ABSTRACT
Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.
ABSTRACT
Background: The COVID-19 pandemic, its rapid spread has impacted health care workers psychologically. Nurses experience heightened sense of anxiety as they are directly engaged in patient care. This study was designed to assess the Knowledge, attitude, fears of practicing nurses towards the Pandemic situation. Materials: The online questionnaire based descriptive study was conducted among nurses in the southern parts of Tamil Nadu. The Questionnaire was constructed by authors based on previous studies. The data collected were analyzed for descriptive statistics using SPSS 25. Results: A total of 186 completed questionnaires were analyzed for statistics. Female nurses were 146 (78.5%) whereas male participants were 40 (21.5%). Those with 5-10 years’ work experience had participated in large numbers (76). About 53% of the participants are working in Government hospitals. 93(50%) nurses had completed bachelor’s degree in nursing. About 108 nurses (58.1%) are from urban areas. The Mean knowledge score of the participants were 9.34. A majority of 144 nurses (77.4%) had scored more than 80% bloom cut-off marks. About 83 nurses had shown positive attitude in this COVID pandemic situation while more than half of the nurses agreed that spread among them can be controlled by regular Hospital infection control programs. In this study, about 111 nurses (59%) scored higher in anxiety/fear related questions showing that they are under occupation-related stress and anxiety. Conclusion: the study shows that the nurses are well informed about the COVID-19, its transmission and nature of spread. The fear/anxiety stress that most of the participants shows the imminent need for reinforcing active participation in counselling sessions, and hospital infection control programs to tackle the burden of work-related anxiety.