Mixed chaotic maps based encryption for high crypto secrecy

Arivazhagan, S.; Jebarani, W. Sylvia Lilly; Kalyani, S. Veera; Abinaya, A.B.

doi:10.1109/icscn.2017.8085697

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…To generate a chaotic sequence, there are several mathematical operations known as chaotic maps that could be used [25]. Random sequences could be substituted by any number of chaotic maps [41]. For example, the Gingerbreadman chaotic map is an outstanding example of chaotic randomness in two dimensions [42].…”

Section: B Chaotic Mapsmentioning

confidence: 99%

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Hybrid Chaotic Zebra Optimization Algorithm and Long Short-Term Memory for Cyber Threats Detection

Amin,

El-Taweel,

Ali

et al. 2024

IEEE Access

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Cyber Threat Detection (CTD) is subject to complicated and rapidly accelerating developments. Poor accuracy, high learning complexity, limited scalability, and a high false positive rate are problems that CTD encounters. Deep Learning defense mechanisms aim to build effective models for threat detection and protection allowing them to adapt to the complex and ever-accelerating changes in the field of CTD. Furthermore, swarm intelligence algorithms have been developed to tackle the optimization challenges. In this paper, a Chaotic Zebra Optimization Long-Short Term Memory (CZOLSTM) algorithm is proposed. The proposed algorithm is a hybrid between Chaotic Zebra Optimization Algorithm (CZOA) for feature selection and LSTM for cyber threat classification in the CSE-CIC-IDS2018 dataset. Invoking the chaotic map in CZOLSTM can improve the diversity of the search and avoid trapping in a local minimum. In evaluating the effectiveness of the newly proposed CZOLSTM, binary and multi-class classifications are considered. The acquired outcomes demonstrate the efficiency of implemented improvements across many other algorithms. When comparing the performance of the proposed CZOLSTM for cyber threat detection, it outperforms six innovative deep learning algorithms for binary classification and five of them for multiclass classification. Other evaluation criteria such as accuracy, recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy was achieved using the proposed algorithm for binary is 99.83%, with F1-score of 99.82%, precision of 99.83%, and recall of 99.82%. The proposed CZOLSTM algorithm also achieved the best performance for multi-class classification among other compared algorithms.

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Section: B Chaotic Mapsmentioning

confidence: 99%

“…Hence, in this paper, we use a hybrid between three chaotic maps; ginger breadman chaotic map [41], quadratic map [44] and the hénon map [45]. Consequently, the final form given in Equation 11represents our proposed chaotic map:…”

Section: B Chaotic Mapsmentioning

confidence: 99%

Hybrid Chaotic Zebra Optimization Algorithm and Long Short-Term Memory for Cyber Threats Detection

Amin,

El-Taweel,

Ali

et al. 2024

IEEE Access

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“…The algorithm results with less Unified Average Changing Intensity (UACI) value for standard test images and noise immunity is unnoticed. Transposition and substitution based chaotic maps are used for encryption process by Arivazhagan, et al [12]. Arnold cat map and standard chaotic maps are used in the transposition stage and Logistic and Duffing maps are used in the substitution stage.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Transformation and Non-Chaotic Substitution Based Image Cryptosystem

2019

ijeat

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In this article, a Modified Pseudo Hadamard Transformation (MPHT) and non-Chaotic substitution based image encryption scheme has been proposed. Due to intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, images are more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using MPHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for secrete images. An average 99.6089% of Number of Pixel Changing Rate (NPCR) and 33.4328% of Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing techniques

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