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Enhance cloud security and effectiveness using improved RSA-based RBAC with XACML technique

International Journal of Intelligent Networks 2023年第1期4卷 62-67页

作者： Kousalya, A. Baik, Nam-kyun Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Dept. of Cyber Security College of Science and Technology Duksung Women's University 33 Samyang-ro 144-gil Dobong-gu Seoul Korea Republic of

In the current era, numerous products are accessible online from anywhere and every minute. This work used encryption with access management because authenticities, anonymity, and security over accessibility are mandatory. The proposed design introduced a significantly better encryption strategy to ensure better protection of overall resource admissions. This proposed work uses Improved RSA-based role-based access control (RBAC) with extendable access connectivity markup language (XACML) to encrypt information and maintain privileges. This approach enables storing information within the online computer using cryptographic ideas and information available via a basic admission management mechanism. To ensure the overall protection of sensitive information, the encryption method is employed that merged the conventional homogeneous encryption procedure with unstable information distribution method. This hybrid technique provides user to get advantage from retrieved information in a protected manner. The overall execution of proposed work is considerably quicker than other existing encryption methods. © 2023

关键词： Cryptography

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Optimizing Real-time Task Scheduling in Cloud-based AI Systems using Genetic Algorithms 7

Optimizing Real-time Task Scheduling in Cloud-based AI Syste...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mohammed, Abdul Sajid Mallikarjunaradhya, Vinay Sreeramulu, Madhusudhan Dasari Boddapati, Nagesh Jiwani, Nasmin Natarajan, Yuvaraj University of the Cumberlands Department of School of Computer and Information Sciences KY United States Thomson Reuters Toronto Canada American Express Department of Cyber Security United States Microsoft Department of Computer Science CharlotteNC United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

Artificial Intelligence (AI) has also started to be regularly introduced in cloud-based systems as it can help improve performance and skyrocket efficiency. It is a challenge for such systems to schedule tasks in real time, and computing sources need to be allocated promptly and efficiently. The current scheduling mechanisms being relatively static under varying system conditions leads to inefficient allocation with a slight (to severe) degradation in performance. This project suggests that Genetic Algorithms (GA) be employed for real-time task scheduling optimization in AI services on the cloud. GAs are robust optimization methods based on fundamental natural selection and survival of the fittest principles, thus well suited for solving complex issues like task scheduling in dynamic environments. The LC scheduling uses a GA-based approach to form a group of shuttle schedules (candidate emergency transportation solution) where the fittest one is selected and executed using genetic operators like selection, crossover, mutation, etc. It allows the system to adjust resources more effectively for its current state, leading to better performance and scalability. © 2024 IEEE.

关键词： Genetic algorithms

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InvMetrics: Measuring Privacy Risks for Split Model-Based Customer Behavior Analysis

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4168-4177页

作者： Deng, Ruijun Hu, Shijing Lin, Junxiong Yang, Jirui Lu, Zhihui Wu, Jie Huang, Shih-Chia Duan, Qiang Fudan University Sch. of Comp. Science and the Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China Fudan University School of Computer Science Shanghai200433 China Shanghai Blockchain Engineering Research Center Shanghai200433 China National Taipei University of Technology Department of Electronic Engineering Taipei City10608 Taiwan Pennsylvania State University Information Sciences and Technology Department AbingtonPA19001 United States

Mobile Edge Computing (MEC) has great potential to facilitate cheap and fast customer behavior analysis (CBA). Model splitting, widely adopted in collaborative learning of MEC, partitions the CBA models between the customer device and the edge server in a layer-wise manner to support efficient distributed learning. However, the split-model architecture (SMA) is vulnerable to data reconstruction attacks for privacy leakage in intermediate data, of which the risk measurements remain unexplored. In this paper, we propose a privacy risk measurement framework, called InvMetrics, for split model-based CBA systems, which assess the degree of privacy leakage from both the CBA owners' and the regulators' perspectives. For CBA owners, we propose a privacy metric, Distance Loss (DLoss), based on distance correlation, which is computationally efficient, and thus eligible for being deployed on the customers' devices. For third-party evaluators, we propose Uncertainty Loss (ULoss) based on entropy, which can measure privacy risk without accessing raw data. Evaluation results on three CBA datasets and one image dataset demonstrate that InvMetrics framework with DLoss and ULoss can accurately measure privacy risk and is more efficient than the state-of-the-art. © 1975-2011 IEEE.

关键词： Data privacy

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Phishing Classification Based on Text Content of an Email Body Using Transformers

Phishing Classification Based on Text Content of an Email Bo...

