Hira Akhtar Butt¹, Khoula Said Al Harthy², Mumtaz Ali Shah³, Mudassar Hussain^2,*, Rashid Amin^4,*, Mujeeb Ur Rehman¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3003-3031, 2024, DOI:10.32604/cmc.2024.057185 - 18 November 2024

Abstract Detecting sophisticated cyberattacks, mainly Distributed Denial of Service (DDoS) attacks, with unexpected patterns remains challenging in modern networks. Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking (SDN) environments. While Machine Learning (ML) models can distinguish between benign and malicious traffic, their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining. In this paper, we propose a novel DDoS detection framework that combines Machine Learning (ML) and Ensemble Learning (EL) techniques to improve DDoS attack detection and mitigation in SDN environments. Our model… More >

Wenming Wang^1,2,3,*, Zhiquan Liu¹, Shumin Zhang¹, Guijiang Liu¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2517-2545, 2024, DOI:10.32604/cmc.2024.055184 - 18 November 2024

Abstract Advancements in the vehicular network technology enable real-time interconnection, data sharing, and intelligent cooperative driving among vehicles. However, malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users. Trust mechanisms serve as an effective solution to this issue. In recent years, many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes, incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area. In this paper, we propose a distributed vehicular network scheme based on trust scores. Specifically, the designed architecture partitions multiple More >

Shen Su^1,*, Daojing He², Neeraj Kumar³

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1933-1937, 2024, DOI:10.32604/cmes.2024.059318 - 31 October 2024

Abstract This article has no abstract. More >

Yuanjing Luo, Xichen Tan, Zhiping Cai^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 133-160, 2024, DOI:10.32604/cmc.2024.055150 - 15 October 2024

Abstract In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces, resilient to various attacks. These methods, characterized by a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analysis of deep watermarking’s core technologies, current status, and prospective More >

Xin Cong, Lanjin Feng^*, Lingling Zi

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 663-684, 2024, DOI:10.32604/cmc.2024.054372 - 15 October 2024

Abstract In the digital information age, distributed file storage technologies like the InterPlanetary File System (IPFS) have gained considerable traction as a means of storing and disseminating media content. Despite the advantages of decentralized storage, the proliferation of decentralized technologies has highlighted the need to address the issue of file ownership. The aim of this paper is to address the critical issues of source verification and digital copyright protection for IPFS image files. To this end, an innovative approach is proposed that integrates blockchain, digital signature, and blind watermarking. Blockchain technology functions as a decentralized and… More >

Cong Shen^1,*, Wei Zhang^1,2,*, Tanping Zhou^1,2, Yiming Zhang¹, Lingling Zhang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Zaharaddeen Karami Lawal^3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3917-3943, 2024, DOI:10.32604/cmc.2024.054150 - 12 September 2024

Abstract The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems (CPSs) seamlessly integrate physical processes with advanced digital technologies. However, as industries become increasingly interconnected and reliant on smart digital technologies, the intersection of physical and cyber domains introduces novel security considerations, endangering the entire industrial ecosystem. The transition towards a more cooperative setting, including humans and machines in Industry 5.0, together with the growing intricacy and interconnection of CPSs, presents distinct and diverse security and privacy challenges. In this regard, this study provides a comprehensive review of security and privacy concerns pertaining… More >

Linghong Kuang^1,2, Wenlong Shi^1,2, Jing Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3193-3219, 2024, DOI:10.32604/cmc.2024.054377 - 15 August 2024

Abstract The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing… More >

Naglaa Salem El‑Sayed, Mohamed Hasanin, Samir Kamel^*

Journal of Renewable Materials, Vol.12, No.4, pp. 699-720, 2024, DOI:10.32604/jrm.2024.049118 - 12 June 2024

Abstract In recent decades, the ozone layer has suffered considerable damage, increasing the entry of ultraviolet (UV) light into the atmosphere and reaching the earth’s surface, negatively affecting life. Accordingly, researchers aimed to solve this problem by synthesizing advanced UV-shielding materials. On the other hand, developing an easy and green strategy to prepare functional materials with outstanding properties based on naturally abundant and environmentally friendly raw materials is highly desirable for sustainable development. Because biomass-derived materials are sustainable and biodegradable, they present a promising substitute for petroleum-based polymers. The three main structural constituents of the plant More > Graphic Abstract

CARLOS ALBERTO-SILVA^*, BRENDA RUFINO DA SILVA

BIOCELL, Vol.48, No.6, pp. 897-904, 2024, DOI:10.32604/biocell.2024.050443 - 10 June 2024

Abstract Venom snake-derived peptides have multiple biochemical, pharmacological, and toxicological profiles, allowing for the discovery of new medicinal products and therapeutic applications. This review specifically examines the fundamental elements of neuroprotection offered by different oligopeptides derived from snake venom. It also includes a brief evaluation of short peptides that are being considered as potential therapeutic agents. Proline-rich peptides and tryptophyllin family peptides isolated from the crude venom of Viperidae family snakes, specifically Bothrops atrox, Bothrops jararaca, and Bothrops moojeni, have been shown to have pro-survival properties, the ability to reduce oxidative stress, and the ability to promote cell viability More >