Liying Zhang¹, Xin Gu¹, Qiang Zhao²

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-7, 2024, DOI:10.23967/j.rimni.2024.05.001 - 17 May 2024

Abstract In the burgeoning epoch of big data, the imperative for secure computer network data storage is confronted with formidable challenges, including the perils of data breaches and a paucity of robust security measures. An enhanced storage paradigm, predicated upon a refined Hash algorithm—termed H-AONT—is herein delineated. This methodology augments data storage security through the formulation of an apposite system model, the amalgamation of the merits inherent in conventional encryption algorithms, and the deployment of the H-AONT dual encryption algorithm in data processing. Empirical evidence substantiates that, vis-à-vis alternative approaches, the proposed method significantly bolsters data 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 >

Muhammad Dawood¹, Shanshan Tu¹, Chuangbai Xiao¹, Muhammad Haris², Hisham Alasmary³, Muhammad Waqas^4,5,*, Sadaqat Ur Rehman⁶

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4513-4542, 2024, DOI:10.32604/cmc.2024.050049 - 12 September 2024

Abstract The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow More >

Iuon-Chang Lin¹, I-Ling Yeh¹, Ching-Chun Chang², Jui-Chuan Liu³, Chin-Chen Chang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 809-822, 2024, DOI:10.32604/cmes.2024.051612 - 20 August 2024

Abstract Centralized storage and identity identification methods pose many risks, including hacker attacks, data misuse, and single points of failure. Additionally, existing centralized identity management methods face interoperability issues and rely on a single identity provider, leaving users without control over their identities. Therefore, this paper proposes a mechanism for identity identification and data sharing based on decentralized identifiers. The scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data integrity. Data is stored on InterPlanetary File System (IPFS) to avoid the risk of single More >

Waseem Akram¹, Khalid Mahmood², Hafiz Burhan ul Haq³, Muhammad Asif³, Shehzad Ashraf Chaudhry^4,5, Taeshik Shon^6,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 263-280, 2024, DOI:10.32604/cmc.2024.052405 - 18 July 2024

Abstract Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol (HTTP) requests and maintains the states of clients’ information over the Internet. HTTP cookies are exploited to carry client patterns observed by a website. These client patterns facilitate the particular client’s future visit to the corresponding website. However, security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the browser. Several protocols have been introduced that maintain HTTP cookies, but many of those fail to achieve More >

Marya Iqbal¹, Yaser Hafeez¹, Nabil Almashfi², Amjad Alsirhani³, Faeiz Alserhani⁴, Sadia Ali¹, Mamoona Humayun^5,*, Muhammad Jamal⁶

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5031-5049, 2024, DOI:10.32604/cmc.2024.051371 - 20 June 2024

Abstract Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, More >

Zhenjiang Dong¹, Xin Ge¹, Yuehua Huang¹, Jiankuo Dong¹, Jiang Xu^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4021-4044, 2024, DOI:10.32604/cmc.2024.049233 - 20 June 2024

Abstract This paper presents a comprehensive exploration into the integration of Internet of Things (IoT), big data analysis, cloud computing, and Artificial Intelligence (AI), which has led to an unprecedented era of connectivity. We delve into the emerging trend of machine learning on embedded devices, enabling tasks in resource-limited environments. However, the widespread adoption of machine learning raises significant privacy concerns, necessitating the development of privacy-preserving techniques. One such technique, secure multi-party computation (MPC), allows collaborative computations without exposing private inputs. Despite its potential, complex protocols and communication interactions hinder performance, especially on resource-constrained devices. Efforts… More >

Ibraheem Al-Hejri¹, Farag Azzedin^1,*, Sultan Almuhammadi¹, Naeem Firdous Syed²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.047117 - 20 June 2024

Abstract The use of the Internet of Things (IoT) is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices. In critical infrastructure domains like oil and gas supply, intelligent transportation, power grids, and autonomous agriculture, it is essential to guarantee the confidentiality, integrity, and authenticity of data collected and exchanged. However, the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques. Consequently, designing a lightweight secure More >

Tao Bao^*, Xiyuan Ma, Zhuohuan Li, Duotong Yang, Pengyu Wang, Changcheng Zhou

Energy Engineering, Vol.121, No.6, pp. 1713-1737, 2024, DOI:10.32604/ee.2023.046150 - 21 May 2024

Abstract The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able More >

Qiong Yang¹, Li Ma^1,2,*, Shanshan Tu¹, Sami Ullah³, Muhammad Waqas^4,5, Hisham Alasmary⁶

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2035-2062, 2024, DOI:10.32604/cmc.2024.049970 - 15 May 2024

Abstract Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and More >