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Location Privacy in Cellular Networks
(01. Izmir Institute of Technology, 2022) Yaman, Okan; Ayav, Tolga; Erten, Yusuf Murat; Ayav, Tolga; Erten, Yusuf Murat
Many third-party utilities and applications that run on devices used in cellular networks keep track of our location data and share it. This vulnerability affects even the subscribers who use dumbphones. This thesis defines three location tracing attacks which are based on utilizing the background data and compares them with the most relevant known attacks. We have demonstrated that any attacker who knows two associated cells of a subscriber with adequate background data can deduce the intermediate cell IDs. Also, utilizing the Hidden Markov Model (HMM) increases the accuracy of an attack. In this dissertation, we introduced novel accuracy metrics for all the anticipated attacks and exploited these for detailed analysis of the threats in a real-life case, a 5G network. This work demonstrates improvements in the current privacy-preserving methods, including adaptation to 5G, and provides insights into preventing this location privacy breach. Various methods have been proposed to overcome these threats and preserve privacy against possible attacks based on this information. A friendly jamming (FJ) based solution, which offers efficient usage of resources, including computing power and energy, was introduced as a solution for these problems. However, one of the tradeoffs of FJ is its viability. Although some studies try to cope with this challenge, they are complicated and focus on old technologies. We propose a lightweight and flexible FJ scheme to address these challenges. We also demonstrate that our model has the same performance as one of the mentioned studies above in a more straightforward way.
Density Grid Based Stream Clustering Algorithm
(Izmir Institute of Technology, 2019) Ahmed, Rowanda Daoud; Ayav, Tolga; Ayav, Tolga; Dalkılıç, Gökhan
Recently as applications produce overwhelming data streams, the need for strategies to analyze and cluster streaming data becomes an urgent and a crucial research area for knowledge discovery. The main objective and the key aim of data stream clustering is to gain insights into incoming data. Recognizing all probable patterns in this boundless data which arrives at varying speeds and structure and evolves over time, is very important in this analysis process. The existing data stream clustering strategies so far, all suffer from different limitations, like the inability to find the arbitrary shaped clusters and handling outliers in addition to requiring some parameter information for data processing. For fast, accurate, efficient and effective handling for all these challenges, we proposed DGStream, a new online-offline grid and density-based stream clustering algorithm. We conducted many experiments and evaluated the performance of DGStream over different simulated databases and for different parameter settings where a wide variety of concept drifts, novelty, evolving data, number and size of clusters and outlier detection are considered. Our algorithm is suitable for applications where the interest lies in the most recent information like stock market, or if the analysis of existing information is required as well as cases where both the old and the recent information are all equally important. The experiments, over the synthetic and real datasets, show that our proposed algorithm outperforms the other algorithms in efficiency.
Fourier Analysis Based Testing of Finite State Machines
(Izmir Institute of Technology, 2019) Takan, Savaş; Takan, Savaş; Ayav, Tolga; Ayav, Tolga
Finite state machine (FSM) is a widely used modeling technique for circuit and software testing. FSM testing is a well-studied topic in the literature and there are several test case generation methods such as W, Wp, UIO, UIOv, DS, HSI and H. Despite the existing methods, there is still a need for alternative techniques with better performance in terms of test suite size, fault detection ratio and test generation time. In this thesis, two new test case generation methods, F and Fw have been proposed. The proposed test generation methods are based on Fourier analysis of Boolean functions. Fourier transformations have been studied extensively in mathematics, computer science and engineering. The proposed F method only tests outputs whereas Fw method also tests the next state with the outputs. In this context, the proposed methods are compared with UIO andWmethods in terms of characteristic, cost, fault detection ratio and effectiveness. The evaluation data are analyzed using T-Test and Hedges’ g. Results show that F and Fw methods outperform the existing methods in terms of the fault detection ratio per test.

Phd Degree / Doktora

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