Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - Scopus: 1A Lightweight and Energy Efficient Secrecy Outage Probability-Based Friendly Jamming(IEEE, 2023) Yaman, Okan; Ayav, Tolga; Erten, Yusuf MuratThird parties and legitimate entities can reach and process users' private data through most wireless networks. However, attackers such as intruders and eavesdroppers may also try to exploit this property in communication. Hence, wireless networks are intrinsically more vulnerable to threats, unlike their wired alternatives. Cryptographic techniques are the conventional approaches to deal with that weakness. Nevertheless, they still need to meet the requirements of contemporary technologies, including IoT nodes with energy and processing power constraints. In that respect, friendly jamming (FJ) is one of the encouraging countermeasures to overcome the mentioned susceptibility since it has an energy-efficient and computation-friendly nature. However, that promising approach brings another challenge, applicability. Although various models exist against this issue, a lightweight scheme compliant with novel technologies is needed. Hence, we propose a more straightforward FJ model evaluated on cellular network-based simulations in this study. Moreover, introducing a lightweight secrecy outage probability definition increases robustness and energy efficiency. © 2023 IEEE.Conference Object Citation - Scopus: 3A Novel Countermeasure for Selective Forwarding Attacks in Iot Networks(IEEE, 2022) Yaman, Okan; Sokat, Barış; Ayav, Tolga; Erten, Yusuf MuratAs the Internet of Things (IoT) devices become more widespread there are rising public concerns about whether or not IoT devices and their services are secure. One of the major threats they face is selective forwarding attacks performed by malicious nodes. Although packets can be lost inherently due to network conditions, malicious nodes, such as those performing blackhole attacks, may deliberately drop some, but not all of them. Therefore, distinguishing these nodes from legitimate ones is not so easy. This study has proposed a lightweight countermeasure to deal with this kind of attack in IoT networks, using the standard IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). The mechanism is based on Mobile Trusted Nodes (MTNs). For the given threat model, we showed that our model has robust detection accuracy and brings no additional overhead to the network. © 2022 IEEE.Article Citation - WoS: 2Citation - Scopus: 2Density-Aware Cellular Coverage Control: Interference-Based Density Estimation(Elsevier, 2019) Eroğlu, Alperen; Yaman, Okan; Onur, ErtanAs demand for mobile communications increases, cells have to become smaller to efficiently use the scarce spectrum and to increase capacity, and small-cell networks will hereby emerge. They may be large in scale and highly dynamic resembling ad hoc networks due to the moving base stations. The variations in the density of the small cell networks impact the quality of service and introduce many novel challenges such as coverage control. We propose two novel base station density estimators, the interference-based density estimator (IDE) and the multi-access edge cloud-based density estimator (CDE) in a three-dimensional field. The estimators employ received signal strength measurements. We validate these two density estimators by using Monte-Carlo simulations. Furthermore, we analyze the impact of density on network outage in cellular networks and propose a density-aware cell zooming technique. According to the observations, base station (BS) density affects network coverage significantly. Received signal strength (RSS)-based density estimators can easily be implemented and applied in the network communication stack although they are more prone to shadowing and fading. Under favour of the density-aware cell zooming method, the network outage can be managed dynamically by adapting the transmit power, which provides a self-configurable and -organized network. (C) 2019 Elsevier B.V. All rights reserved.