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International Conference on Information security, Privacy, and Digital Forensics, 2022

作者： Somesha, M. Pais, Alwyn R. Department of Computer Science and Engineering Information Security Research Lab National Institute of Technology Karnataka Surathkal575025 India

ISBN: (纸本)9789819950904

Phishing attacks steal sensitive credentials using different techniques, tools, and some sophisticated methods. The techniques include content injection, information re-routing, social engineering, server hacking, social networking, SMS and WhatsApp mobile applications. To overcome such attacks and minimize risks of such attacks, many phishing detection and avoidance techniques were introduced. Among various techniques, deep learning algorithms achieved the efficient results. In the proposed work, a transformers-based technique is used to classify phishing emails. The proposed method outperformed the other similar mechanisms for the classification of phishing emails. The phishing classification accuracy achieved by the proposed work is 99.51% using open-source datasets. The proposed model is also used to learn and validate the correctness of the in-house created datasets. The obtained results with in-house datasets are equally competitive. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic mail

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Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(Information Sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

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Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

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Blockchain-Enabled DNS: Enhancing security and Mitigating Attacks in Domain Name Systems 6

Blockchain-Enabled DNS: Enhancing Security and Mitigating At...

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6th International Conference on Signal Processing and Information security, ICSPIS 2023

作者： Shah, Kaushal Padhya, Mukti Sharma, Sachin Pandit DeenDayal Energy University School of Technology Department of Computer Science and Engineering Gandhinagar India National Forensic Sciences University School of Cyber Security and Digital Forensics Gandhinagar India

ISBN: (纸本)9798350329599

The importance of Internet security is increasing as businesses expand through digitalization. Like in the physical world, data transmitted over the Internet must be safeguarded to ensure confidentiality, integrity, and availability. Among the various security threats, Distributed Denial-of-Service (DDoS) attacks are widely recognized as they can potentially compromise all three aspects of data security. Despite several attempts to address these attacks, the traditional Domain Name System (DNS) has struggled to counter them effectively. In this research, we propose the concept of a Blockchain-based DNS as a solution to mitigate DNS-related threats, particularly DDoS attacks. The suggested architecture encompasses all levels of the DNS, including registrars, intermediaries (if any), and clients. By leveraging the decentralized nature of blockchain technology, this approach provides a fundamental defense against DDoS attacks, as it eliminates the vulnerability of a single point of failure. Furthermore, this work explores the appropriate type of blockchain to be used, estimates the cost of implementing such a service, and offers flexibility for organizations to adopt this solution at their discretion. © 2023 IEEE.

关键词： Blockchain

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security and Privacy Preservation for Future Vehicular Transportation Systems: A Survey 12

Security and Privacy Preservation for Future Vehicular Trans...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Almarshoodi, Abdulazeiz Keenan, James Campbell, Ivan Hassan, Tashfin Ibrahem, Mohamed I. Fouda, Mostafa M. George Mason University Department of Cyber Security Engineering FairfaxVA United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9781665462617

Electric Vehicles (EVs) with numerous optimized features are becoming more popular in today's world of vehicle technology. This is especially in these days when climate change is becoming more of a concern, and the usage of renewable energy is promoted. These EVs require frequent charging for daily use, and the usage of different charging stations would store sensitive data about EVs, e.g., location, driver's license, etc. This leads to significant privacy and security concerns. In this paper, we will investigate the existing solutions proposed in the literature to address these concerns. Some of these solutions are implementing a framework that depends on cryptography and the Matching Market, where cryptography plays a vital role in securing user information privacy. Other solutions include implementing a secure and privacy-preserving physical layer-assisted scheme for charging stations to improve authentication and preserve user privacy. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance the security level for future electric vehicle transportation systems. © 2023 IEEE.

关键词： Electric vehicles

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Enhancing IoT Data security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization 8

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Anuradha, K. Alam, Irfan Nair, Deveendra Menon Narayanan Kiran, Ajmeera Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Telangana India Scai Vit Bhopal University Cyber Security and Digital Forensics Division Kothari Kalan Sehore India Mlr Institute of Technology Department of Computer Science and Engineering Hdyerabad India

ISBN: (纸本)9798350376425

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications. © 2024 IEEE.

关键词： Steganography

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Decrease the Prompt Uncertainty: Adversarial Prompt Learning for Few-shot Text Classification

Decrease the Prompt Uncertainty: Adversarial Prompt Learning...

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2024 IEEE International Conference on Systems, Man, and cybernetics, SMC 2024

作者： Weng, Jinta Zhang, Zhaoguang Yaqi, Jing Niu, Chenxu Huang, Heyan Hu, Yue School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Cyber Engineering Guangzhou University China National Computer Network Emergency Response Technical Team Coordination Center of China China Beijing Institute of Technology School of Computer Science and Technology Beijing China Southeast Academy of Information Technology Putian China

ISBN: (纸本)9781665410205

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs. © 2024 IEEE.

关键词： Adversarial machine learning

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