This paper introduces a novel approach to addressing the security challenges of the Internet of Things (IoT) by presenting a secure Chaos-based lightweight cryptosystem. The proposed design incorporates a Pseudo-Chaotic Numbers Generator combined with the Speck64/128 lightweight block cipher, to meet the stringent requirements of security and lightweight characteristics. First, we subject the cryptosystem to a battery of rigorous tests, including various cryptanalytics such as brute-force, differential attacks, and statistical attacks. The results of these tests clearly demonstrate the cryptosystem’s exceptional security resilience and its capacity to effectively withstand these attacks. Then, the novel cryptosystem architecture, specifically designed for resource-limited applications like IoT, was implemented on a Xilinx PYNQ-Z2 XC7Z020 FPGA platform, which aligns perfectly with the constraints of IoT devices. The investigation comprises a thorough analysis and evaluation of the developed cryptosystem according to the algorithm complexity and the achieved precision, hardware area, maximum operational frequency, throughput, efficiency, and power consumption. The findings prove the effectiveness of our approach in terms of computational complexity, memory requirements, and power consumption. Eventually, after reviewing and comparing our results to the existing literature, our cryptosystem’s superiority becomes evident. The simulation results and performance analysis show that the proposed Chaos-Based Lightweight Cryptosystem (SCBLC) can be considered an ideal choice for securing communication in IoT devices with limited resources, making significant strides toward enhancing IoT network security.

Lightweight ciphers have been developed to meet the rising need for secure communication in environments with limited resources. These ciphers provide robust encryption while ensuring efficient computation. Our paper introduces a new enhanced PRESENT lightweight cipher that utilizes chaotic systems to enhance its robustness and randomness while retaining the simplicity and compactness of the original cipher. By integrating chaotic maps into the cipher's core components, we improve its resistance against advanced cryptanalysis, such as differential, Salt & Peppers Noise (SPN), and loss data attacks. We also optimize the design for computational efficiency, making it suitable for deployment in devices with limited resources. Through extensive simulations and comparative analyses, we demonstrate the superiority of our enhanced cipher in terms of security and efficiency compared to other state-of-the-art lightweight ciphers. Our research contributes to the advancement of lightweight cryptography and provides a promising solution for secure communication in resource-constrained environments.

Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi‐stage technique. The first stage combines an R–R peak extraction with a low‐pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network‐based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT‐BIH‐database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1‐score of 0.99 is obtained. Experimental results show a high achievement compared to the state‐of‐the‐art models where the implementation of GPU confirms the low computational complexity of the system.

Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhythm that can lead to heart-related complications. Remarkable interest has been given to ECG signals analysis for AF detection in an early stage. In this context, we propose an artificial neural network ANN application to classify ECG signals into three classes, the first presents Normal Sinus Rhythm NSR, the second depicts abnormal signal with Atrial Fibrillation (AF) and the third shows noisy ECG signals. Accordingly, we achieve 93.1% accuracy classification results, 95.1% of sensitivity, 90.5% of specificity and 98%. Furthermore, we yield a value of zero error and a low value of cross entropy, which prove the robustness of the proposed ANN model architecture. Thus, we outperform the state of the art by achieving high accuracy classification without pre-processing step and without high level of feature extraction, and then we enable clinicians to determine automatically the class of each patient ECG signal.

In order to improve security and efficiency, this study presents a novel lightweight encryption technique that makes use of chaotic systems. Our method creatively combines the new chaotic KLEIN_64 algorithm with the Keccak-256 hash function, offering a solid basis for producing initial values essential for causing chaotic maps during the encryption process. After a deep validation with rigorous NIST testing, our chaotic pseudo random generator, LAC, exhibits excellent reliability and cryptographic robustness. Furthermore, the complexity of the cryptographic round function is improved by incorporating a second chaotic pseudo random generator that combines chaotic LFSR and Skew Tent Maps, thereby fortifying security measures.Designed with resource-limited applications in mind, our approach ensures that the cryptosystem remains both lightweight and efficient, meeting the stringent constraints typical of such environments. The practical feasibility and performance of our approach are extensively evaluated through FPGA implementation on the Zybo 7Z010 platform. Our implementation achieves a remarkable throughput of 2.820 Gbps while maintaining optimal resource utilization and efficiency. Extensive experimental results confirm the superior security of our cryptosystem, with correlation tests, entropy measurement, and histogram analysis showcasing robustness against statistical attacks. Moreover, the cryptosystem shows little fluctuation in the Unified Average Changing Intensity (UACI) and Non-Linear Pixel Change Rate (NPCR), confirming its resistance to differential attacks. Overall, our technique advances lightweight cryptography by providing a robust and efficient solution to modern cybersecurity challenges. In particular, our approach is well-suited for applications with limited resources, ensuring that security is maintained without compromising on performance or efficiency, thus fulfilling the needs of modern, constrained environments.