In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-...In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks periodically.This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks.The Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks.The Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this scenario.The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT environment.The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature selection.Next,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment.Finally,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the hyperparameters.This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty.The proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(168/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R237)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR01).
文摘In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks periodically.This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks.The Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks.The Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this scenario.The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT environment.The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature selection.Next,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment.Finally,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the hyperparameters.This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty.The proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.
