Article
Computer Science, Information Systems
Jinkun You, Yuan-Gen Wang, Guopu Zhu, Ligang Wu, Hongli Zhang, Sam Kwong
Summary: This paper proposes an equivalent keys (EK)-based estimator for estimating the secret key in both traditional and more secure spread spectrum (SS) watermarking methods. The proposed estimator selects equivalent keys by adding up uniformly sampled equivalent keys from the equivalent region. The experimental results validate the theoretical analysis and demonstrate the superiority of the proposed estimator over existing methods. Moreover, this paper reveals the insecurity of the more secure SS watermarking methods in the known-message attack (KMA) scenario for the first time.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Wenjia Ding, Huyin Zhang, Ralf Reulke, Yulin Wang
Summary: This paper proposes a scalable difference expansion based data hiding technology that uses super pixels instead of single pixels as the basic carrier and adjusts the size and differential magnification to control the hiding capacity and fidelity of stego image. Experimental results verify the feasibility of the method and its expected performance, and the size and shape of super pixels provide security for data hiding to some extent. Moreover, the proposed algorithm can be applied to real-time scenarios as it does not involve complex operations.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Information Systems
Chia-Chen Lin, Ting-Lin Lee, Ya-Fen Chang, Pei-Feng Shiu, Bohan Zhang
Summary: Digital images have unique features that make them easily transmittable and easy to tamper. Image authentication has become more crucial with the advancement of digital processing techniques and the increasing transmission of valuable digital images over the Internet. This paper presents an image authentication scheme using fragile watermarking, which can localize tampering and recover the image. The proposed scheme can authenticate the image, locate modifications, verify integrity, and even reconstruct tampered regions without accessing the original image.
Article
Optics
Jinfen Liu, Le Wang, Shengmei Zhao
Summary: This paper proposes a novel secret sharing scheme based on spread spectrum ghost imaging (SSGI), which enhances the data security capacity of optical information encryption by splitting the key into multiple parts and sharing them among participants.
Article
Chemistry, Multidisciplinary
Marco Botta, Davide Cavagnino
Summary: This paper investigates the potential of embedding extra information in printable string encodings, such as Base45 and Base85. A framework for reversibly embedding data in these encodings is described, leveraging their non-surjective characteristics. The expected payload can be easily estimated assuming uniformly distributed binary input data.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Guangyong Gao, Tingting Han, Zhao Feng, Min Wang, Zhihua Xia
Summary: This article proposes a data authentication scheme based on a double watermark for data security in wireless sensor networks (WSNs). The double watermark consists of a reversible watermark and an irreversible watermark. The scheme embeds the reversible watermark into the effective precision bit of data generated by group head data, and embeds the irreversible watermark into the flag bit using a defined array and the effective precision bit. The comprehensive performance of the proposed scheme outperforms that of existing schemes, as demonstrated by security analysis and experimental results.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
B. Sridhar, V. Syambabu
Summary: This paper presents a non-blind video watermarking technique that reshapes watermark images, decomposes and recombines the luminance band of frames to achieve copyright marking. The main objective is to design and develop gatefold-based video authentication approaches to increase robustness, payload, and minimize the bit error rate.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Gaoyang Liu, Tianlong Xu, Xiaoqiang Ma, Chen Wang
Summary: This paper presents a novel framework called MeFA for detecting training data IP embezzlement. MeFA extracts fingerprints of the target data using membership inference techniques and constructs an authentication model to verify data ownership. It can also serve as a post-protection method for verifying the ownership of ML models. Extensive experiments validate the effectiveness and robustness of MeFA.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Information Systems
A. Hernandez-Joaquin, G. Melendez-Melendez, R. Cumplido
Summary: This paper introduces a dual watermarking scheme for image authentication and copyright protection. Two watermarks, a fragile watermark and a robust watermark, are embedded into the host image using the discrete wavelet transform (DWT). The proposed scheme achieves satisfactory watermark imperceptibility levels and high watermark robustness against image processing attacks. It also achieves high authentication accuracy against tampering attacks, outperforming similar state-of-the-art dual watermarking methods in terms of watermark robustness and authentication accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shehu Ayuba, Wan Mohd Nazmee Wan Zainon
Summary: Medical images are essential for providing diagnosis and treatment to patients. Watermarking is an important data security approach for protecting medical images against unauthorized use, ensuring confidentiality, authentication, and integrity verification. Constant updates on trends, issues, and challenges in medical image watermarking are necessary for research and development in this field.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2023)
Article
Computer Science, Information Systems
Adnan Abdul-Aziz Gutub, Khaled Aydh Alaseri
