Zhihua Liu, Shengquan Liu^*, Jian Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for… More >

Senshan Ouyang^1,2, Xiang Liu², Lei Liu², Shangchao Wang², Baichuan Shao³, Yang Zhao^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 903-915, 2024, DOI:10.32604/cmes.2023.028895

Abstract With the continuous expansion of the Industrial Internet of Things (IIoT), more and more organisations are placing large amounts of data in the cloud to reduce overheads. However, the channel between cloud servers and smart equipment is not trustworthy, so the issue of data authenticity needs to be addressed. The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems. Unfortunately, it still suffers from the problem of key exposure. In order to address this concern, this study first introduces a key-insulated scheme, SM2-KI-SIGN, based on the SM2 algorithm. This scheme boasts strong key insulation… More >

Anıl Sezgin^1,2,*, Aytuğ Boyacı³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2121-2143, 2023, DOI:10.32604/cmc.2023.040287

Abstract By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known as AID4I, makes use of… More >

Dae-Young Kim¹, SoYeon Lee², MinSeung Kim², Seokhoon Kim^1,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2049-2060, 2023, DOI:10.32604/csse.2023.040473

Abstract In many IIoT architectures, various devices connect to the edge cloud via gateway systems. For data processing, numerous data are delivered to the edge cloud. Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency. There are two types of costs for this kind of IoT network: a communication cost and a computing cost. For service efficiency, the communication cost of data transmission should be minimized, and the computing cost in the edge cloud should be also minimized. Therefore, in this paper, the communication cost for data transmission is defined as the delay factor, and the… More >

Jiale Cheng¹, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.3, pp. 183-195, 2022, DOI:10.32604/jiot.2022.042054

Abstract A novel Federated learning classifier is proposed using the Dempster-Shafer (DS) theory for the set-valued classification of industrial IoT Distributed Denial of Service (DDoS) attack traffic. The proposed classifier, referred to as the evidence-based federated learning classifier, employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service (DDoS) traffic from the local data of private industrial clients. The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer, situated on the federated server. Lastly, the set value classification task of attack mode is executed in the… More >

Muhammad Saad¹, Maaz Bin Ahmad^1,*, Muhammad Asif², Muhammad Khalid Khan¹, Toqeer Mahmood³, Elsayed Tag Eldin^4,*, Hala Abdel Hameed^5,6

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 687-709, 2023, DOI:10.32604/cmc.2023.038335

Abstract Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact tracing and quarantine management solution… More >

Noura Alenezi, Ahamed Aljuhani^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2899-2915, 2023, DOI:10.32604/csse.2023.036657

Abstract The rapid growth of the Internet of Things (IoT) in the industrial sector has given rise to a new term: the Industrial Internet of Things (IIoT). The IIoT is a collection of devices, apps, and services that connect physical and virtual worlds to create smart, cost-effective, and scalable systems. Although the IIoT has been implemented and incorporated into a wide range of industrial control systems, maintaining its security and privacy remains a significant concern. In the IIoT contexts, an intrusion detection system (IDS) can be an effective security solution for ensuring data confidentiality, integrity, and availability. In this paper, we… More >

Khaled M. Alalayah¹, Fatma S. Alrayes², Jaber S. Alzahrani³, Khadija M. Alaidarous¹, Ibrahim M. Alwayle¹, Heba Mohsen⁴, Ibrahim Abdulrab Ahmed⁵, Mesfer Al Duhayyim^6,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3121-3139, 2023, DOI:10.32604/csse.2023.036352

Abstract With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL)… More >

Ming Cheng¹, Shiming Li^1,3,*, Yuhe Wang¹, Guohui Zhou¹, Peng Han¹, Yan Zhao²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2527-2555, 2023, DOI:10.32604/cmc.2023.036427

Abstract To address the problem of network security situation assessment in the Industrial Internet, this paper adopts the evidential reasoning (ER)algorithm and belief rule base (BRB) method to establish an assessment model. First, this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge. Second, the evaluation indicators are fused with expert knowledge and the ER algorithm. According to the fusion results, a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established, and the projection covariance matrix adaptive evolution… More >

Zeyong Sun¹, Guo Ran², Zilong Jin^1,3,*

Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416

Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve… More >