2 results
Search Results
Now showing 1 - 2 of 2
Article Citation - WoS: 13Citation - Scopus: 15A Novel Data Encryption Method Using an Interlaced Chaotic Transform(Pergamon-elsevier Science Ltd, 2024) Gokcay, Erhan; Tora, HakanWe present a novel data encryption approach that utilizes a cascaded chaotic map application. The chaotic map used in both permutation and diffusion is Arnold's Cat Map (ACM), where the transformation is periodic and the encrypted data can be recovered. The original format of ACM is a two-dimensional mapping, and therefore it is suitable to randomize the pixel locations in an image. Since the values of pixels stay intact during the transformation, the process cannot encrypt an image, and known-text attacks can be used to get back the transformation matrix. The proposed approach uses ACM to shuffle the positions and values of two-dimensional data in an interlaced and nested process. This combination extends the period of the transformation, which is significantly longer than the period of the initial transformation. Furthermore, the nested process's possible combinations vastly expand the key space. At the same time, the interlaced pixel and value transformation makes the encryption highly resistant to any known-text attacks. The encrypted data passes all random-data tests proposed by the National Institute of Standards and Technology. Any type of data, including ASCII text, can be encrypted so long as it can be rearranged into a two-dimensional format.Article Citation - WoS: 3Citation - Scopus: 3An Information-Theoretic Instance-Based Classifier(Elsevier Science inc, 2020) Gokcay, ErhanClassification algorithms are used in many areas to determine new class labels given a training set. Many classification algorithms, linear or not, require a training phase to determine model parameters by using an iterative optimization of the cost function for that particular model or algorithm. The training phase can adjust and fine-tune the boundary line between classes. However, the process may get stuck in a local optimum, which may or may not be close to the desired solution. Another disadvantage of training processes is that upon arrival of a new sample, a retraining of the model is necessary. This work presents a new information-theoretic approach to an instance-based supervised classification. The boundary line between classes is calculated only by the data points without any external parameters or weights, and it is given in closed-form. The separation between classes is nonlinear and smooth, which reduces memorization problems. Since the method does not require a training phase, classified samples can be incorporated in the training set directly, simplifying a streaming classification operation. The boundary line can be replaced with an approximation or regression model for parametric calculations. Features and performance of the proposed method are discussed and compared with similar algorithms. (C) 2020 Elsevier Inc. All rights reserved.
