An optimized neural network for prediction of security threats on software testing

Software testing involves evaluating and confirming a software program or product to ensure it operates according to its intended functionality. Testing offers advantages like bug prevention, reduced development expenses, and improved performance. The problems are dialogue gap, ecological danger, creation of software quickly, cost of operation and upkeep, inadequate assessment, and incorrect testing estimates. The structure was initially educated using internet presentation data that included intrusion information. A novel Dove Swarmbased Deep Neural Method (DSbDNM) with the required traits and stages of processing has been developed. Moving forward, feature extraction and malicious behaviour forecast have both been completed. Also, the different types of assaults and negative behaviours were categorized. The developed prediction model is also examined by initiating and detecting an unidentified assault. Finally, the performance measures' accuracy, error rate, Precision, Recall and f-measure were computed. Moreover, the proposed system implementation is done in Python. Therefore, the proposed work performance can be enhanced and attain high accuracy in low computational time. For the DSbDNM dataset, the designed prototypical achieved 94.65 accuracy, 94.95 precision, 90.16 Recall and 92.02 F-measure for the NF-UQ-NIDS-v2 Dataset. Moreover, the Intrusion Detection Dataset attained an accuracy of 98, Precision of 98.8, Recall of 94.2, and F-score of 96 in the developed model. Subsequently, the Network Intrusion Detection Dataset attained an accuracy of 99, a precision of 99.2, a Recall of 95.8 and an F-measure of 97.1