RESUMO
Along with the advancement of online platforms and significant growth in Internet usage, various threats and cyber-attacks have been emerging and become more complicated and perilous in a day-by-day base. Anomaly-based intrusion detection systems (AIDSs) are lucrative techniques for dealing with cybercrimes. As a relief, AIDS can be equipped with artificial intelligence techniques to validate traffic contents and tackle diverse illicit activities. A variety of methods have been proposed in the literature in recent years. Nevertheless, several important challenges like high false alarm rates, antiquated datasets, imbalanced data, insufficient preprocessing, lack of optimal feature subset, and low detection accuracy in different types of attacks have still remained to be solved. In order to alleviate these shortcomings, in this research a novel intrusion detection system that efficiently detects various types of attacks is proposed. In preprocessing, Smote-Tomek link algorithm is utilized to create balanced classes and produce a standard CICIDS dataset. The proposed system is based on gray wolf and Hunger Games Search (HGS) meta-heuristic algorithms to select feature subsets and detect different attacks such as distributed denial of services, Brute force, Infiltration, Botnet, and Port Scan. Also, to improve exploration and exploitation and boost the convergence speed, genetic algorithm operators are combined with standard algorithms. Using the proposed feature selection technique, more than 80 percent of irrelevant features are removed from the dataset. The behavior of the network is modeled using nonlinear quadratic regression and optimized utilizing the proposed hybrid HGS algorithm. The results show the superior performance of the hybrid algorithm of HGS compared to the baseline algorithms and the well-known research. As shown in the analogy, the proposed model obtained an average test accuracy rate of 99.17%, which has better performance than the baseline algorithm with 94.61% average accuracy.
RESUMO
BACKGROUND: The remote medical monitoring system can facilitate monitoring patients with cardiac arrhythmia, and consequently, reduce mortality and complications in individuals requiring emergency interventions. Hence, it is necessary to evaluate new telemedicine devices and compare them with standard devices. Therefore, this study aimed to evaluate and compare the new remote monitoring system, Smart Emergency Medical System-Health Internet of Things (SEMS-HIOT) developed by the Health Technology Development Centre of Babol University of Medical Sciences on patients with different cardiac arrhythmias and compare it with the standard device. MATERIALS AND METHODS: In this case-control study, 60 patients were divided into the six most common arrhythmia groups (n=10 per each group and equal gender) as atrial fibrillation, ventricular tachycardia, paroxysmal supraventricular tachycardia, premature ventricular contractions, atrial tachycardia, and premature atrial contractions. Also, 20 healthy individuals (including 10 men and 10 women) without any arrhythmia (normal rhythm) were considered as the control group. Three similar SEMS-HIOT devices were used as test devices and a standard cardiac monitoring device as the control device. The clinical parameters, including heart rate, pulse rate, oxygen saturation, body temperature, and cardiac electrical activity via electrocardiogram (ECG) lead-II were recorded. RESULTS: Findings showed that the performance of the SEMS-HIOT test device was similar and in the same range for all indices in each group and there were no significant differences compared to the performance of the control device (P0.05). Also, the ECG records measured with SEMS-HIOT and standard device indicate no significant differences (P0.05). CONCLUSION: Our study showed that the cardiac indices as well as ECG findings, which were measured with SEMS-HIOT and common standard devices confirmed the accuracy and reliability of the new telematics device for monitoring patients with cardiac diseases.