Supervisory control and data acquisition (SCADA) systems have become a salient part in controlling critical infrastructures, such as power plants, energy grids, and water distribution systems. In the past decades, these systems were isolated and use proprietary software, operating systems, and protocols. In recent years, SCADA systems have been interfaced with enterprise systems, which therefore exposed them to the vulnerabilities of the Internet and the security threats. Traditional security solutions (e.g., firewalls, antivirus software, and intrusion detection systems) cannot fully protect SCADA systems, because they have different requirements. This paper presents an innovative intrusion detection approach to detect SCADA tailored attacks. This is based on a data-driven clustering technique of process parameters, which automatically identifies the normal and critical states of a given system. Later, it extracts proximity-based detection rules from the identified states for monitoring purposes. The effectiveness of the proposed approach is tested by conducting experiments on eight data sets that consist of process parameters' values. The empirical results demonstrated an average accuracy of 98% in automatically identifying the critical states, while facilitating the monitoring of the SCADA system.

Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains.

This paper presents findings from a study evaluating eLearning systems in the Kingdom of Saudi Arabia (KSA). E-Learning Systems are a technological development that have reformed and restructured the delivery and interaction of students and teachers with course materials and related resources. E-Learning systems have been widely used in developed countries and have recently become more popular in many developing countries. To date, however, little attention has been paid to the issue of measuring the impact of eLearning on academic staff at higher educational institutions in the Kingdom of Saudi Arabia. Responding to this gap in the literature, this study investigates the impact of e-Learning systems in higher education institutions in this context. A survey based on the IS Success/Impact Measurement Framework was executed in a sample population of faculty members using e-Learning in two top universities in the KSA. The findings of this study show positive academics’ attitudes towards eLearning systems in higher education, helping faculty members to improve their job performance, and educational organizations to provide better and new products and services to users.