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collectively underscore the growing importance of encryption methods in diverse technological domains. The exponential growth of digital data transmission, particularly in sensitive domains like healthcare and IoT, has amplified the demand for robust encryption techniques. Traditional encryption methods (e.g., AES, DES) often falter against sophisticated cyberattacks due to their linearity and deterministic nature. Chaos-based encryption, leveraging the inherent unpredictability and sensitivity of chaotic systems, has emerged as a promising alternative. However, challenges persist in ensuring data integrity, resisting differential attacks, and maintaining real-time efficiency. This study addresses these gaps by integrating a refined hash function into a chaos-based encryption framework, enhancing both security and computational efficiency. This work aims to Design a chaos-based image encryption system using refined hash function, evaluate the system’s security through statistical metrics like NPCR, UACI, Entropy and also to Compare its performance with existing methods in terms of execution time and robustness. The proposed system enhances data confidentiality and integrity for applications like medical imaging and IoT security. By combining chaotic dynamics with a hash function sensitive to minute data alterations, it offers a scalable solution resistant to differential and brute-force attacks. This study is important for several reasons. First, chaos-based encryption offers superior security by leveraging its extreme sensitivity to initial conditions and the inherently unpredictable nature of chaotic systems. Second, in an era where protecting sensitive information is critical, the study’s findings pave the way for more effective encryption techniques that safeguard data across diverse applications—from communication systems to financial transactions. Finally, as cyber threats continually evolve, the optimized chaos-based encryption method proposed here serves as a powerful tool in reducing risks such as unauthorized access, data breaches, and other malicious activities. RESEARCH METHOD The design methodology for a chaos-based encryption system involves a constructive research approach to conceptualize, develop, and implement the encryption technique. It encompasses the systematic identification, selection, processing, and analysis of information pertinent to the research. This methodology serves as a scientific framework for addressing the problem at hand, guiding researchers in their exploration, description, explanation, and prediction of phenomena related to chaos-based encryption. At its core, the research methodology for chaos-based encryption entails studying the methods through which knowledge is acquired and applied in the creation of the encryption system. Its primary objective is to outline a comprehensive work plan for conducting the research, ensuring that each step is carefully designed and executed to achieve meaningful results 2.1 EXPERIMENTAL DATA ANALYSIS The dissertation utilizes several analyses which includes, Intensity histogram analysis, Adjacent pixel autocorrelation test, and key sensitivity tests, to assess the efficiency and robustness of encryption algorithms. The intensity histogram of the original image in Figure 1.0 graphically depicts the spread of pixel intensities. The x-axis represents the spectrum of pixel values, often ranging from 0 to 255 in grayscale images. Lower values indicate darker tones, while higher values indicate brighter tones. The y-axis represents the number or frequency of pixels for each intensity level, indicating the prevalence of certain intensities in the image. Within the framework of an intensity histogram with a y-axis range of 0 to 1400, it indicates that the image exhibits a wide- ranging distribution of pixel intensities, encompassing both dark and brilliant values. The pixel count distribution extends up to 1400 pixels for a specific intensity level. Examining these histograms is advantageous for comprehending the global contrast and tonal attributes of an image, facilitating image processing endeavors such as contrast modifications or discerning distinct features based on intensity patterns. Figure 1.0: Original Image Table 1.0: Histogram analysis of the original image Pixel Value Red Channel Count Green Channel Count Blue Channel Count 0 2081 2380 2408 1 145 169 150 2 136 157 153 3 141 153 153 4 140 148 142 5 113 119 123 6 105 101 99 7 94 118 103 8 143 159 142 9 106 117 124 Figure 1.1: Intensity of the original image (histogram) Figure 1.2: Encrypted Image Table. 1.2: Histogram Analysis Table of the Encrypted Image Pixel Value Red Channel Count Green Channel Count Blue Channel Count 0 2877 1967 1757 1 558 641 423 2 637 664 376 3 708 762 415 4 649 733 376 5 707 840 422 6 737 801 337 7 716 843 416 8 659 856 326 9 670 743 302 Figure 1.3: Intensity of the encrypted image (histogram) The histogram offers a visual depiction of the distribution of pixel values throughout the full intensity spectrum, providing insights on the general brightness and contrast characteristics of the encrypted image. Examining the histogram allows for the identification of patterns, peaks, or fluctuations in pixel intensities, which assists in evaluating the visual attributes of the image and potential qualities affected by the encryption procedure. 2.2: STATISTICAL AND SECURITY ANALYSIS The robustness of the encryption scheme was evaluated using several widely recognized metrics, including the Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal-to-Noise-Ratio (PSNR), and Shannon Entropy. These metrics assess how effectively the encryption disrupts the original image’s structure, how sensitive the encryption is to small changes, and the overall unpredictability of the encrypted image. The encryption achieved an NPCR of 99.814% and a UACI of 33.694%, indicating high resistance to differential attacks. The Shannon Entropy value of 7.998 demonstrates that the cipher image is highly random, minimizing the possibility of leaking any useful information. Additionally, the PSNR of 7.723 indicates the image distortion introduced by encryption is substantial, further enhancing security. This can be seen in Table 4.8 Table 2.0: Statistical and Security Analysis Metrics Metric Value Number of Pixels Change Rate (NPCR) 99.814% Unified Average Changing Intensity (UACI) 33.694% Peak Signal-to-Noise-Ratio (PSNR) 7.723 Shannon Entropy 7.998 3.0: PROPOSED IMPROVED CHAOS-BASED IMAGE ENCRYPTION SYSTEM The system architecture in Figure 3.0 incorporates a comprehensive approach to encryption, leveraging chaos theory and refined hash functions to ensure robust data security. At its core, the architecture comprises a chaos generator for producing high-quality chaotic signals, a mapping function to transform these signals into a suitable domain for cryptographic operations, and a key generation mechanism to generate secure cryptographic keys. Encryption and decryption processes are facilitated through chaos-enhanced operations and the application of the refined hash function, providing an additional layer of security. User interaction is facilitated through a user-friendly interface, while secure key management ensures the protection of encryption keys. Rigorous security measures, including entropy evaluation and cryptographic tests, are integrated into the architecture to uphold data confidentiality and integrity. Overall, the architecture is designed to deliver high-level security, performance, and reliability in encrypting and
