Yazar "Ince, Cemile" seçeneğine göre listele

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    A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption
    (Mdpi, 2024) Ince, Cemile; Ince, Kenan; Hanbay, Davut
    As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method's efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.
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    Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ince, Cemile; Ince, Kenan; Hanbay, Davut
    Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version. © 2024 IEEE.
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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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    Investigating Effect of Optimization Algorithms on Chaos Based Image Encryption
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ince, Cemile; Ince, Kenan
    Image encryption is a specialized subfield of cryptography, distinguished by the high correlation between neighboring pixels, which necessitates unique approaches to disrupt this correlation. While all encryption approaches fundamentally involve confusion and diffusion operators, the specific implementations of these operators can vary significantly. In recent years, approaches that combine chaos theory with optimization techniques, which aim to find the optimal solution among numerous possibilities, have become prevalent. These studies, which aim to enhance the security and efficiency of the encryption process, such as by selecting the initial values of chaotic maps in regions with high chaotic behavior, show promise. This study examines the most commonly applied optimization methods in the field of cryptography within the context of image encryption, providing a comprehensive overview. Additionally, it empirically demonstrates that the optimization techniques employed in image encryption studies improve encryption quality. © 2024 IEEE.
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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.
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    Random Strip Peeling: A novel lightweight image encryption for IoT devices based on colour planes permutation
    (Wiley, 2025) Ince, Kenan; Ince, Cemile; Hanbay, Davut
    This paper introduces a novel lightweight colour image encryption algorithm, specifically designed for resource-constrained environments such as Internet of Things (IoT) devices. As IoT systems become increasingly prevalent, secure and efficient data transmission becomes crucial. The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption. Traditional image encryption relies on confusion and diffusion steps. These stages are generally implemented linearly, but this work introduces a new RSP (Random Strip Peeling) algorithm for the confusion step, which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions. The diffusion stage then employs an XOR matrix generated by the Logistic Map. Different evaluation metrics, such as entropy analysis, key sensitivity, statistical and differential attacks resistance, and robustness analysis demonstrate the proposed algorithm's lightweight, robust, and efficient. The proposed encryption scheme achieved average metric values of 99.6056 for NPCR, 33.4397 for UACI, and 7.9914 for information entropy in the SIPI image dataset. It also exhibits a time complexity of O(2xMxN) $O(2\times M\times N)$ for an image of size MxN $M\times N$.