Summary: This study introduces a new method of hiding secret information in Arabic text using improved Arabic text steganography, which can be applied in counting-based secret sharing technique to hide secret shares in a non-memorizable way. The research tests proposed modifications to original Arabic text steganography and explores the potential of using this technique for secure information sharing.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jun Ye, Xinhui Cao, Zhen Guo
Summary: In the field of marine communication, the development of new-generation information technology such as the Internet of Things, big data, and blockchain has brought opportunities for marine information security. However, open marine communication networks and vulnerable marine equipment pose threats to system security. We propose a multitarget authentication and key exchange protocol to address these challenges.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Lin Zhou, Xiaohui Yuan, Yuanyuan Liu, Zhanlong Chen, Peng Xie
Summary: This study proposes a novel watermarking method for map legends to protect copyright. The watermark bit embedding positions for each data type are defined based on data types and just noticeable distortion tolerance. Text watermark is used to reduce the number of watermark bits, and map legends are divided into groups to contain multiple watermarks. A watermark bit recovery method is designed to fix damaged watermark bits, ensuring greater robustness. Experimental results demonstrate the expected goal achievement under various types of attacks and image operations.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Telecommunications
Poonam Kadian, Shiafali M. Arora, Nidhi Arora
Summary: This paper presents key paradigms of research in robust watermarking techniques, focusing on imperceptibility, security, and robustness. It also discusses the main content of robust watermarking schemes in the transform domain, particularly the application of frequency transformation techniques.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Multidisciplinary Sciences
Danda Prudhvi Krishna, R. Ramaguru, K. Praveen, M. Sethumadhavan, Kattur Soundarapandian Ravichandran, Raghunathan Krishankumar, Amir H. Gandomi
Summary: This paper proposes a distributed authentication and authorization framework using a secret-sharing mechanism, blockchain-based decentralized identifier, and interplanetary file system. Performance analysis shows that secret sharing-based authentication is fast. Security analysis demonstrates that the model is robust, end-to-end secure, and compliant with the Universal Composability Framework.
SCIENTIFIC REPORTS
(2023)
Article
Engineering, Electrical & Electronic
Ghani Ur Rehman, Anwar Ghani, Muhammad Zubair, Muhammad Imran Saeed, Dhananjay Singh
Summary: Smart and connected communities (SCC) is an emerging field of internet of things (IoT) that has the potential to improve human life in terms of preservation, revitalization, livability, and sustainability of a community. To encourage cooperation and discourage selfish behavior, a novel mechanism called socially omitting selfishness (SOS) has been proposed, which utilizes a socially oriented election process and an extended version of the Dempster Shafer model.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Nidal Nasser, Qazi Emad-ul-Haq, Muhammad Imran, Asmaa Ali, Imran Razzak, Abdulaziz Al-Helali
Summary: This study proposes an intelligent healthcare system based on IoT-cloud technologies for real-time patient tracking and reliable COVID-19 detection. By utilizing deep learning algorithms and state-of-the-art classification techniques, the system achieves high accuracy and effectiveness in processing CT scan images.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Hamra Afzaal, Muhammad Imran, Muhammad Umar Janjua, Sarada Prasad Gochhayat
Summary: This paper proposes a secure and trustworthy blockchain-based crowdsourcing consensus protocol, which is verified for correctness through model checking and formal methods to prevent security attacks.
Article
Computer Science, Artificial Intelligence
Zainab Ayaz, Saeeda Naz, Naila Habib Khan, Imran Razzak, Muhammad Imran
Summary: The recent advancements in information technology and bioinformatics have significantly contributed to medical sciences. Artificial intelligence has been widely utilized in the diagnosis of Parkinson's disease, with promising results achieved using different datasets and techniques.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Basit Shahzad, Wardah Naeem Awan, Fazal-E-Amin, Ahsanullah Abro, Muhammad Shoaib, Sultan Alyahya
Summary: This study investigates the challenges and mitigation strategies of using Scrum in Global Software Development (GSD) through a systematic literature review and an industrial survey. The findings are consolidated into a research framework that is validated by experts and deemed helpful in addressing the identified challenges in distributed Scrum.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Zijie Ren, Jianhua Shi, Muhammad Imran
Summary: Product lifecycle management is an effective method for enhancing the market competitiveness of modern manufacturing industries. The digital twin, with its integration of physics and information systems, provides a technical means to integrate multisource information and overcome communication barriers in the lifecycle. However, there is a lack of focus on twin data and its evolution mechanisms, limiting the full potential of digital twin technology in global data resource management.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Muhammad Nouman, Umar Qasim, Hina Nasir, Abdullah Almasoud, Muhammad Imran, Nadeem Javaid
Summary: In this study, blockchain is used to register nodes and address security issues at Base Stations and Cluster Heads. A Machine Learning classifier called HGB is used to classify nodes as malicious or legitimate. Malicious nodes are revoked from the network, while legitimate nodes have their data stored in IPFS with their hashes stored in blockchain. Performance evaluation shows that HGB outperforms other classifiers and VBFT performs better than PoW. Furthermore, the proposed model efficiently detects malicious nodes and ensures secure data storage.
