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  • Open Access

    ARTICLE

    A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

    Bing Liu1, Zhe Zhang1, Shengrong Hu2, Song Sun3,*, Dapeng Liu4, Zhenyu Qiu5

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4049-4069, 2024, DOI:10.32604/cmc.2024.048099

    Abstract Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on… More >

  • Open Access

    ARTICLE

    CL2ES-KDBC: A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

    Talal Albalawi, P. Ganeshkumar*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3511-3528, 2024, DOI:10.32604/cmc.2024.046396

    Abstract The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution value precisely. In addition, a… More >

  • Open Access

    ARTICLE

    A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

    Navaneetha Krishnan Muthunambu1, Senthil Prabakaran2, Balasubramanian Prabhu Kavin3, Kishore Senthil Siruvangur4, Kavitha Chinnadurai1, Jehad Ali5,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

    Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >

  • Open Access

    ARTICLE

    A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

    Abdullah Alsaleh1,2,*

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 431-449, 2024, DOI:10.32604/csse.2023.043107

    Abstract With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, and methods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles. To speed… More >

  • Open Access

    ARTICLE

    Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

    Ali Hamid Farea1,*, Omar H. Alhazmi1, Kerem Kucuk2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1525-1545, 2024, DOI:10.32604/cmc.2023.045794

    Abstract While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work. AI2AI employs AI techniques to… More >

  • Open Access

    ARTICLE

    RPL-Based IoT Networks under Decreased Rank Attack: Performance Analysis in Static and Mobile Environments

    Amal Hkiri1,*, Mouna Karmani1, Omar Ben Bahri2, Ahmed Mohammed Murayr2, Fawaz Hassan Alasmari2, Mohsen Machhout1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 227-247, 2024, DOI:10.32604/cmc.2023.047087

    Abstract The RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem. Despite its significance, RPL’s susceptibility to attacks remains a concern. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static and mobile network environments. We employ the Random Direction Mobility Model (RDM) for mobile scenarios within the Cooja simulator. Our systematic evaluation focuses on critical performance metrics, including Packet Delivery Ratio (PDR), Average End to End Delay (AE2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption… More >

  • Open Access

    ARTICLE

    Cybernet Model: A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

    Azar Abid Salih1,*, Maiwan Bahjat Abdulrazaq2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1275-1295, 2024, DOI:10.32604/cmc.2023.046101

    Abstract Cyberspace is extremely dynamic, with new attacks arising daily. Protecting cybersecurity controls is vital for network security. Deep Learning (DL) models find widespread use across various fields, with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and hosts. The capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic patterns. This study presents novel lightweight DL models, known as Cybernet models, for the detection and recognition of various cyber Distributed Denial of Service (DDoS) attacks. These models were constructed to have a reasonable number… More >

  • Open Access

    ARTICLE

    Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

    Chengsheng Yuan1,2, Baojie Cui1,2, Zhili Zhou3, Xinting Li4,*, Qingming Jonathan Wu5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854

    Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

  • Open Access

    ARTICLE

    Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

    Shahad Alzahrani1, Hatim Alsuwat2, Emad Alsuwat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

    Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework utilizes latent variables to quantify… More >

  • Open Access

    REVIEW

    Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

    Jiansheng Peng1,2,*, Jinsong Guo1, Fengbo Bao1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897

    Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of federated learning and multi-robot systems… More >

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