Neural Attractor-Based Adaptive Key Generator with DNA-Coded Security and Privacy Framework for Multimedia Data in Cloud Environments

Mahalingam, Hemalatha; Meikandan, Padmapriya Velupillai; Thenmozhi, K.; Moria, Kawthar; Lakshmi, C.; Chidambaram, Nithya; Amirtharajan, Rengarajan

doi:10.3390/math11081769

Cited by 28 publications

(5 citation statements)

References 51 publications

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“…In practical scenarios, attackers might not necessarily aim to decrypt cryptographic systems; instead, they might focus on disrupting the transmission medium to achieve their objectives. Common techniques involve cropping encrypted image transmissions or introducing noise to the ciphertext images to disrupt communication [69][70][71]. To evaluate the resilience of the cryptographic system proposed in this paper against cropping and noise attacks, we conducted the following experiments:…”

Section: Cropping Attack and Noise Attackmentioning

confidence: 99%

A novel image encryption scheme based on ccnn

Zhang,

Sun,

Geng

et al. 2024

Phys. Scr.

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With the advancement of computational capacity, the key space will become one of the crucial factors influencing the security of digital cryptographic systems. Despite chaotic-based digital cryptographic systems possessing large key spaces, the post-Moore's era rapid growth in computational capacity continues to pose challenges to the security of chaotic-based cryptographic systems. To address this issue, a novel image encryption scheme based on non-autonomous chaotic system is presented in this paper. In particular, a brain inspired neuron called continuous-coupled neural network (CCNN) is utilized to design image encryption scheme. To achieve the efficient image encryption scheme, firstly, the CCNN model is simplified to uncoupled-linking neuron model. The dynamic behavior under various driving signals is studied. The analysis showed that uncoupled-linking CCNN neuron exhibit various dynamic behavior under sine waves, triangular waves, sawtooth, superimposed sine waves, etc. Secondly, the decorrelation operation method is utilized to enhance the pseudo-randomness of the sequence. On this basis, thirdly, the image encryption scheme is proposed. It uses bit-level pixel scrambling, row scrambling, column scrambling and diffusion to modify the pixel value and the pixel position of the image. Security analysis shows that the proposed scheme is able to resist differential attack, statistics attack, known-plaintext attack and brute force attack. Moreover, the key space of the proposed scheme can be extended by the combination of drive signals. This unique feature makes the key space of the proposed scheme to be infinite, leading this kind of chaos-based cryptographic system to be a competitive candidate in post-Moore's era.

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Section: Cropping Attack and Noise Attackmentioning

confidence: 99%

A novel image encryption scheme based on ccnn

Zhang,

Sun,

Geng

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…Natural Language Processing (NLP) Natural Language Processing (NLP) serves as a potent tool for generating code from natural language (NL) descriptions [27], [28]. Techniques within NLP, such as parsing, neural language models, sequence-to-sequence models [11], semantic analysis, and machine translation, are harnessed for this purpose.…”

Section: Literature Reviewmentioning

confidence: 99%

Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI

Tembhekar,

Devan,

Jeyaraman

2023

JKLST

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Artificial Intelligence (AI) is a pivotal domain within computer science, profoundly influencing the software development lifecycle, particularly during the implementation phase. Here, developers grapple with the task of translating software requirements and designs into executable code. Automated Code Generation (ACG) leveraging AI emerges as a promising solution in this context. The automation of code generation processes is gaining traction as a means to tackle diverse software development challenges while boosting productivity. This paper presents a thorough review and discourse on both traditional and AI-driven techniques employed in ACG, highlighting their respective challenges and constraints. Through an examination of pertinent literature, we identify various AI methodologies and algorithms utilized in ACG, extracting evaluation metrics such as Accuracy, Efficiency, Scalability, Correctness, Generalization, among others. These metrics serve as the basis for a comparative analysis of AI-driven ACG methods, delving into their applications, strengths, weaknesses, performance metrics, and future prospects.

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“…Consequently, if the Euclidean distance between the newly introduced data and the trained data is similar (termed as a match in the AIS algorithm), it is deemed as "self," representing the normal state. Conversely, if there is dissimilarity, it is categorized as "non-self," indicating an abnormal state [39]. In this study, data failing to match are classified as faulty data.…”

Section: Algorithmmentioning

confidence: 99%

Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging

Katari,

Venkatasubbu,

Krishnamoorthy

2023

JKLST

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Semiconductor manufacturing involves a complex sequence of unit processes, where even a minor error can disrupt the entire production chain. Present-day manufacturing setups rely on continuous data monitoring of equipment health, wafer measurements, and inspections to identify any abnormalities that could impact the quality and performance of the final chip. The primary aim is fault detection and classification (FDC), for which a range of techniques including statistical analysis and machine learning algorithms are commonly employed. In this study, we introduce an innovative approach utilizing an artificial immune system (AIS), inspired by biological mechanisms, for FDC in semiconductor equipment. The main culprits behind process failures are shifts caused by the aging of parts and modules over time. Our methodology integrates state variable identification (SVID) data, reflecting current equipment conditions, and optical emission spectroscopy (OES) data, capturing plasma process information under faulty scenarios induced by deliberate gas flow rate adjustments in semiconductor fabrication. Our results demonstrate a modeling prediction accuracy of 94.69% when incorporating selected SVID and OES data, and 93.68% accuracy using OES data alone. In conclusion, we suggest the potential application of AIS in semiconductor process decision-making, offering promising avenues for enhancing fault detection in semiconductor equipment.

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Neural Attractor-Based Adaptive Key Generator with DNA-Coded Security and Privacy Framework for Multimedia Data in Cloud Environments

Cited by 28 publications

References 51 publications

A novel image encryption scheme based on ccnn

A novel image encryption scheme based on ccnn

Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI

Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging

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