Article
Automation & Control Systems
Yanping Wang, Xiaofen Wang, Hong-Ning Dai, Xiaosong Zhang, Muhammad Imran
Summary: Intelligent Transport Systems (ITS) have attracted attention due to advances in the Industrial Internet of Vehicles (IIoV). However, existing data reporting protocols for ITS have limitations in terms of storage, computation costs, and revocation of malicious users. This paper proposes a novel data reporting protocol for edge-assisted ITS that addresses these issues, achieving better performance and security.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Green & Sustainable Science & Technology
Arif Hussain Magsi, Leanna Vidya Yovita, Ali Ghulam, Ghulam Muhammad, Zulfiqar Ali
Summary: A threshold-based content caching mechanism is proposed to detect and prevent content poisoning attacks, along with the integration of a blockchain system for privacy protection and network extension. Experimental results show that the mechanism achieves a 100% accuracy in identifying and preventing attackers, while effectively filtering out malicious blocks.
Article
Chemistry, Multidisciplinary
Roopdeep Kaur, Gour Karmakar, Muhammad Imran
Summary: This paper investigates the importance of denoising in digital image processing and compares the performance of traditional and embedded denoising methods. The experimental results show that traditional denoising methods have better accuracy, while embedded denoising methods have lower computational time.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Sajid Ali, Omar Abusabha, Farman Ali, Muhammad Imran, Tamer Abuhmed
Summary: Despite the increasing threat of IoT-specific malware, assessing IoT systems' security and developing mitigation measures are critical. This study proposes a multitask DL model using LSTM for detecting IoT malware, achieving high accuracy in tasks of determining benign/malicious traffic and identifying malware types. Traffic data from 18 IoT devices were used for training and feature selection enhanced the model's performance.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2023)
Article
Mathematics
Iftikhar Ahmad, Muhammad Imran, Abdul Qayyum, Muhammad Sher Ramzan, Madini O. Alassafi
Summary: This research proposes a new hybrid deep learning intrusion detection model called HD-IDM, which combines GRU and LSTM classifiers for analyzing network traffic. HD-IDM achieves remarkable performance on multiple datasets, with outstanding accuracy and precision for classification tasks. However, it has limitations such as the need for labeled data and potential challenges in handling new intrusion methods.
Article
Computer Science, Information Systems
Safia Amir Dahri, Muhammad Mujtaba Shaikh, Musaed Alhussein, Muhammad Afzal Soomro, Khursheed Aurangzeb, Muhammad Imran
Summary: The coverage and capacity required for 5G and beyond can be achieved using heterogeneous wireless networks. This study explores various factors such as distance, line of sight, idle mode capability, and path loss models to improve the performance of 5G networks. The installation of directional antennas at macro base stations and omnidirectional antennas at pico base stations significantly improves coverage and area spectral efficiency.
Article
Computer Science, Information Systems
Zahoor Ali Khan, Sana Amjad, Farwa Ahmed, Abdullah M. Almasoud, Muhammad Imran, Nadeem Javaid
Summary: Wireless sensor networks (WSNs) have gained great importance in various domains like healthcare, military, and social products, but they are vulnerable to attacks due to their heterogeneous nature. This research proposes a network with deep learning (DL) techniques and a blockchain-based real-time message content validation (RMCV) scheme to detect malicious nodes (MNs) in WSNs. DL models are trained on a dataset generated from the routing protocols, and a decentralized blockchain is deployed on cluster heads (CHs) and the base station (BS) to overcome single point of failure. RMCV and DL techniques are used to remove MNs, and legitimate nodes (LNs) are registered using a proof-of-authority consensus protocol. The routing results are compared with original protocols, and the accuracy of different DL techniques is evaluated. Oyente is used for formal security analysis of the smart contract, proving the resilience of the blockchain network against vulnerabilities.
Article
Engineering, Multidisciplinary
Amjad Ali, Khursheed Aurangzeb, Muhammad Shoaib, Musaed Alhussein, Muhammad Zeeshan Malik
Summary: This paper examines the irreversibilities of the Al2O3-Cu/water hybrid nanofluid in a PVT solar collector. The results show that the double serpentine (DS) channel has lower frictional and thermal entropy generation rates compared to the single serpentine (SS) channel. Additionally, nanoparticle concentration and fluid velocity also affect the entropy generation rates.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
(2023)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)