Cloud computing is susceptible to the existing information technology attacks, as it extends and uses the traditional operating system, information technology infrastructure, and applications. However, in addition to the existing threats, the cloud computing environment faces various security issues in detecting anomalous network behaviors. In order to resolve the security issues, an effective intrusion detection system named Chronological Salp Swarm Algorithm‐based Deep Belief Network is proposed for detecting the suspicious intrusions in a cloud environment. Accordingly, the proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network is developed by integrating the Chronological concept with the Salp Swarm Algorithm. The optimal solution for detecting the intrusion is revealed using the fitness function, which accepts the minimal error vale as the optimum solution. Here, the weights are optimally tuned by the proposed algorithm to generate an effective and optimal solution for detecting the intruders. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance through the facility of exploitation and the exploration in search space. The performance of the proposed method is analyzed using two datasets, namely KDD cup dataset and BoT‐IoT dataset, the comparative analysis is performed with the existing methods, such as Host based intrusion detection system, Deep learning, and Deep Neural Network + Genetic Algorithm. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance in terms of accuracy, sensitivity, and specificity, with the values of 0.9618%, 0.9702%, and 0.9307% using KDD cup dataset, and 0.9764%, 0.9824%, and 0.9309% using BoT‐IoT dataset.

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Identifying the interest points in an image is a key step in image processing and computer vision tasks. Every corner of the images represents a lot of information. Extracting the true corners is the main object to image processing, which can reduce much of the time and calculations. Many algorithms have been suggested in the image processing to detect the true corners, based on the robust statistics. In this paper the corner detection algorithms SIFT and FAST have been studied in image processing under the various image formats. Also, it can provide a direction to the researchers to use the algorithm for the suitable image format and to develop a new algorithm which can detect the exact corners of an image/blurred image. The FAST corner detection method compared with the results of SIFT corner detection method. Experimental results show that the FAST corner detection gives better results compared to SIFT method. All the experiments are carried out MATLAB software.