Voice encryption using a unified hyper-chaotic system

Bonny, Talal; Nassan, Wafaa Al; Baba, Abdullatif

doi:10.1007/s11042-022-13317-w

Cited by 21 publications

(4 citation statements)

References 38 publications

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“…However, amidst this challenge, the advent of deep learning, a burgeoning branch of machine learning research, offers a glimmer of hope. Deep learning models, including Recurrent Neural Networks (RNNs), have demonstrated remarkable efficacy across various application domains [5][6][7][8][9][10][11]. RNNs, in particular, stand out for their capacity to handle sequential data and capture temporal dependencies, thereby offering a flexible framework for analyzing the dynamic behavior of chaotic systems [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks

Al Nassan,

Bonny,

AlShabi

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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This Chaos theory has long been a fascinating realm of study, offering insights into systems characterized by sensitivity to initial conditions and complex, unpredictable behaviour. Among these intriguing systems, the "Capsule-Shaped Equilibrium Curve Chaotic System" stands out due to its distinctive and intricate dynamics. In this paper, we present a novel approach to understanding and predicting the behaviour of this complex chaotic system through the application of Recurrent Neural Networks (RNNs). Our investigation begins with a thorough examination of the capsule-shaped equilibrium curve chaotic system, revealing its underlying principles and revealing its chaotic nature. We employ the scalability of neural networks to propose an innovative approach for predicting the temporal progression of this system. A promising avenue for modeling the dynamic behavior of chaotic systems with a high degree of precision is offered by the RNN framework, which is capable of capturing temporal dependencies. We delve into the details of our prediction methodology, including data preprocessing, network architecture, and training strategies tailored to the unique characteristics of the capsule-shaped equilibrium curve chaotic system. We conduct extensive experiments and provide quantitative evaluations of prediction precision and dependability to evaluate the predictive capabilities of our neural network-based strategy. Furthermore, this investigation contributes to the comprehension of chaotic systems and opens the door to practical applications in diverse fields, such as physics, engineering, and finance, where precise predictions of chaotic dynamics are essential for making decisions and controlling systems.

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Section: Introductionmentioning

confidence: 99%

Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks

Al Nassan,

Bonny,

AlShabi

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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“…Sensitive data security and privacy are essential to maintaining today's digital environment [1][2][3]. Encryption techniques are widely used to protect data from unauthorized access or interception.…”

Section: Introductionmentioning

confidence: 99%

Data-driven techniques for signal recovery and decryption

Al Nassan,

Bonny,

AlShabi

2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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In an era where data security is paramount, encrypting sensitive information is ubiquitous. Traditional encryption methods often rely on complex algorithms and keys, making decryption an intricate and computationally intensive task. This paper introduces a novel approach to signal recovery and decryption, leveraging data-driven techniques, specifically Long Short-Term Memory (LSTM) neural networks. Our methodology is not limited to any specific encryption algorithm, allowing it to be applicable in various domains. We explore using LSTM networks as powerful tools for deciphering encrypted signals without prior knowledge of the encryption process. The critical insight behind our approach is the ability of LSTMs to capture intricate patterns and dependencies in the encrypted data, thus enabling the reconstruction of the original signal. We present experimental results that showcase the effectiveness of our data-driven decryption approach in a range of scenarios. This research signifies a paradigm shift in signal recovery and decryption, offering an alternative to traditional cryptanalysis techniques. By harnessing the power of data-driven modelling, we open new avenues for retrieving valuable information from encrypted signals, with potential applications in data cybersecurity and beyond.

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“…There has been a growing interest in using chaotic systems for secure communication and encryption. Chaotic systems, with their complex dynamics, sensitivity to initial conditions, and deterministic nature, provide inherent randomness and unpredictability that can enhance the security of communication channels [20]. The application of chaos theory to cryptography, known as chaotic cryptology, has attracted much interest in recent years.…”

Section: Introductionmentioning

confidence: 99%

Multistability and Bifurcation Analysis of a Novel 3D Jerk System: Electronic Circuit Design, FPGA Implementation, and Image Cryptography Scheme

Bonny,

Vaidyanathan,

Sambas

et al. 2023

IEEE Access

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In this paper, we propose a novel 3-D jerk system with three quadratic nonlinear terms and demonstrate the dynamical properties of the proposed jerk system in terms of phase portraits, bifurcation diagrams, Lyapunov exponents, multistability and coexisting attractors. For practical implementations, we apply Multisim version 14.0 to design an electronic model of the proposed 3-D jerk system. To demonstrate the feasibility of the proposed chaotic jerk system, we implement the system using a field-programmable gate array (FPGA), which shows high throughput and low power consumption. Furthermore, a new image encryption scheme based on the proposed jerk system is developed, which involves permutation and diffusion operations. Experimental results and security analysis show the effectiveness of our proposed algorithm in terms of high security and excellent encryption performance.

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Voice encryption using a unified hyper-chaotic system

Cited by 21 publications

References 38 publications

Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks

Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks

Data-driven techniques for signal recovery and decryption

Multistability and Bifurcation Analysis of a Novel 3D Jerk System: Electronic Circuit Design, FPGA Implementation, and Image Cryptography Scheme

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