Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communica-tions.MANETs are more vulnerable to security threats.Changes in nodes,band...Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communica-tions.MANETs are more vulnerable to security threats.Changes in nodes,band-width limits,and centralized control and management are some of the characteristics.IDS(Intrusion Detection System)are the aid for detection,deter-mination,and identification of illegal system activity such as use,copying,mod-ification,and destruction of data.To address the identified issues,academics have begun to concentrate on building IDS-based machine learning algorithms.Deep learning is a type of machine learning that can produce exceptional outcomes.This study proposes that WOA-DNN be used to detect and classify incursions in MANET(Whale Optimized Deep Neural Network Model)WOA(Whale Opti-mization Algorithm)and DNN(Deep Neural Network)are used to optimize the preprocessed data to construct a system for classifying and predicting unantici-pated cyber-attacks that are both effective and efficient.As a result,secure data transport to other nodes is provided,preventing intruder attacks.The invaders are found using the(Machine Learning)ML-IDS and WOA-DNN methods.The data is reduced in dimensionality using Principal Component Analysis(PCA),which improves the accuracy of the outputs.A classifier is used in forward propagation to predict whether a result is normal or malicious.To compare the traditional and proposed models’effectiveness,the accuracy of classification,detection of the attack rate,precision rate,and F-Measure,Recall are utilized.The proposed WOA-DNN model has higher assessment metrics and a 99.1%accuracy rate.WOA-DNN also has a greater assault detection rate than others,resulting in fewer false alarms.The classification accuracy of the proposed WOA-DNN model is 99.1%.展开更多
文摘Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communica-tions.MANETs are more vulnerable to security threats.Changes in nodes,band-width limits,and centralized control and management are some of the characteristics.IDS(Intrusion Detection System)are the aid for detection,deter-mination,and identification of illegal system activity such as use,copying,mod-ification,and destruction of data.To address the identified issues,academics have begun to concentrate on building IDS-based machine learning algorithms.Deep learning is a type of machine learning that can produce exceptional outcomes.This study proposes that WOA-DNN be used to detect and classify incursions in MANET(Whale Optimized Deep Neural Network Model)WOA(Whale Opti-mization Algorithm)and DNN(Deep Neural Network)are used to optimize the preprocessed data to construct a system for classifying and predicting unantici-pated cyber-attacks that are both effective and efficient.As a result,secure data transport to other nodes is provided,preventing intruder attacks.The invaders are found using the(Machine Learning)ML-IDS and WOA-DNN methods.The data is reduced in dimensionality using Principal Component Analysis(PCA),which improves the accuracy of the outputs.A classifier is used in forward propagation to predict whether a result is normal or malicious.To compare the traditional and proposed models’effectiveness,the accuracy of classification,detection of the attack rate,precision rate,and F-Measure,Recall are utilized.The proposed WOA-DNN model has higher assessment metrics and a 99.1%accuracy rate.WOA-DNN also has a greater assault detection rate than others,resulting in fewer false alarms.The classification accuracy of the proposed WOA-DNN model is 99.1%.
