Sultan Alkhliwi^*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4735-4752, 2023, DOI:10.32604/cmc.2023.037141

Abstract Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be enhanced with no requirement for… More >

Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 265-298, 2023, DOI:10.32604/cmes.2023.027581

Abstract Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion detection systems (IDS) is more imperative than ever. In IDS research, the most effectively used methodology is based on supervised Neural Networks (NN) and unsupervised clustering, but there are few works dedicated to their hybridization with metaheuristic algorithms. As intrusion detection data usually contains several features, it is essential to select the best ones appropriately. Linear Discriminant Analysis (LDA) and t-statistic are considered as efficient conventional techniques to select the best features, but they have been little exploited in IDS design. Thus, the research proposed in this… 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 >

S. Priya^1,*, K. Pradeep Mohan Kumar²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3339-3353, 2023, DOI:10.32604/csse.2023.030630

Abstract An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets.… More >

Abdulaziz Aldribi^1,2,*, Aman Singh^2,3, Jose Breñosa^3,4

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3865-3881, 2023, DOI:10.32604/csse.2023.037748

Abstract Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve the intrusion detection mechanism’s performance.… More >

Lelisa Adeba Jilcha¹, Deuk-Hun Kim², Julian Jang-Jaccard³, Jin Kwak^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615

Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.… More >

Mohammed Basheri, Mahmoud Ragab^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3783-3798, 2023, DOI:10.32604/csse.2023.037130

Abstract The Internet of Things (IoT) is one of the emergent technologies with advanced developments in several applications like creating smart environments, enabling Industry 4.0, etc. As IoT devices operate via an inbuilt and limited power supply, the effective utilization of available energy plays a vital role in designing the IoT environment. At the same time, the communication of IoT devices in wireless mediums poses security as a challenging issue. Recently, intrusion detection systems (IDS) have paved the way to detect the presence of intrusions in the IoT environment. With this motivation, this article introduces a novel Quantum Cat Swarm Optimization… More >

Reda Salama¹, Mahmoud Ragab^1,2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2917-2932, 2023, DOI:10.32604/csse.2023.037016

Abstract In the Internet of Things (IoT) based system, the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence… 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 >

Yousef Sanjalawe^1,2,*, Turke Althobaiti^3,4

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3571-3588, 2023, DOI:10.32604/cmc.2023.037386

Abstract Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic features. Therefore, in this paper,… More >