Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to organizations.To preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT ecosystems.In this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks.The proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and classification.In the pre-processing phase,data cleaning and nor-malization are performed.In the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components analysis.For thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant analysis.The proposed work uses a new data-driven IIoT data set called X-IIoTID.The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%.

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

The rapid growth of the Internet of Things(IoT)in the industrial sector has given rise to a new term:the Industrial Internet of Things(IIoT).The IIoT is a collection of devices,apps,and services that connect physical and virtual worlds to create smart,cost-effective,and scalable systems.Although the IIoT has been implemented and incorporated into a wide range of industrial control systems,maintaining its security and privacy remains a significant concern.In the IIoT contexts,an intrusion detection system(IDS)can be an effective security solution for ensuring data confidentiality,integrity,and availability.In this paper,we propose an intelligent intrusion detection technique that uses principal components analysis(PCA)as a feature engineering method to choose the most significant features,minimize data dimensionality,and enhance detection performance.In the classification phase,we use clustering algorithms such as K-medoids and K-means to determine whether a given flow of IIoT traffic is normal or attack for binary classification and identify the group of cyberattacks according to its specific type for multi-class classification.To validate the effectiveness and robustness of our proposed model,we validate the detection method on a new driven IIoT dataset called X-IIoTID.The performance results showed our proposed detection model obtained a higher accuracy rate of 99.79%and reduced error rate of 0.21%when compared to existing techniques.

Anomaly Detection for Internet of Things Cyberattacks

The Internet of Things(IoT)has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives.The IoT revolution has redefined digital services in different domains by improving efficiency,productivity,and cost-effectiveness.Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’operation;however,IoT security issues remain a significant challenge.To minimize the risk of cyberattacks on IoT networks,anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT cyberattacks.Although various detection techniques have been proposed in the literature,existing detection methods address limited cyberattacks and utilize outdated datasets for evaluations.In this paper,we propose an intelligent,effective,and lightweight detection approach to detect several IoT attacks.Our proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection model.In the detection phase,we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT attacks.The experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%,a precision rate of 99.982%,a recall rate of 99.984%,and an F1-score of 99.983%.

Anomaly Detection Framework in Fog-to-Things Communication for Industrial Internet of Things

The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable systems.Although the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big challenge.An anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT environments.In this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data source.The anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection performance.In the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or malicious.To validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack protocols.The experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.

Energy-efficient and Secure Wireless Communication for Telemedicine in IoT

The Internet of Things (IoT) represents a radical shifting paradigm fortechnological innovations as it can play critical roles in cyberspace applications invarious sectors, such as security, monitoring, medical, and environmental sectors,and also in control and industrial applications. The IoT in E-medicine unleashedthe design space for new technologies to give instant treatment to patients whilealso monitoring and tracking health conditions. This research presents a systemlevel architecture approach for IoT energy efficiency and security. The proposedarchitecture includes functional components that provide privacy managementand system security. Components in the security function group provide securecommunications through Multi-Authority Ciphertext-Policy Attributes-BasedEncryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices,presuming that the devices are reliable, the user encodes data with AdvancedEncryption Standard (AES) and protects the ABE approach using the solutionsof symmetric key. The Johnson’s algorithm with a new computation measure isused to increase network lifetime since an individual sensor node with limitedenergy represents the inevitable constraints for the broad usage of wireless sensornetworks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residualenergy, the number of neighbors, their distance, and the link dependability. Theexperiment results demonstrate that the proposed model increases network lifeby about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s,and Dijkstra’s algorithms, lowering consumption of energy by eliminating thenecessity for re-routing the message as a result of connection failure.