Il Hwan Ji¹, Ju Hyeon Lee¹, Seungho Jeon², Jung Taek Seo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1519-1549, 2024, DOI:10.32604/cmes.2024.053437 - 27 September 2024

Abstract In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lack of performance of each component, such as computing resources like CPUs and batteries, to encrypt and decrypt data. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomical financial and human casualties. For this reason, the application of encrypted communication to IoT has been required, and the application of encrypted communication to IoT has become possible due to improvements in the computing performance of IoT devices and the development… More >

Kaveh Heidary^*

Journal on Artificial Intelligence, Vol.6, pp. 153-175, 2024, DOI:10.32604/jai.2024.050563 - 18 July 2024

Abstract This paper provides a detailed mathematical model governing the operation of feedforward neural networks (FFNN) and derives the backpropagation formulation utilized in the training process. Network protection systems must ensure secure access to the Internet, reliability of network services, consistency of applications, safeguarding of stored information, and data integrity while in transit across networks. The paper reports on the application of neural networks (NN) and deep learning (DL) analytics to the detection of network traffic anomalies, including network intrusions, and the timely prevention and mitigation of cyberattacks. Among the most prevalent cyber threats are R2L,… More >

Ahmed S. Almasoud^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.039718 - 11 September 2023

Abstract The Internet of Things (IoT) is considered the next-gen connection network and is ubiquitous since it is based on the Internet. Intrusion Detection System (IDS) determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour. In this background, the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification (EMML-CADC) model in an IoT environment. The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency. To attain this, the EMML-CADC… More >

Manal Abdullah Alohali¹, Muna Elsadig¹, Fahd N. Al-Wesabi^2,*, Mesfer Al Duhayyim³, Anwer Mustafa Hilal⁴, Abdelwahed Motwakel⁴

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3583-3598, 2023, DOI:10.32604/csse.2023.037545 - 03 April 2023

Abstract With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) 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 >

Maha Sabir¹, Jawad Ahmad^2,*, Daniyal Alghazzawi¹

Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277 - 20 January 2023

Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based… More >

Abdullah Alshehri^1,*, Nayeem Khan¹, Ali Alowayr¹, Mohammed Yahya Alghamdi²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1679-1689, 2023, DOI:10.32604/csse.2023.026526 - 15 June 2022

Abstract This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or… More >

Amani Abdulrahman Albraikan¹, Siwar Ben Haj Hassine², Suliman Mohamed Fati³, Fahd N. Al-Wesabi^2,4, Anwer Mustafa Hilal^5,*, Abdelwahed Motwakel⁵, Manar Ahmed Hamza⁵, Mesfer Al Duhayyim⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 907-923, 2022, DOI:10.32604/cmc.2022.024488 - 24 February 2022

Abstract Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN More >