Hamza Safwan¹, Zeshan Iqbal¹, Rashid Amin¹, Muhammad Attique Khan², Majed Alhaisoni³, Abdullah Alqahtani⁴, Ye Jin Kim⁵, Byoungchol Chang^6,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2365-2381, 2023, DOI:10.32604/cmc.2023.033896 - 31 March 2023

Abstract Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on More >

Moody Alhanaya, Khalil Hamdi Ateyeh Al-Shqeerat^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3709-3724, 2023, DOI:10.32604/iasc.2023.036856 - 15 March 2023

Abstract The increasing number of security holes in the Internet of Things (IoT) networks creates a question about the reliability of existing network intrusion detection systems. This problem has led to the developing of a research area focused on improving network-based intrusion detection system (NIDS) technologies. According to the analysis of different businesses, most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques. However, these techniques are not suitable for every type of network. In light of this, whether the optimal algorithm and feature reduction techniques can… More >

Abdelwahed Motwakel^1,*, Fadwa Alrowais², Khaled Tarmissi³, Radwa Marzouk⁴, Abdullah Mohamed⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Mohamed I. Eldesouki⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3157-3173, 2023, DOI:10.32604/iasc.2023.034908 - 15 March 2023

Abstract The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such… More >

Mesfer Al Duhayyim^1,*, Heba G. Mohamed², Fadwa Alrowais³, Fahd N. Al-Wesabi⁴, Anwer Mustafa Hilal⁵, Abdelwahed Motwakel⁵

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1293-1310, 2023, DOI:10.32604/csse.2023.035589 - 09 February 2023

Abstract The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be… More >

Fahad F. Alruwaili^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 99-115, 2023, DOI:10.32604/cmc.2023.034752 - 06 February 2023

Abstract The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so… More >

Mohammed Maray¹, Haya Mesfer Alshahrani², Khalid A. Alissa³, Najm Alotaibi⁴, Abdulbaset Gaddah⁵, Ali Meree^1,6, Mahmoud Othman⁷, Manar Ahmed Hamza^8,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6587-6604, 2023, DOI:10.32604/cmc.2023.034176 - 28 December 2022

Abstract In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed… More >

Mesfer Al Duhayyim^1,*, Mohammed Maray², Ayman Qahmash², Fatma S. Alrayes³, Nuha Alshuqayran⁴, Jaber S. Alzahrani⁵, Mohammed Alghamdi^2,6, Abdullah Mohamed⁷

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3133-3149, 2023, DOI:10.32604/cmc.2023.032740 - 31 October 2022

Abstract Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services’ domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is… More >

Salaheddine Kably^1,2,*, Tajeddine Benbarrad¹, Nabih Alaoui², Mounir Arioua¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 253-278, 2023, DOI:10.32604/cmc.2023.032220 - 22 September 2022

Abstract Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which… More >

P. Nalayini^1,*, R. Arun Prakash²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2033-2047, 2023, DOI:10.32604/csse.2023.028269 - 01 August 2022

Abstract Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in… More >

R. Bharathi^1,*, T. Abirami²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 797-811, 2023, DOI:10.32604/csse.2023.025336 - 01 June 2022

Abstract With the exponential developments of wireless networking and inexpensive Internet of Things (IoT), a wide range of applications has been designed to attain enhanced services. Due to the limited energy capacity of IoT devices, energy-aware clustering techniques can be highly preferable. At the same time, artificial intelligence (AI) techniques can be applied to perform appropriate disease diagnostic processes. With this motivation, this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification (SSAC-MDC) model in an IoT environment. The goal of the SSAC-MDC technique is to attain maximum energy efficiency and… More >