RESUMO
Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.
RESUMO
We evaluated the prevalence and association of Vitamin D deficiency with glycemic control and CVD risk in T2DM patients. Serum 25 (OH)D3, lipid profile, glucose panel, HbA1c, serum insulin, and HOMA-IR were assessed in 93 T2DM patients and 69 controls. 10 years and lifetime ASCVD risk scores were calculated. The levels of 25(OH)D3 were significantly low in T2DM patients compared to the control. T2DM patients with hypovitaminosis D displayed significantly increased FBG, insulin, and HOMA-IR compared to normovitaminosis. Their lifetime and 10-year ASCVD risk scores were significantly higher regardless of vitamin D deficiency levels (P=0.006; P=0.023) in comparison to patients with sufficient levels of vitamin D. Among patients, the lifetime and 10 years of ASCVD risk showed a significant negative correlation with serum 25(OH)D3 and HDLc (P=0.037; 0.018) (P=0.0001), respectively, and significant positive correlation with T2DM duration, serum insulin, and HOMA-IR (P=0.018; 0.0001) (P=0.002; 0.001) (P=0.005; 0.001), respectively. The 10-year ASCVD risk exhibited a significant positive correlation with FBG (P=0.003) and HbA1c (P=0.009). T2DM duration was a predictor of vitamin D deficiency among T2DM patients (ß = 0.22; CI = 0.002-0.04). There is a considerable association between lifetime and 10 years of ASCVD risk with hypovitaminosis D in T2DM, regardless of the deficiency levels which could be predicted by the diabetes duration.
RESUMO
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.
