Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Conference Object
    Citation - Scopus: 10
    Security and Privacy Challenges, Solutions, and Open Issues in Smart Metering: A Review
    (Institute of Electrical and Electronics Engineers Inc., 2021-09-15) Win, Lae Lae; Tonyali, Samet
    The traditional power grid becomes ‘smart’ when it is combined with the communication and information technology. Along with smart grid, the traditional meter is replaced with smart meter. Smart meters play an important role in energy consumption reporting and, thereby, billing. Besides smart meters, the smart grid communication network is composed of heterogeneous devices that are communicating through public networks. Therefore, smart metering communications are susceptible to cyber-attacks and privacy breaches which are still under debating. This paper gives a brief overview of smart grid, smart metering, and the communication networks. Then, the privacy and security requirements of the smart grid network are derived. The various kind of cyber-attacks are discussed, after that, the different schemes and approaches that have been proposed in previous papers are reviewed. Lastly, the open issues on security and privacy of smart grid metering communications are highlighted. © 2022 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 10
    Performance Evaluation of TLS 1.3 Handshake on Resource-Constrained Devices Using NIST's Third Round Post-Quantum Key Encapsulation Mechanisms and Digital Signatures
    (Institute of Electrical and Electronics Engineers Inc., 2022-09-14) Saribas, Sultan; Tonyali, Samet
    Towards the end of the 20. century, quantum computing came into sight by the main effect of Shor's Algorithm. While this algorithm offers a solution for the factorization problem, which makes it exponentially faster, it alongside becomes a critical threat for the encryption schemes since mostly their security mechanisms rely on the difficulty of integer factorization or discrete log problems. Despite it is not known when advanced quantum computers will show up, yet, once it is developed, most existing cryptography will be rendered useless, which means all existing information security will be vulnerable. Owing to this risky situation, The National Institute of Standards and Technology (NIST) launched post-quantum cryptography (PQC) standardization process for the development of PQC schemes. In this paper, we have chosen three key encapsulation mechanisms and two digital signature algorithms with different parameter sets from the round three submissions. We measured their TLS 1.3 handshake performance using two resource-constrained devices and compared it to that of classical encryption and digital signature schemes. Experiment results showed that post-quantum algorithms come with an extra message overhead while their handshake delay values are promising. © 2022 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 2
    Machine Learning Algorithms Against Hacking Attack and Detection Success Comparison
    (Institute of Electrical and Electronics Engineers Inc., 2020-09-15) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.
    Power system protection units has got enormous importance with the growing risk of cyber-attacks. To create sustainable and well protected system, power system data must be healthy. For that purpose, many machine learning applications have been developed and used for bad data detection. However, each method has got different detection and application process. Methods has superiority over other methods. Although, an algorithm can detect some injections easily, same algorithm can be fail when injection type changed. So methods have got different success results when the injection types changed. For that reason, different injection types are applied on power system IEEE 14 bus system via created special hacking algorithm. PSCAD and python linkage has been used for simulation and detection parts. 3 different injection types created and applied on the system and five different most popular algorithms (SVM, k- NN, LDA, NB, LR) tested. Each algorithm's performances are compared and evaluated. © 2020 Elsevier B.V., All rights reserved.
  • Article
    Citation - Scopus: 3
    Attack-Aware Dynamic Upstream Bandwidth Assignment Scheme for Passive Optical Network
    (Walter de Gruyter GmbH, 2019-09-19) Butt, Rizwan Aslam; Faheem, Muhammed Yasir; Ashraf, Muhammad Waqar; Khawaja, Attaullah; Raza, Basit
    Network security is an important component of today's networks to combat the security attacks. The passive optical network (PON) works at the medium access layer (MAC). A distributed denial of service (DDOS) attack may be launched from the network and transport layers of an Optical Network unit (ONU). Although there are various security techniques to mitigate its impact, however, these techniques cannot mitigate the impact on the MAC Layer of the PON and can cause an ONU to continuously drain too much bandwidth. This will result in reduced bandwidth availability to other ONUs and, thus, causing an increase in US delays and delay variance. In this work we argue that the impact of a DDOS attack can be mitigated by improving the Dynamic bandwidth assignment (DBA) scheme which is used in PON to manage the US bandwidth at the optical line terminal (OLT). The present DBA schemes do not have the capability to combat a security attack. Thus, this study, uses a machine learning approach to learn the ONU traffic demand patterns and presents a security aware DBA (SA-DBA) scheme that detects a rogue (attacker) ONU from its traffic demand pattern and limits its illegitimate bandwidth demand and only allows it the bandwidth assignment to it as per the agreed service level agreement (SLA). The simulation results show that the SA-DBA scheme results in up to 53%, 55% and 90% reduced US delays and up to 84%, 76% and 95% reduced US delay variance of T2, T3 and T4 traffic classes compared to existing insecure DBA schemes. © 2023 Elsevier B.V., All rights reserved.