Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities f...Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.展开更多
Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been...Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.展开更多
Today,road safety remains a serious concern for governments around the world.In fact,approximately 1.35 million people die and 2–50 million are injured on public roads worldwide each year.Straight bends in road traff...Today,road safety remains a serious concern for governments around the world.In fact,approximately 1.35 million people die and 2–50 million are injured on public roads worldwide each year.Straight bends in road traffic are the main cause of many road accidents,and excessive and inappropriate speed in this very critical area can cause drivers to lose their vehicle stability.For these reasons,new solutions must be considered to stop this disaster and save lives.Therefore,it is necessary to study this topic very carefully and use new technologies such as Vehicle Ad Hoc Networks(VANET),Internet of Things(IoT),Multi-Agent Systems(MAS)and Embedded Systems to create a new system to serve the purpose.Therefore,the efficient and intelligent operation of the VANET network can avoid such problems as it provides drivers with the necessary real-time traffic data.Thus,drivers are able to drive their vehicles under correct and realistic conditions.In this document,we propose a speed adaptation scheme for winding road situations.Our proposed scheme is based on MAS technology,the main goal of which is to provide drivers with the information they need to calculate the speed limit they must not exceed in order to maintain balance in dangerous areas,especially in curves.The proposed scheme provides flexibility,adaptability,and maintainability for traffic information,taking into account the state of infrastructure and metering conditions of the road,as well as the characteristics and behavior of vehicles.展开更多
Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them ...Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them useful.Regardless of these advantages,cloud providers suffer from many security limits.Particularly,the security of resources and services represents a real challenge for cloud technologies.For this reason,a set of solutions have been implemented to improve cloud security by monitoring resources,services,and networks,then detect attacks.Actually,intrusion detection system(IDS)is an enhanced mechanism used to control traffic within networks and detect abnormal activities.This paper presents a cloud-based intrusion detection model based on random forest(RF)and feature engineering.Specifically,the RF classifier is obtained and integrated to enhance accuracy(ACC)of the proposed detection model.The proposed model approach has been evaluated and validated on two datasets and gives 98.3%ACC and 99.99%ACC using Bot-IoT and NSL-KDD datasets,respectively.Consequently,the obtained results present good performances in terms of ACC,precision,and recall when compared to the recent related works.展开更多
Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.How...Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.展开更多
Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for an extended period. IoT interface...Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for an extended period. IoT interface attacks must be evaluated in real-time for effective safety and security measures. This study implements a smart intrusion detection system (IDS) designed for IoT threats, and interoperability with IoT connectivity standards is offered by the identity solution. An IDS is a common type of network security technology that has recently received increasing interest in the research community. The system has already piqued the curiosity of scientific and industrial communities to identify intrusions. Several IDSs based on machine learning (ML) and deep learning (DL) have been proposed. This study introduces IDS-SIoDL, a novel IDS for IoT-based smart cities that integrates long shortterm memory (LSTM) and feature engineering. This model is tested using tensor processing unit (TPU) on the enhanced BoT-IoT, Edge-IIoT, and NSL-KDD datasets. Compared with current IDSs, the obtained results provide good assessment features, such as accuracy, recall, and precision, with approximately 0.9990 recording time and calculating times of approximately 600 and 6 ms for training and classification, respectively.展开更多
文摘Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.
文摘Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.
基金King Saud University through Researchers Support-ing Project number(RSP-2021/387),King Saud University,Riyadh,Saudi Arabia。
文摘Today,road safety remains a serious concern for governments around the world.In fact,approximately 1.35 million people die and 2–50 million are injured on public roads worldwide each year.Straight bends in road traffic are the main cause of many road accidents,and excessive and inappropriate speed in this very critical area can cause drivers to lose their vehicle stability.For these reasons,new solutions must be considered to stop this disaster and save lives.Therefore,it is necessary to study this topic very carefully and use new technologies such as Vehicle Ad Hoc Networks(VANET),Internet of Things(IoT),Multi-Agent Systems(MAS)and Embedded Systems to create a new system to serve the purpose.Therefore,the efficient and intelligent operation of the VANET network can avoid such problems as it provides drivers with the necessary real-time traffic data.Thus,drivers are able to drive their vehicles under correct and realistic conditions.In this document,we propose a speed adaptation scheme for winding road situations.Our proposed scheme is based on MAS technology,the main goal of which is to provide drivers with the information they need to calculate the speed limit they must not exceed in order to maintain balance in dangerous areas,especially in curves.The proposed scheme provides flexibility,adaptability,and maintainability for traffic information,taking into account the state of infrastructure and metering conditions of the road,as well as the characteristics and behavior of vehicles.
文摘Cloud computing(CC)is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services.In addition,it aims to strengthen systems and make them useful.Regardless of these advantages,cloud providers suffer from many security limits.Particularly,the security of resources and services represents a real challenge for cloud technologies.For this reason,a set of solutions have been implemented to improve cloud security by monitoring resources,services,and networks,then detect attacks.Actually,intrusion detection system(IDS)is an enhanced mechanism used to control traffic within networks and detect abnormal activities.This paper presents a cloud-based intrusion detection model based on random forest(RF)and feature engineering.Specifically,the RF classifier is obtained and integrated to enhance accuracy(ACC)of the proposed detection model.The proposed model approach has been evaluated and validated on two datasets and gives 98.3%ACC and 99.99%ACC using Bot-IoT and NSL-KDD datasets,respectively.Consequently,the obtained results present good performances in terms of ACC,precision,and recall when compared to the recent related works.
文摘Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.
文摘Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for an extended period. IoT interface attacks must be evaluated in real-time for effective safety and security measures. This study implements a smart intrusion detection system (IDS) designed for IoT threats, and interoperability with IoT connectivity standards is offered by the identity solution. An IDS is a common type of network security technology that has recently received increasing interest in the research community. The system has already piqued the curiosity of scientific and industrial communities to identify intrusions. Several IDSs based on machine learning (ML) and deep learning (DL) have been proposed. This study introduces IDS-SIoDL, a novel IDS for IoT-based smart cities that integrates long shortterm memory (LSTM) and feature engineering. This model is tested using tensor processing unit (TPU) on the enhanced BoT-IoT, Edge-IIoT, and NSL-KDD datasets. Compared with current IDSs, the obtained results provide good assessment features, such as accuracy, recall, and precision, with approximately 0.9990 recording time and calculating times of approximately 600 and 6 ms for training and classification, respectively.
