over the last decade, it has become more appropriate to use anomaly detection systems to detect anomalies in ADS-B systems. To the best of our knowledge, this is the first survey to focus on studies using machine learning and deep learning algorithms for ADS-B security. In this context, this study addresses research on this topic from different perspectives, draws a road map for future research, and searches for five research questions related to machine learning and deep learning algorithms used in anomaly detection systems.
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    A Systematic Literature Review on Host-Based Intrusion Detection Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Satilmis, Hami; Akleylek, Sedat; Tok, Zaliha Yuce
    With the advancements in computer networks and systems, the number of security vulnerabilities and cyber attacks targeting/using these vulnerabilities continues to increase. Consequently, various intrusion detection systems (IDS) have been developed to detect cyber attacks and ensure information security. IDSs are categorized into two classes based on the data sources: Network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS). In this systematic literature review (SLR), studies are examined that focus on HIDS or propose methods applicable to HIDS, as well as those related to IDSs that can be converted into HIDSs. The studies published between 2020 and 2023 are collected from widely used academic databases through various query statements. Filtering based on specific selection and elimination criteria is undergone by the collected studies, resulting in 21 studies for examination. Subsequently, these studies and their advantages and disadvantages are discussed. In addition, while examining the studies, five research questions are addressed. Finally, the defects, potential areas for improvement, and future research directions related to HIDSs are discussed.