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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Ajith, Vishnu Mathews, Mahima Mary Panchami, V. Computer Science and Engineering Iiit Kottayam Kerala Pala India Cse - Cyber Security Iiit Kottayam Kerala Pala India

ISBN: (纸本)9781665477192

Quantum technology accelerated computing may have the capacity to provide solutions that are much better compared to their best known classical solutions. Quantum Algorithms reducing the complexity of problems like factorisation and searching of unstructured data has triggered a panic mode among cryptographers to analyse the security state of current cryptography schemes. The paper proposes applying Quantum enhanced State Vector Machine to the Frequency Analysis Attack on Ceaser Cipher, the simplest substitution cipher. The method is message length agnostic as it analyzes the frequency of characters and improves in accuracy with increasing message length. Previous attempts at applying QSVM were focused on using the entire ciphertext and required twice as many qubits as the length of plaintext. Our method uses a quantum circuit only as the kernel for the SVM method and can be implemented with only as many classifiers as the size of the alphabet being used in the ciphertext, irrespective of message size. © 2022 IEEE.

关键词： Support vector machines

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Enhanced CNN-DCT Steganography: Deep Learning-Based Image Steganography Over Cloud

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SN computer science 2024年第4期5卷 408页

作者： Ahmad, Shahnawaz Ogala, Justin Onyarin Ikpotokin, Festus Arif, Mohd. Ahmad, Javed Mehfuz, Shabana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida 201310 India Department of Cyber Security University of Delta Agbor Nigeria Department of Computer Science Ambrose Alli University Edo State Ekpoma Nigeria Department of CSE School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida 203201 India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida 201310 India Department of Electrical Engineering Jamia Millia Islamia New Delhi 110025 India

Image steganography plays a pivotal role in secure data communication and confidentiality protection, particularly in cloud-based environments. In this study, we propose a novel hybrid approach, CNN-DCT Steganography, which combines the power of convolutional neural networks (CNNs) and discrete cosine transform (DCT) for efficient and secure data hiding within images over cloud storage. The proposed method capitalizes on the robust feature extraction capabilities of CNNs and the spatial frequency domain transformation of DCT to achieve imperceptible embedding and enhanced data-hiding capacity. In the proposed CNN-DCT Steganography approach, the cover image undergoes a two-step process. First, feature extraction using a deep CNN enables the selection of appropriate regions for data embedding, ensuring minimal visual distortions. Next, the selected regions are subjected to the DCT-based steganography technique, where secret data is seamlessly embedded into the image, rendering it visually indistinguishable from the original. To evaluate the effectiveness of our approach, extensive experiments are conducted using a diverse dataset comprising 500 high-resolution images. Comparative analysis with existing steganography methods demonstrates the superiority of the proposed CNN-DCT Steganography approach. The results showcase higher data hiding capacity, superior visual quality with an MSE of 112.5, steganalysis resistance with a false positive rate of 2.1%, and accurate data retrieval with a bit error rate of 0.028. Furthermore, the proposed method exhibits robustness against common image transformations, ensuring the integrity of the concealed data even under various modifications. Moreover, the computational efficiency of our approach is demonstrated by a competitive execution time of 2.3 s, making it feasible for real-world cloud-based applications. The combination of deep learning techniques and DCT-based steganography ensures a balance between security and visual qual

关键词： Cloud storage CNN Data hiding DCT Deep learning Image steganography Visual fidelity

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Privacy-Aware Artificial Intelligence with Homomorphic Encryption using Machine Learning

Privacy-Aware Artificial Intelligence with Homomorphic Encry...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Srinivasa Rao, B. Chattopadhyay, Saumitra Singh, Prashant Hazela, Bramah Sabarinathan, G. Yamini, Kalva Department of Computer Science and Engineering Bachupally Telangana Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Psna College of Engineering and Technology Department of Mathematics Tamilnadu Dindigul India Sri Ramachandra Institute of Higher Education and Research Cyber Security in Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350333602

Along with the expansion of machine learning (ML) applications, the amount of data required to create predictions increases. Big-data ML has always been limited by off-chip memory capacity and computational speed. Considerably, privacy is one of the limitations of big data, which can be solved by homomorphic encryption (HE). Due to the combination of HE and ML, the multi-party privacy-protected ML suggested in this research may assist numerous users in doing artificial intelligence (AI) without disclosing private data. The technique may train common models in situations of data abuse, particularly in private data protection. The model trained using the ML technique named Artificial Neural Network (ANN) has a similar impact to the model developed using all data on a single computer, according to experiments using the algorithm. The gradient data is simply transmitted by all parties, and homomorphic procedures in the main computing system combine the gradient data. Besides, the optimal key is selected using the significance of the Lion Algorithm (LA). After homomorphic procedures, the learning model is modified depending on the new gradient data. © 2023 IEEE.

