بشائر عبود كريم موسوي

Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively.

Recently, the emphasis on developing laboratory teaching skills for university classes has increased. Past researchers had mainly focused on the need to improve teaching methods to transfer from the teacher is the center of the learning to the student is the center of the learning. Therefore, the main purpose of this paper is to investigate the effects of a biotechnology virtual labs approach to improve the understanding of 1 st year students. The sampling of the research selected from the biology department faculty of the education university of Kufa-Iraq has been selected using a purposive sampling technique with a total sample size of 120 students. A standard instrument tool is used to measure the students' understanding of cell biology class which is named the understanding cell biology virtual lab test (UCBVLT). Data obtained from research instruments were then analyzed using SPSS. Independent sample t-test analyses revealed a significant difference in terms of 1 st year university student understanding of cell Biology between those who are exposed to biotechnology virtual labs and those who followed the conventional lab activities. In conclusion, the proposed teaching approach (BVL) achieved better results for students' understanding of cell biology. The research is recommended to implement the BVL approach for cell biology class and other science classes. Another important point, the proposed teaching approach (BVL), The proposed learning method can play a crucial role in teaching several sciences classes and be an alternative to traditional education in cases of necessity, as happened at the time of the COVID-19 pandemic.