The Internet of Things(IoT)has aided in the development of new products and services.Due to the heterogeneity of IoT items and networks,traditional techniques cannot identify network risks.Rule-based solutions make it...The Internet of Things(IoT)has aided in the development of new products and services.Due to the heterogeneity of IoT items and networks,traditional techniques cannot identify network risks.Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity.While the use of artificial intelligence eliminates the need to define rules,the training and retraining processes require additional processing power.This study proposes a methodology for analyzing constrained devices in IoT environments.We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices.The training and retraining stages for the Mirai attack were also evaluated for accuracy.Various approaches are evaluated in smaller sample sizes to minimize training time on low-resource devices.Cross-validation was used to avoid overfitting classification methods during the learning process.We used the Bootstrapping technique to generate 1000,10000,and 100000 samples to examine the performance metrics of different-sized variations of the dataset.In this study,we demonstrated that a sample size of 10000 is sufficient for 99,56%accuracy and learning in the detection of Mirai attacks in IoT devices.展开更多
Ubiquitous computing facilitated by Internet of things(IoT)devices has made modern day life easier across many areas.It offers capabilities to measure parameters associated with the devices,to infer from their results...Ubiquitous computing facilitated by Internet of things(IoT)devices has made modern day life easier across many areas.It offers capabilities to measure parameters associated with the devices,to infer from their results,and to understand and control millions of such devices in various application domains.The enormous potential of IoT systems enables each and every device to communicate with each other,thereby providing more productivity.In this scenario,heterogeneity of technologies in use is expected to intensify the security threats.Policy enforcement for the assurance of privacy and security plays a key role in these systems.Fulfillment of privacy and security related requirements include con-fidentiality of data,user and device authentication,access control,and trust assurance among the things.However,recent reported events related to security attacks show colossal vulnerabilities among IoT devices capable of bringing security risks to the whole environment.One of the common uses of these devices by the attackers is to generate powerful distributed denial of service(DDoS)at-tacks.It is one of the most prominent attacking behaviors over a network by a group of geographically distributed zombie computers that interrupt and block legitimate users to use the network resources and hence,requires great attention.In this regard,the current work being novel in the field puts concentration on variants of DDoS attacks and their impact on IoT networks along with some of the existing countermeasures to defend against these attacks.The paper also discusses the detailed working mechanism of these attacks and highlights some of the commonly used tools that are deployed in such attack scenarios.展开更多
文摘The Internet of Things(IoT)has aided in the development of new products and services.Due to the heterogeneity of IoT items and networks,traditional techniques cannot identify network risks.Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity.While the use of artificial intelligence eliminates the need to define rules,the training and retraining processes require additional processing power.This study proposes a methodology for analyzing constrained devices in IoT environments.We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices.The training and retraining stages for the Mirai attack were also evaluated for accuracy.Various approaches are evaluated in smaller sample sizes to minimize training time on low-resource devices.Cross-validation was used to avoid overfitting classification methods during the learning process.We used the Bootstrapping technique to generate 1000,10000,and 100000 samples to examine the performance metrics of different-sized variations of the dataset.In this study,we demonstrated that a sample size of 10000 is sufficient for 99,56%accuracy and learning in the detection of Mirai attacks in IoT devices.
文摘Ubiquitous computing facilitated by Internet of things(IoT)devices has made modern day life easier across many areas.It offers capabilities to measure parameters associated with the devices,to infer from their results,and to understand and control millions of such devices in various application domains.The enormous potential of IoT systems enables each and every device to communicate with each other,thereby providing more productivity.In this scenario,heterogeneity of technologies in use is expected to intensify the security threats.Policy enforcement for the assurance of privacy and security plays a key role in these systems.Fulfillment of privacy and security related requirements include con-fidentiality of data,user and device authentication,access control,and trust assurance among the things.However,recent reported events related to security attacks show colossal vulnerabilities among IoT devices capable of bringing security risks to the whole environment.One of the common uses of these devices by the attackers is to generate powerful distributed denial of service(DDoS)at-tacks.It is one of the most prominent attacking behaviors over a network by a group of geographically distributed zombie computers that interrupt and block legitimate users to use the network resources and hence,requires great attention.In this regard,the current work being novel in the field puts concentration on variants of DDoS attacks and their impact on IoT networks along with some of the existing countermeasures to defend against these attacks.The paper also discusses the detailed working mechanism of these attacks and highlights some of the commonly used tools that are deployed in such attack scenarios.
