486 research outputs found

    Cellular Automata for Medical Image Processing

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    Robust Image Encryption Based on Balanced Cellular Automaton and Pixel Separation

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    The purpose of image encryption is to protect content from unauthorized access. Image encryption is usually done by pixel scrambling and confusion, so process is possible to reverse only by knowing secret information. In this paper we introduce a new method for digital image encryption, based on a 2D cellular automaton and pixel separation. Novelty in the proposed method lies in the application of the balanced 2D cellular automata with extended Moore neighborhood separately on each level of pseudorandom key-image. This process extends key space several times when compared to the previous methods. Furthermore, pixel separation is introduced to define operation for each pixel of the source image. Thanks to pixel separation, decryption process is more difficult to conduct without knowing secret information. Moreover, encryption is robust against different statistical attacks and analysis, does not affect image quality and can cope with loss of encrypted image content

    A parallel block-based encryption schema for digital images using reversible cellular automata

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    AbstractWe propose a novel images encryption schema based on reversible one-dimensional cellular automata. Contrasting to the sequential operating mode of several existing approaches, the proposed one is fully parallelizable since the encryption/decryption tasks can be executed using multiple processes running independently for the same single image. The parallelization is made possible by defining a new RCA-based construction of an extended pseudorandom permutation that takes a nonce as a supplementary parameter. The defined PRP exploit the chaotic behavior and the high initial condition's sensitivity of the RCAs to ensure perfect cryptographic security properties. Results of various experiments and analysis show that high security and execution performances can be achieved using the approach, and furthermore, it provides the ability to perform a selective area decryption since any part of the ciphered-image can be deciphered independently from others, which is very useful for real time applications

    An Image Encryption Scheme Based on DNA Computing and Cellular Automata

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    Networks have developed very quickly, allowing the speedy transfer of image information through Internet. However, the openness of these networks poses a serious threat to the security of image information. The field of image encryption has drawn attention for this reason. In this paper, the concepts of 1-dimensional DNA cellular automata and T-DNA cellular automata are defined, and the concept of reversible T-DNA cellular automata is introduced. An efficient approach to encryption involving reversible T-DNA cellular automata as an encryption tool and natural DNA sequences as the main keys is here proposed. The results of a simulation experiment, performance analysis, and comparison to other encryption algorithms showed this algorithm to be capable of resisting brute force attacks, statistical attacks, and differential attacks. It also enlarged the key space enormously. It meets the criteria for one-time pad and resolves the problem that one-time pad is difficult to save

    Chaotic Behaviors of Symbolic Dynamics about Rule 58 in Cellular Automata

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    The complex dynamical behaviors of rule 58 in cellular automata are investigated from the viewpoint of symbolic dynamics. The rule is Bernoulli στ-shift rule, which is members of Wolfram’s class II, and it was said to be simple as periodic before. It is worthwhile to study dynamical behaviors of rule 58 and whether it possesses chaotic attractors or not. It is shown that there exist two Bernoulli-measure attractors of rule 58. The dynamical properties of topological entropy and topological mixing of rule 58 are exploited on these two subsystems. According to corresponding strongly connected graph of transition matrices of determinative block systems, we divide determinative block systems into two subsets. In addition, it is shown that rule 58 possesses rich and complicated dynamical behaviors in the space of bi-infinite sequences. Furthermore, we prove that four rules of global equivalence class ε43 of CA are topologically conjugate. We use diagrams to explain the attractors of rule 58, where characteristic function is used to describe that some points fall into Bernoulli-shift map after several times iterations, and we find that these attractors are not global attractors. The Lameray diagram is used to show clearly the iterative process of an attractor

    A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection

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    The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.publishedVersio

    Cellular Automata

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    Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

    Deteksi, Klasifikasi dan Model Prediksi Tutupan Lahan Embung untuk Pertanian menggunakan Support Vector Machine dan Markov Cellular Automata

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    Sektor pertanian merupakan sektor andalan dalam perekonomian Kabupaten Malang. Namun Kabupaten Malang telah mengalami penurunan luas panen padi yang disebabkan oleh kekeringan. Salah satu upaya Pemerintah untuk mengatasi hal tersebut ialah dengan melakukan kegiatan pembangunan embung untuk pertanian. Penggunaan teknologi remote sensing (penginderaan jauh) merupakan salah satu alat yang efektif untuk memantau fenomena perubahan yang terjadi secara terus menerus dan dalam area yang luas dalam hal ini embung. Tujuan dari penelitian ini adalah menentukan dan menganalisis penggunaan klasifikasi SVM pada citra satelit dalam hal deteksi embung, serta mengetahui model prediksi perubahan lahan embung untuk pertanian di Kabupaten Malang. Penelitian ini menggunakan Support Vector Machine (SVM) untuk mengklasifikasi jenis tutupan lahan dan model Markov Cellular Automata (Markov-CA) untuk memprediksi perubahan tutupan lahan embung untuk pertanian berdasarkan peluang perubahan lahan. Model prediksi dibangun dengan kombinasi interval waktu yaitu tahun 2004-2009 dan 2009-2015 yang kemudian diuji untuk memprediksi tutupan lahan tahun 2015 dan 2020. Penelitian ini menggunakan citra satelit PlanetScope, Landsat 7 dan 8. Penelitian ini terdiri dari empat pekerjaan utama yaitu praproses citra satelit, klasifikasi citra satelit, deteksi dan model prediksi perubahan penggunaan lahan. Hasil penelitian menunjukan penambahan jumlah area contoh pada algoritme SVM berdampak pada waktu komputasi dan akurasi klasifikasi embung, dimana jumlah area contoh yang sedikit waktu komputasi 16 detik dan akurasi 0.5641. Sedangkan jumlah area contoh yang banyak waktu komputasi 307 detik dan akurasi 07093. Model prediksi Markov-CA memiliki akurasi yang baik daripada model aktual pada kasus deteksi perubahan lahan embung untuk pertanian di Kabupaten Malang sebesar 0.3834 dan 0.3769
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