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Öğe A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption(Mdpi, 2024) Ince, Cemile; Ince, Kenan; Hanbay, DavutAs 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.Öğe Benchmarking Various 1D Chaotic Maps For Lightweight Pseudo-Random Number Generation(Institute of Electrical and Electronics Engineers Inc., 2024) Kaya, Muhammed Saadetdin; Ince, KenanThis study comparatively analyzes the performance of various one-dimensional (1D) chaotic maps for Internet of Things (IoT) devices and resource-constrained environments using a lightweight Pseudo Random Number Generator (PSRNG). The research aims to determine the most suitable 1D chaotic map for IoT devices and image encryption applications. The study tested and evaluated nine different chaotic maps such as Logistic Map, ID ILM, ID IQM, Tent Map, Sine Map, Gauss Map, Circle Map, Chebyshev Map and Quadratic Map using the NIST-SP800-22R1A statistical test suite. Five out of the nine chaotic maps (1D IQM, 1D ILM, Sine Map, Gauss Map, and Chebyshev Map) successfully passed all the statistical tests. Regarding security, 1D IQM, 1D ILM, and Gauss Map were found to be resistant to both brute-force attacks and quantum attacks. According to the execution time tests, Sine Map has the fastest execution time but does not provide quantum resistance. Among the quantum-resistant maps, Gauss Map showed the quickest performance. It is concluded that Gauss Map can be a good option for applications requiring secure and efficient random number generation, especially for IoT devices and other resource-constrained environments. This study guides IoT developers and cryptography experts to develop random number generation solutions that meet high-security standards in resource-constrained environments. © 2024 IEEE.Öğe Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal(Institute of Electrical and Electronics Engineers Inc., 2024) Ince, Cemile; Ince, Kenan; Hanbay, DavutLightweight 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.Öğe Collaboration Graph as a New Graph Definition Approach(Ieee, 2017) Ince, Kenan; Karci, AliModelling is a crucial step for analyzing the data. Graph is an important modelling technique for some areas especially if the data has some kind of relation between each other like complex networks. There are plenty of study in complex network area which uses graphs as a modelling tool. Collaboration networks are a kind of complex evolving networks. Also community detection and evaluation is an important topic in graph mining. Especially in recent years, the importance of social networks is increased and mining of these networks became more vital. However, there is no specific topic about collaboration graph which focus on how to evaluate the how strong a bond is and meaning of it. This study aims to propose a definition which named collaboration graph as a graph type for understanding structure of the network more clearly and less noisy.Öğe Collaboration Network Analysis of Turkey in Regional Basis(Ieee, 2017) Ince, Kenan; Karci, AliModeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such that computer networks, social networks, data structures and parallel programming topologies, etc. In this study, our data is scientific publications of Turkey and the goal is to find the cliques of academic collaboration network of Turkey at regional base via BK (Bron-Kerbosch) algorithm. By finding cliques, we aim to explore how relations are established between universities of Turkey and to reveal prominent university in each region.Öğe Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT(Springer, 2024) Ince, KenanThe Internet of Things (IoT) is an incredibly growing technology. However, due to hardware inadequacy, IoT security is not improving to the same extent. For this reason, lightweight encryption algorithms have begun to be developed. This paper presents a method for assessing the security of Pseudorandom Number Generator (PRNG) generated binary sequences in a reasonable time using a pre-trained deep learning (DL) model. Due to their long execution times, Randomness Test Standards (RTSs) that include statistical tests that examine whether the sequences generated by PRNGs contain any patterns that cause cryptographic vulnerabilities are not suitable for running on edge devices with low processing capacities such as the IoT. We argue that every random sequence, even generated by a PRNGs that are classified as cryptographically secure, utilized in cryptographic applications should be used after successful results obtained from RTSs in every time. Therefore, an alternative method based on machine learning has been proposed to overcome the processing time problem of these test suites. The most utilized RTSs are NIST 800-22 Rev.1a, GB/T 32915-2016 and AIS 20/31. The 800-22 Rev.1a, which NIST has designated as a standard, has been observed to be the most referenced test standard in the literature. With this implementation, we sought to show that 15 statistical tests of the NIST 800-22 rev.1a environment can be modeled using DL. The application findings indicate that this modeling can serve as an alternative to the existing test environments. The average accuracy recorded throughout 15 tests was 98.64 percent. As a result, the trained model can be implemented even in edge computing devices with limited capability including IoTs.