The smartphones users have been rapidly increasing over the years, mainly the Android users. The main concern on the Android platform growing is malware detection. Among the various existing approaches in detecting malware, machine learning-based algorithms have achieved a high accuracy in detecting malware. Therefore, Android devices must be provided with the ability to detect malware using machine learning algorithms. Different researchers proposed different Machine learning systems to detect malware based on Machine Learning Algorithms such as SVM, NB, and DNN. This paper surveys the state of the art on Android malware detection techniques by focusing on Machine Learning-based classifiers to detect malicious software on Android devices.

Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the performance of support vector machine (SVM) and Bayesian network (BN) in predicting the survival state of breast cancer patients after having a surgery. The experiments on both techniques have been carried out using Weka software package. Empirical results from simulations showed that support vector machine outperformed Bayesian network in this task, where support vector machine achieved better accuracy of 74.44% while Bayesian network had its best accuracy of 67.56%.

Industrial Control Systems (ICS) are the backbone of many critical infrastructure sectors; however, their growing level of connectivity, long lifespan and integration with the Information Technology (IT) environment introduces numerous cybersecurity challenges. The merging of Operational Technology (OT) and IT along with the deployment of Industry 4.0 technologies increases the attack surface of ICS environments, which in turn makes them more vulnerable to advanced cyber threats. Therefore, many researchers have shown interest in the field of cybersecurity of ICS. The topics of intrusion detection, anomaly detection, threat intelligence, attack simulation and resilience assessment of ICS have received much attention. Nevertheless, the development and testing of cybersecurity solutions for ICS remains to be challenging due to the lack of appropriate datasets and experimental environment. The main objective of this paper is to provide the roadmap of existing ICS cybersecurity datasets, testbeds and digital twins. This paper presents various taxonomies along with systematic analysis of architecture, characteristics, capabilities, pros and cons of these tools. The results of the analysis demonstrate the presence of persistent problems such as lack of standardized benchmarking datasets, lack of modern attack scenarios, insufficient number of datasets based on real operational traffic and difficulty in validating artificial intelligence-driven cybersecurity solutions. In addition to summarizing current research on ICS cybersecurity datasets and testbeds, this roadmap provides the identification of research gaps and recommendations on creation of new tools.