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

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 irregular behavior. The importance of… More >

Ashit Kumar Dutta^1,*, Basit Qureshi², Yasser Albagory³, Majed Alsanea⁴, Anas Waleed AbulFaraj⁵, Abdul Rahaman Wahab Sait⁶

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2097-2110, 2022, DOI:10.32604/iasc.2022.027500

Abstract Recently, the exponential utilization of Internet has posed several cybersecurity issues in social networks. Particularly, cyberbulling becomes a common threat to users in real time environment. Automated detection and classification of cyberbullying in social networks become an essential task, which can be derived by the use of machine learning (ML) and deep learning (DL) approaches. Since the hyperparameters of the DL model are important for optimal outcomes, appropriate tuning strategy becomes important by the use of metaheuristic optimization algorithms. In this study, an effective glowworm swarm optimization (GSO) with deep neural network (DNN) model named EGSO-DNN is derived for cybersecurity… More >

Niraj Kumar^1,*, Vishnu Mohan Mishra², Adesh Kumar³

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 623-638, 2022, DOI:10.32604/iasc.2022.024804

Abstract Supervisory control and data acquisition (SCADA) systems continuously monitor the real-time processes in the smart grid. The system software, which is based on a human-machine interface (HMI), makes intelligent decisions to assist the system operator and perform normal grid management activities. The management of SCADA networks and monitoring without proper security is a major concern, as many grids and plant networks still lack necessary monitoring and detection systems, making them vulnerable to attack. SCADA networks exploit physical weaknesses as well as cyber-attacks. Researchers have developed a monitoring system based on a field-programmable gate array (FPGA) and a microcontroller that allows… More >

Abdullah Saad AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*, Maha Farouk S. Sabir¹, Ahmed Elhassanein^5,6, Ashraf A. Gouda⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3385-3399, 2022, DOI:10.32604/cmc.2022.026477

Abstract Education acts as an important part of economic growth and improvement in human welfare. The educational sectors have transformed a lot in recent days, and Information and Communication Technology (ICT) is an effective part of the education field. Almost every action in university and college, right from the process from counselling to admissions and fee deposits has been automated. Attendance records, quiz, evaluation, mark, and grade submissions involved the utilization of the ICT. Therefore, security is essential to accomplish cybersecurity in higher security institutions (HEIs). In this view, this study develops an Automated Outlier Detection for CyberSecurity in Higher Education… More >

Abdullah Saad AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3131-3144, 2022, DOI:10.32604/cmc.2022.026457

Abstract Education 4.0 is being authorized more and more by the design of artificial intelligence (AI) techniques. Higher education institutions (HEI) have started to utilize Internet technologies to improve the quality of the service and boost knowledge. Due to the unavailability of information technology (IT) infrastructures, HEI is vulnerable to cyberattacks. Biometric authentication can be used to authenticate a person based on biological features such as face, fingerprint, iris, and so on. This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions, named SRODL-LAC technique. The proposed SRODL-LAC technique… More >

Abdullah S. AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*, Maha Farouk S. Sabir¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2895-2907, 2022, DOI:10.32604/cmc.2022.026405

Abstract As higher education institutions (HEIs) go online, several benefits are attained, and also it is vulnerable to several kinds of attacks. To accomplish security, this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish security. The incorporation of the strategies into business is a tendency among several distinct industries, comprising education, have recognized as game changer. Consequently, the HEIs are highly related to the requirement and knowledge of the learner, making the education procedure highly effective. Thus, artificial intelligence (AI) and machine learning (ML) models have shown significant interest in HEIs. This study designs a novel Artificial Intelligence… More >

Ghalib H. Alshammri^1,2, Amani K. Samha³, Ezz El-Din Hemdan⁴, Mohammed Amoon^1,4, Walid El-Shafai^5,6,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3529-3548, 2022, DOI:10.32604/cmc.2022.025262

Abstract Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process. In recent times, the most complex task in Software Defined Network (SDN) is security, which is based on a centralized, programmable controller. Therefore, monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment. Consequently, this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms: K-means, Farthest First, Canopy, Density-based algorithm, and Exception-maximization (EM), using the Waikato Environment for Knowledge Analysis (WEKA) software to compare extensively between these five algorithms.… More >

Ahmed S. Almasoud¹, Abdelzahir Abdelmaboud², Faisal S. Alsubaei³, Manar Ahmed Hamza^4,*, Ishfaq Yaseen⁴, Mohammed Abaker⁵, Abdelwahed Motwakel⁴, Mohammed Rizwanullah⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2633-2648, 2022, DOI:10.32604/cmc.2022.024619

Abstract Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a… More >

Mohammad Hafiz Mohd Yusof^1,*, Abdullah Mohd Zin², Nurhizam Safie Mohd Satar²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571

Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common… More >

M. Anuradha^1,*, G. Mani², T. Shanthi³, N. R. Nagarajan⁴, P. Suresh⁵, C. Bharatiraja⁶

Computer Systems Science and Engineering, Vol.43, No.1, pp. 381-396, 2022, DOI:10.32604/csse.2022.023321

Abstract In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS… More >