关键词： Neural networks

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IDEA-PTLMS: Designing Secure Image Encoding and Decoding Algorithms using Pixel Transposition and Logistic Map based Substitutions

IDEA-PTLMS: Designing Secure Image Encoding and Decoding Alg...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Prajwal N Halvi Prajwalasimha S N Durbadal Chattaraj Naveen Kulkarni Deepthika Karuppusamy Department of Computer Science and Engineering (Cyber Security) School of Engineering (SoE) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In this paper, a hybrid approach based on Pseudo Hadamard transformation and Logistic Substitution is proposed. Inter pixel redundancy and strong correlation between the adjacent pixels in an image is condensed by imperiling it to transformation and chaotic substitution. Pixel value variation is accomplished using modified Pseudo Hadamard chaotic transformation and pixel position variation is made by substitution using S-boxes. Secrete key is used for encryption with 256 bits. S-box is constructed using chaotic logistic map to generate random numbers. Multimedia and biometric images are considered from CVG, University of Granada, Spain for experimental analysis. Two stage fortifications are provided using transformation and substitution. The results obtained for standard images are compared with existing techniques and high degree of security is achieved by this proposed technique.

关键词： Biometrics Ciphers Correlation Redundancy Mean square error methods Multimedia databases Encryption Information and communication technology Standards Logistics

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Universal Watermark Vaccine: Universal Adversarial Perturbations for Watermark Protection

Universal Watermark Vaccine: Universal Adversarial Perturbat...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Chen, Jianbo Liu, Xinwei Liang, Siyuan Jia, Xiaojun Xun, Yuan Hunan University College of Computer Science and Electronic Engineering Hunan China Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350302493

As computing ability continues to rapidly develop, neural networks have found widespread use in various fields. However, in the realm of visible watermarking for image copyright protection, neural networks have made image protection through watermarking less effective. Some research has even shown that watermarks can be removed without damaging to the original image, posing a significant threat to digital copyright protection. In response, the community has introduced adversarial perturbations for watermark protection, but these are sample-specific and time-consuming in real-world scenarios. To address this issue, we propose a new universal adversarial perturbation for watermark removal networks that offers two options. The first option involves adding perturbations to the entire host image, bringing the output of the watermark removal network closer to the original image and providing protection. The second option involves adding perturbations only to the watermark position, reducing the impact of the perturbation on the image and enhancing stealthiness. Our experiments demonstrate that our method effectively resists watermark removal networks and has good generalizability across different images. © 2023 IEEE.

关键词： Copyrights

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ZKCross: An Efficient and Reliable Cross-Chain Authentication Scheme Based on Lightweight Attribute-Based Zero-Knowledge Proof 1

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Cai, Hailang Chen, Jialuo Xiang, Qiao Xu, Jingdong Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China School of Informatics Xiamen University Xiamen China College of Computer Science Nankai University Tianjin China

ISBN: (数字)9789819615513

ISBN: (纸本)9789819615506

The consortium blockchain finds wide application in finance and logistics. However, these independent networks create information silos. Cross-chain authentication aims to bridge these gaps. It verifies qualifications across different blockchains, yet it can risk leaking sensitive data. The main methods include Attribute-Based Signatures (ABS) and Attribute-Based Zero-Knowledge Proof (ABZKP). ABZKP better handles key management, general applicability, and attribute verification than ABS. However, the current ABZKP suffers from large proof files and slow verification. Additionally, many schemes can’t handle sets of multiple attributes and may introduce centralization. Overall, the current schemes based on zero-knowledge proof still struggle to strike a balance between security and efficiency. We propose a reliable cross-chain authentication scheme. Our lightweight zero-knowledge proof algorithm enhances verification speed and reduces file size. We also introduce parallel verification to address security concerns. security analysis and experiments show that ZKCross when properly configured, achieves near-perfect accuracy and outperforms others in size and speed. Thus, ZKCross stands as an efficient and dependable cross-chain authentication scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm 3

Investigation of Red Fox Algorithm for Solving Task Assignme...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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AI-Driven Intelligent IoT Systems for Real-Time Food Quality Monitoring and Analysis 2

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnan, Prabhakar Ebenezar, U. Samson Ranitha, R. Purushotham, N. Balakrishnan, T. Suresh Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University Department of Computer Science and Engineering Mr.palliAP India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring. © 2024 IEEE.

关键词： Artificial Intelligence Food Quality Analysis Intelligent IoT Systems Real-Time Monitoring

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CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification

CHESS: Optimizing LLM Inference via Channel-Wise Thresholdin...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： He, Junhui Wu, Shangyu Wen, Weidong Xue, Chun Jason Li, Qingan Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China School of Computer Science Wuhan University China MBZUAI United Arab Emirates City University of Hong Kong Hong Kong

ISBN: (纸本)9798891761643

Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory *** sparsification can mitigate these resource challenges by reducing the number of activated neurons during *** methods typically employ thresholding-based sparsification based on the statistics of activation ***, they do not model the impact of activation sparsification on performance, resulting in suboptimal performance *** address the limitations, this paper reformulates the activation sparsification problem to explicitly capture the relationship between activation sparsity and model ***, this paper proposes CHESS, a general activation sparsification approach via CHannel-wise thrEsholding and Selective ***, channel-wise thresholding assigns a unique threshold to each activation channel in the feed-forward network (FFN) ***, selective sparsification involves applying thresholding-based activation sparsification to specific layers within the attention ***, we detail the implementation of sparse kernels to accelerate LLM *** results demonstrate that the proposed CHESS achieves lower performance degradation over eight downstream tasks while activating fewer parameters than existing methods, thus speeding up the LLM inference by up to 1.27x. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Assessing the Effects of Blackhole Attacks on MANET Reliability and security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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