Security breaches can seriously harm the Internet of Things(IoT)and Industrial IoT(IIoT)environments.The damage can exceedfinancial and material losses to threaten human lives.Overcoming these security risks is challen...Security breaches can seriously harm the Internet of Things(IoT)and Industrial IoT(IIoT)environments.The damage can exceedfinancial and material losses to threaten human lives.Overcoming these security risks is challenging given IoT ubiquity,complexity,and restricted resources.Security intrusion man-agement is a cornerstone in fortifying the defensive security process.This paper presents an integrated multilayered framework facilitating the orchestration of the security intrusion management process and developing security decision support systems.The proposed framework incorporates four layers with four dedicated processing phases.This paper focuses mainly on the analytical layer.We present the architecture and models for predictive intrusion analytics for reactive and proactive defense strategies.We differentiate between the device and network levels to master the complexity of IoT infrastructure.Benefiting from the singu-larity of IIoT devices traffic,we approach the reactive security intrusion predic-tion through outlier detection models mean.We thoroughly experiment with ten outlier detection models on the IIoT wustl realistic dataset.The obtained results show the adequacy of the approach with an area under the curve(AUC)results surpassing 98%for several models with a good level of precision and time effi-ciency.Furthermore,we investigate the use of survival analysis semi-parametric predictive models to forecast the security intrusion before its occurrence for the proactive security strategy.The experiments show encouraging results with a con-cordance index(c-Index)reaching 89%and an integrated brier score(IBS)of 0.02.By integrating outlier intrusion detection and survival forecasting,the fra-mework provides a valuable means to monitor the security intrusions in IoT.展开更多
基金The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4350605DSR01.
文摘Security breaches can seriously harm the Internet of Things(IoT)and Industrial IoT(IIoT)environments.The damage can exceedfinancial and material losses to threaten human lives.Overcoming these security risks is challenging given IoT ubiquity,complexity,and restricted resources.Security intrusion man-agement is a cornerstone in fortifying the defensive security process.This paper presents an integrated multilayered framework facilitating the orchestration of the security intrusion management process and developing security decision support systems.The proposed framework incorporates four layers with four dedicated processing phases.This paper focuses mainly on the analytical layer.We present the architecture and models for predictive intrusion analytics for reactive and proactive defense strategies.We differentiate between the device and network levels to master the complexity of IoT infrastructure.Benefiting from the singu-larity of IIoT devices traffic,we approach the reactive security intrusion predic-tion through outlier detection models mean.We thoroughly experiment with ten outlier detection models on the IIoT wustl realistic dataset.The obtained results show the adequacy of the approach with an area under the curve(AUC)results surpassing 98%for several models with a good level of precision and time effi-ciency.Furthermore,we investigate the use of survival analysis semi-parametric predictive models to forecast the security intrusion before its occurrence for the proactive security strategy.The experiments show encouraging results with a con-cordance index(c-Index)reaching 89%and an integrated brier score(IBS)of 0.02.By integrating outlier intrusion detection and survival forecasting,the fra-mework provides a valuable means to monitor the security intrusions in IoT.
