Huang Zeng, Mingtian Zhang, Tengfei Liu, Anjia Yang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5125-5142, 2024, DOI:10.32604/cmc.2024.052846

Abstract Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus… More >

Jane Kim¹, Soojin Lee¹, Chan Yeob Yeun², Seung-Hyun Seo^1,3,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3495-3518, 2024, DOI:10.32604/cmc.2024.050461

Abstract As big data, Artificial Intelligence, and Vehicle-to-Everything (V2X) communication have advanced, Intelligent Transportation Systems (ITS) are being developed to enable efficient and safe transportation systems. Electronic Toll Collection (ETC), which is one of the services included in ITS systems, is an automated system that allows vehicles to pass through toll plazas without stopping for manual payment. The ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system flow. To ensure secure and efficient toll payments, designing a distributed model for sharing toll payment information among untrusted toll service… More >

Chunhui Li^1,*, Hua Jiang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431

Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in… More >

Sohaib Latif^1,*, M. Saad Bin Ilyas¹, Azhar Imran², Hamad Ali Abosaq³, Abdulaziz Alzubaidi⁴, Vincent Karovič Jr.⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

Zhenxiang He^*, Zhenyu Zhao, Ke Chen, Yanlin Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281

Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for… 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

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 >

Muhammad Ahmad Nawaz Ul Ghani¹, Kun She^1,*, Muhammad Arslan Rauf¹, Shumaila Khan², Javed Ali Khan³, Eman Abdullah Aldakheel⁴, Doaa Sami Khafaga⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611

Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

Xingfan Zhao¹, Changgen Peng^1,2,*, Weijie Tan², Kun Niu¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 307-328, 2024, DOI:10.32604/cmc.2024.047975

Abstract Traditional blockchain key management schemes store private keys in the same location, which can easily lead to security issues such as a single point of failure. Therefore, decentralized threshold key management schemes have become a research focus for blockchain private key protection. The security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset security. The threshold blockchain private key management schemes based on verifiable secret sharing have made some progress, but these schemes do not consider participants’ self-interested behavior, and require trusted nodes to keep private key… More >

Muhammad Umar Nasir¹, Omar Kassem Khalil², Karamath Ateeq³, Bassam SaleemAllah Almogadwy⁴, M. A. Khan⁵, Khan Muhammad Adnan^6,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 963-981, 2024, DOI:10.32604/cmc.2024.047874

Abstract Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train… More >

Xiaohui Yang, Peiyin Zhao^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1521-1538, 2024, DOI:10.32604/cmc.2024.047666

Abstract Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer from privacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharing scheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computational overhead of encryption and decryption reaches a lightweight constant level, and supports keyword search and policy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technology is utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the More >