Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

Background: Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain. Objective: The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications. Methods: In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration. Results: The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR. Conclusions: The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.

Intrusion Detection System is yet an interesting research topic. With a very large amount of traffic in real-time networks, feature selection techniques that are effectively able to find important and relevant features are required. Hence, the most important and relevant set of features is the key to improve the performance of intrusion detection system. This study aims to find the best relevant selected features that can be used as important features in a new IDS dataset. To achieve the aim, an approach for generating optimized ensemble IDS is developed. Six features selection methods are used and compared, i.e.: Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), Relief-F (R-F), One-R (OR) and Chi-Square (CS). The feature selection techniques produce sets of selected features. Each best selected number of features that are obtained from feature ranking step for respective feature selection technique will be used to classify attacks via four classification methods, i.e.: Bayesian Network (BN), Naïve Bayesian (NB), Decision Tree: J48 and SOM. Then, each feature selection technique with its respective best features is combined with each classifier method to generate ensemble IDSs. Lastly, the ensemble IDSs are evaluated using Hold-up, K-fold validation approaches, as well as F-Measure and statistical validation approaches. Experimental results using Weka tools on ITD-UTM dataset show the optimized ensemble IDSs using (SU and BN); using (CS and BN) or (CS and SOM) or (IG and NB); and using (OR and BN) with respective ten, four and seven best selected features achieves 81.0316%, 85.2593%, and 80.8625% of accuracy, respectively. In addition, ensemble IDSs using (SU and BN) and using (OR and J48) with ten and six best respective selected features, perform the best F-measure value, i.e.: 0.853 and 0.830, respectively. Indirect comparison with other ensemble IDS on different dataset is discussed.

Security is the main challenge in Internet of Things (IoT) systems. The devices on the IoT networks are very heterogeneous, many of them have limited resources, and they are connected globally, which makes the IoT much more challenging to secure than other types of networks. Denial of service (DoS) is the most popular method used to attack IoT networks, either by flooding services or crashing services. Intrusion detection system (IDS) is one of countermeasures for DoS attack. Unfortunately, the existing IDSs are still suffering from detection accuracy problem due to difficulty of recognizing features of the DoS attacks. Thus, we need to determine specific features that representing well the traffic attacks, so the IDS will be able to distinguish normal traffic from the attacks. In this work, we investigate ping flood attack pattern recognition on IoT networks. Experiments were conducted using wireless communication with three different scenarios: normal traffic, attack traffic, and combined normal-attack traffic. Each scenario created an associated dataset. The datasets were then grouped into two clusters: normal and attack. The K-Means algorithm was used to produce the clustering results. The average number of packets in the attack cluster was 95 931 packets, and the average in the normal cluster was 4,068 packets. The accuracy level of the clustering results was calculated using a confusion matrix. The accuracy of the clustering using the implemented K-Means algorithm was 99.94%. The rates from the confusion matrix were true negative (98.62%), true positive (100.00%), false negative (0.00%), and false positive (1.38%).

Water distribution is generally installed through underground pipes. Monitoring the underground water pipelines is more difficult than monitoring the water pipelines located on the ground in open space. This situation will cause a permanent loss if there is a disturbance in the pipeline such as leakage. Leaks in pipes can be caused by several factors, such as the pipe's age, improper installation, and natural disasters. Therefore, a solution is required to detect and to determine the location of the damage when there is a leak. The detection of the leak location will use fluid mechanics and kinematics physics based on harness water flow rate data obtained using flow liquid meter sensor and Arduino UNO as a microcontroller. The results show that the proposed method is able to work stably to determine the location of the leak which has a maximum distance of 2 metres, and it's able to determine the leak location as close as possible with flow rate about 10 litters per minute.