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  • Open Access

    ARTICLE

    Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

    Abdullah Alabdulatif1, Navod Neranjan Thilakarathne2,*, Mohamed Aashiq3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610

    Abstract The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for… More >

  • Open Access

    ARTICLE

    Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of Things

    Zhaobin Li*, Xiantao Liu*, Nan Zhang, Zhanzhen Wei

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3835-3854, 2024, DOI:10.32604/cmc.2024.053950

    Abstract The Industrial Internet of Things (IIoT) consists of massive devices in different management domains, and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange. To address the above challenges, a viable solution that combines Certificateless Public Key Cryptography (CL-PKC) with blockchain technology can be utilized. However, as many existing schemes rely on a single Key Generation Center (KGC), they are prone to problems such as single points of failure and high computational overhead. In this case, this paper proposes a novel blockchain-based certificateless cross-domain authentication… More >

  • Open Access

    ARTICLE

    Genome-Wide Identification and Expression Analysis of the GSK3 Gene Family in Sunflower under Various Abiotic Stresses

    Xianwen Ji#, Ziying Jiang#, Jichao Wang, Lili Dong, Xinyi Deng*

    Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 1839-1850, 2024, DOI:10.32604/phyton.2024.052809

    Abstract Genes in the glycogen synthase kinase 3 (GSK3) family are essential in regulating plant response to stressful conditions. This study employed bioinformatics to uncover the GSK3 gene family from the sunflower genome database. The expressions of GSK3 genes in different tissues and stress treatments, such as salt, drought, and cold, were assessed using transcriptome sequencing and quantitative real-time PCR (qRT-PCR). The study results revealed that the 12 GSK3 genes of sunflower, belonging to four classes (Classes I–IV), contained the GSK3 kinase domain and 11–13 exons. The majority of GSK3 genes were highly expressed in the leaf axil and… More >

  • Open Access

    ARTICLE

    Mean Field-Based Dynamic Backoff Optimization for MIMO-Enabled Grant-Free NOMA in Massive IoT Networks

    Haibo Wang1, Hongwei Gao1,*, Pai Jiang1, Matthieu De Mari2, Panzer Gu3, Yinsheng Liu1

    Journal on Internet of Things, Vol.6, pp. 17-41, 2024, DOI:10.32604/jiot.2024.054791

    Abstract In the 6G Internet of Things (IoT) paradigm, unprecedented challenges will be raised to provide massive connectivity, ultra-low latency, and energy efficiency for ultra-dense IoT devices. To address these challenges, we explore the non-orthogonal multiple access (NOMA) based grant-free random access (GFRA) schemes in the cellular uplink to support massive IoT devices with high spectrum efficiency and low access latency. In particular, we focus on optimizing the backoff strategy of each device when transmitting time-sensitive data samples to a multiple-input multiple-output (MIMO)-enabled base station subject to energy constraints. To cope with the dynamic varied channel… More >

  • Open Access

    ARTICLE

    Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks

    Muaadh A. Alsoufi1,*, Maheyzah Md Siraj1, Fuad A. Ghaleb2, Muna Al-Razgan3, Mahfoudh Saeed Al-Asaly3, Taha Alfakih3, Faisal Saeed2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 823-845, 2024, DOI:10.32604/cmes.2024.052112

    Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >

  • Open Access

    ARTICLE

    Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

    Min-Gyu Kim1, Hwankuk Kim2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221

    Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

  • Open Access

    ARTICLE

    PARE: Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

    Peicong He, Yang Xin*, Yixian Yang

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777

    Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

  • Open Access

    ARTICLE

    Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

    Zaed Mahdi1,*, Nada Abdalhussien2, Naba Mahmood1, Rana Zaki3,*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2139-2159, 2024, DOI:10.32604/cmc.2024.053542

    Abstract The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things (IoT) networks, as devices can be monitored or service isolated from them and affect users in one way or another. Securing Internet of Things networks is an important matter, as it requires the use of modern technologies and methods, and real and up-to-date data to design and train systems… More >

  • Open Access

    ARTICLE

    Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

    Uddagiri Sirisha1,, Parvathaneni Naga Srinivasu2,3,*, Panguluri Padmavathi4, Seongki Kim5,, Aruna Pavate6, Jana Shafi7, Muhammad Fazal Ijaz8,*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2301-2330, 2024, DOI:10.32604/cmc.2024.053132

    Abstract Fetal health care is vital in ensuring the health of pregnant women and the fetus. Regular check-ups need to be taken by the mother to determine the status of the fetus’ growth and identify any potential problems. To know the status of the fetus, doctors monitor blood reports, Ultrasounds, cardiotocography (CTG) data, etc. Still, in this research, we have considered CTG data, which provides information on heart rate and uterine contractions during pregnancy. Several researchers have proposed various methods for classifying the status of fetus growth. Manual processing of CTG data is time-consuming and unreliable.… More >

  • Open Access

    REVIEW

    Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions, and Future Directions

    Ahmad Rahdari1,6, Ahmad Jalili2, Mehdi Esnaashari3, Mehdi Gheisari1,4,7,8,*, Alisa A. Vorobeva5, Zhaoxi Fang1, Panjun Sun1,*, Viktoriia M. Korzhuk5, Ilya Popov5, Zongda Wu1, Hamid Tahaei1

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.052994

    Abstract Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes.… More >

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