Öğe Investigating Effect of Optimization Algorithms on Chaos Based Image Encryption(Institute of Electrical and Electronics Engineers Inc., 2024) Ince, Cemile; Ince, KenanImage 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.Öğe Knit scrambling: A novel image scrambling framework and its demonstration in image encryption(Elsevier, 2026) Kaya, Muhammed Saadetdin; Ince, KenanThe exponential growth of visual data and the expansion of resource-constrained IoT platforms have intensified the demand for lightweight yet secure image protection schemes. Conventional ciphers, while cryptographically strong, often fail to meet real-time and hardware-efficiency requirements for image data. To address this gap, this study presents the Knit Scrambling (KS) framework, a textile-inspired deterministic permutation framework designed for reversible image scrambling with linear computational cost. This approach models an image as a sequence interwoven from multiple subsequences following cyclic knitting patterns, ensuring both reversibility and high diffusion. A specific instantiation, termed Triple Check Pattern (TCP), realizes the KS framework by dividing the image into three subsequences and applying cyclic pattern rotations to enhance pixel decorrelation while preserving strict invertibility. The confusion process is integrated with a lightweight diffusion stage based on a key-nonce-derived chaotic keystream generated by a one-dimensional logistic map, eliminating plaintext dependence and enabling per-image uniqueness. Experimental analyses conducted on benchmark color images show near-uniform histograms, high entropy close to eight bits, and strong differential performance, with average NPCR around 99.6 percent and UACI approximately 33.5 percent. Statistical randomness evaluation using the NIST SP 800-22 test suite confirms the scheme's ability to produce unpredictable ciphertexts, while runtime benchmarking on both desktop and embedded-class hardware demonstrates real-time feasibility. The results indicate that the proposed framework provides an effective and hardware-efficient alternative to existing chaos-based and geometric scrambling approaches for lightweight image encryption in IoT environments. The proposed framework (KS) defines a general textile-inspired permutation model, while its implementation through the TCP algorithm demonstrates how this model can be practically realized to achieve efficient and reversible image scrambling.Öğe Modelling Academic Collaboration Network of Turkey as Graph and Dominating Set Analysis(Ieee, 2019) Kaygusuz, Muberra; Ince, KenanSocial network analysis; it is the work of deduction for a specific purpose from a interaction between different actors with dynamic and complex structure. The dominating set problem is to determine the dominant cluster with the minimum number of nodes in a network. In a social network, the criteria of centrality are important for the identification of the dominant node or nodes. Because the high centrality value of a node in the network increases the likelihood of being included in the dominant cluster. In this study, firstly, the data to be used in the analysis are modeled as graphs. By taking into consideration the positions of each node in the academic collaboration network, centrality values are calculated which provide information about the position of node in the network. The centrality criteria used in the study are degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. The dominant nodes in the network were determined on the basis of universities by using the criteria of centrality.Öğe Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices(Springer, 2024) Ince, Cemile; Ince, Kenan; Hanbay, DavutIn 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.Öğe Parallel Implementation of Genetic Algorithms on Multi-Core PCs(Turgut Ozal Univ, 2012) Ince, Kenan; Karci, AliThe goal of this study is reducing the time complexity of complex processes by using parallel programming on multi core PCs which is widely used in our daily computers. In this regard, same level as a result of the acceptance can be obtained in a shorter operating time by parallel implementation of an algorithm. Because of the increasing number of cores in processors with each passing day, we made sure that the application is scalable. This study show, if we implement the same problem parallel, the significant increase in performance occurs by increasing the number of cores. We implemented genetic algorithm to optimize Rastrigin function using Intel TBB. Our tests are made in two different computers. First computer contains the processors of Intel i3 M370@2.4 GHZ which has two core and each core has HT (hyper threading [1]) running on Microsoft Windows Seven operating system. Second computer contains the processors of Intel i5 520M@2.4 GHZ, which also has two cores and each core has HT as previous processor, running on Apple Mac OSX Lion operating system. In this study, not only the comparison of the serial and parallel versions of algorithm in the same platform is observed but also the differences between two different platforms are observed due to the operating time.Öğe Random Strip Peeling: A novel lightweight image encryption for IoT devices based on colour planes permutation(Wiley, 2025) Ince, Kenan; Ince, Cemile; Hanbay, DavutThis 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 $M\times N$.
