An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

Purpose The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method. Design/methodology/approach This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques. Findings The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively. Originality/value The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti-corona virus-Henry gas solubility optimization-based deep maxout network (ACV-HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV-HGSO is designed by incorporating anti-corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi-objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension-reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively. © 2022 John Wiley & Sons, Ltd.