Yazar "Ince, Cemile" seçeneğine göre listele

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    An Intrusion Detection System Using Apache Log Files
    (Ieee, 2019) Ince, Cemile; Omac, Zeki
    Today, thousands of systems are exposed to attacks every day. For this reason, many studies have been done in this area. These studies, using data mining (VM) and machine learning techniques, are trying to determine the intrusion detection by examining the log files in different OSI layers. These studies generally use standard data sets and propose hybrid systems to increase the success rate. In this study, an attack detection system (STS) has been developed by using Inonu University web page log files. The log files used as a data set consist of more than 35 million requests and a total of 326 attacks. As a result, the system was trained using artificial neural network (ANN) and attacks were detected with 99.3% success.
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    Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices
    (Springer, 2024) Ince, Cemile; Ince, Kenan; Hanbay, Davut
    In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.