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

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 model is carried out with… More >

Weiwei Lin^1,2,*, Reiko Haga³

Journal on Big Data, Vol.3, No.4, pp. 147-153, 2021, DOI:10.32604/jbd.2021.017299

Abstract In this paper, a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure. Through the ant colony algorithm structure, the local global optimal solution is obtained; and the cybersecurity threat warning index system is established. Next, the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm, and comparative experiment is also designed. The experimental results show that, compared with the traditional qualitative differential game-based cybersecurity threat warning model, the cybersecurity threat… More >

Mesfer Al Duhayyim¹, Haya Mesfer Alshahrani², Fahd N. Al-Wesabi³, Mohammed Alamgeer⁴, Anwer Mustafa Hilal^5,*, Mohammed Rizwanullah⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6257-6270, 2022, DOI:10.32604/cmc.2022.021212

Abstract Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article… More >

Ahmed Abdullah Alqarni¹, Nizar Alsharif¹, Nayeem Ahmad Khan^1,*, Lilia Georgieva², Eric Pardade³, Mohammed Y. Alzahrani¹

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4075-4085, 2022, DOI:10.32604/cmc.2022.020389

Abstract The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing. A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic. In recent years, researchers have used different machine learning methods to detect network attacks without relying on those signatures. The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product. In this study, we propose and implement a new method that relies on a modular deep neural network for reducing the… More >

Maher Alsharif¹, Shailendra Mishra^2,*, Mohammed AlShehri¹

Computer Systems Science and Engineering, Vol.40, No.3, pp. 1153-1166, 2022, DOI:10.32604/csse.2022.019938

Abstract Today, security is a major challenge linked with computer network companies that cannot defend against cyber-attacks. Numerous vulnerable factors increase security risks and cyber-attacks, including viruses, the internet, communications, and hackers. Internets of Things (IoT) devices are more effective, and the number of devices connected to the internet is constantly increasing, and governments and businesses are also using these technologies to perform business activities effectively. However, the increasing uses of technologies also increase risks, such as password attacks, social engineering, and phishing attacks. Humans play a major role in the field of cybersecurity. It is observed that more than 39%… More >

Nazarii Lutsiv¹, Taras Maksymyuk^1,*, Mykola Beshley¹, Orest Lavriv¹, Volodymyr Andrushchak¹, Anatoliy Sachenko², Liberios Vokorokos³, Juraj Gazda³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 413-431, 2022, DOI:10.32604/cmc.2022.018773

Abstract The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach… More >

Sulaiman Yousef Alshunaifi, Shailendra Mishra^*, Mohammed Alshehri

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 13-28, 2022, DOI:10.32604/iasc.2022.019121

Abstract 5G technology is widely seen as a game-changer for the IT and telecommunications sectors. Benefits expected from 5G include lower latency, higher capacity, and greater levels of bandwidth. 5G also has the potential to provide additional bandwidth in terms of AI support, further increasing the benefits to the IT and telecom sectors. There are many security threats and organizational vulnerabilities that can be exploited by fraudsters to take over or damage corporate data. This research addresses cybersecurity issues and vulnerabilities in 4G(LTE) and 5G technology. The findings in this research were obtained by using primary and secondary data. Secondary data… More >

Nojood O. Aljehane^*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3915-3929, 2021, DOI:10.32604/cmc.2021.017905

Abstract Cyber physical systems (CPSs) are a networked system of cyber (computation, communication) and physical (sensors, actuators) elements that interact in a feedback loop with the assistance of human interference. Generally, CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices. Increased utilization of CPSs, however, poses many threats, which may be of major significance for users. Such security issues in CPSs represent a global issue; therefore, developing a robust, secure, and effective CPS is currently a hot research topic. To resolve this issue, an intrusion… More >

Thavavel Vaiyapuri^*, Adel Binbusayyis

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665

Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More >

Ramsha Saeed¹, Saddaf Rubab¹, Sara Asif¹, Malik M. Khan¹, Saeed Murtaza¹, Seifedine Kadry², Yunyoung Nam^3,*, Muhammad Attique Khan^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 533-547, 2021, DOI:10.32604/cmes.2021.014355

Abstract The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically enriched model is proposed which… More >