IoT Network Traffic Classification Using Machine Learning Algorithms: An Experimental Analysis

Kumar, Rakesh; Swarnkar, Mayank; Singal, Gaurav; Kumar, Neeraj

doi:10.1109/jiot.2021.3121517

Cited by 59 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Systems and algorithms are developed to deal with the high velocity of the healthcare data for efficient prediction. However, most of such systems are subject to data security risk and face attacks such as denial of service (Dos), snooping, and traffic analysis [ 3 ]. In addition, the recent coronavirus pandemic has forced the hospitals to run at overcapacity making it difficult to keep patient records secure.…”

Section: Introductionmentioning

confidence: 99%

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment

Zala

Thakkar

Jadeja

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Traditional healthcare services have changed into modern ones in which doctors can diagnose patients from a distance. All stakeholders, including patients, ward boy, life insurance agents, physicians, and others, have easy access to patients’ medical records due to cloud computing. The cloud’s services are very cost-effective and scalable, and provide various mobile access options for a patient’s electronic health records (EHRs). EHR privacy and security are critical concerns despite the many benefits of the cloud. Patient health information is extremely sensitive and important, and sending it over an unencrypted wireless media raises a number of security hazards. This study suggests an innovative and secure access system for cloud-based electronic healthcare services storing patient health records in a third-party cloud service provider. The research considers the remote healthcare requirements for maintaining patient information integrity, confidentiality, and security. There will be fewer attacks on e-healthcare records now that stakeholders will have a safe interface and data on the cloud will not be accessible to them. End-to-end encryption is ensured by using multiple keys generated by the key conclusion function (KCF), and access to cloud services is granted based on a person’s identity and the relationship between the parties involved, which protects their personal information that is the methodology used in the proposed scheme. The proposed scheme is best suited for cloud-based e-healthcare services because of its simplicity and robustness. Using different Amazon EC2 hosting options, we examine how well our cloud-based web application service works when the number of requests linearly increases. The performance of our web application service that runs in the cloud is based on how many requests it can handle per second while keeping its response time constant. The proposed secure access scheme for cloud-based web applications was compared to the Ethereum blockchain platform, which uses internet of things (IoT) devices in terms of execution time, throughput, and latency.

show abstract

Section: Introductionmentioning

confidence: 99%

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment

Zala

Thakkar

Jadeja

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Generally, macro average and micro average are used to evaluate scoring metrics for multi‐class classification. The difference between macro average and micro average is that macro has the same weight for each category, while micro has the same weight for each sample [41]. The macro average is adopted to calculate classification effect of the MLSC in the paper, as displayed in Figure 10.…”

Section: Resultsmentioning

confidence: 99%

Fault diagnosis of photovoltaic strings by using machine learning‐based stacking classifier

Liu,

Sun,

Wang

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

Photovoltaic (PV) modules are prone to short circuits, open circuits, cracks, which can bring serious harmful effects. It is difficult to establish the corresponding PV fault models to diagnose the status of PV strings. The paper proposes a machine learning‐based stacking classifier (MLSC) for accurate fault diagnosis of PV strings. Specifically, for the operating state of PV modules, the parameter sensitivity algorithm is used to analyze the impact of characteristic factors on the characteristics of PV modules. Then based on the characteristic factors (irradiance, temperature, current, and power), MLSC is proposed to realize the accurate fault diagnosis of PV strings. This structure of MLSC is to integrate all kinds of classifiers by stacking to play the characteristics of each classifier. It combines the characteristics of various types of machine learning algorithms to improve the overall classification effect by using the advantages of each classifier. Finally, experiments reveal that MLSC improves the accuracy of PV fault diagnosis.

show abstract

“…Sivanathan et al [2] extracted 12 attributes, developed a multi-stage ML model and identified the default behavior of IoT devices. In [8], the authors extracted features from the packet-level, flow-level, and behavior-level, respectively, based on the dataset in [2], performing a comparative analysis of popular ML algorithms. In [9], based on packet length, the authors achieved the real-time classification of IoT and non-IoT, IoT devices, and device events in smart home scenarios.…”

Section: Related Workmentioning

confidence: 99%

Identify IoT Devices from Backbone Networks Using Lightweight Neural Networks

Fan

Cheng

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

View full text Add to dashboard Cite

Due to the heterogeneity, fragmentation, and lack of visibility, Internet of Things has become the new target for attacks. Therefore, it is necessary for Internet Service Providers to identify IoT devices to prevent attacks and protect the entire network in time. In this paper, we propose an IoT device identification approach based on lightweight deep learning models using a single feature. Specifically, we analyze the traffic pattern specific to IoT devices and use one feature to characterize this pattern, reducing the time consumption. Moreover, we select multiple time scales to extract this feature for different IoT devices, achieving an accurate characterization and improving the accuracy. Furthermore, we use unidirectional flows as analysis objects, suitable for backbone networks. The evaluation results on real-world datasets show that our approach achieves an accuracy of over 99%, with one-seventeenth of the time consumption of the state-of-the-art approach, realizing the lightweight and real-time requirements.

show abstract

IoT Network Traffic Classification Using Machine Learning Algorithms: An Experimental Analysis

Cited by 59 publications

References 18 publications

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment

Fault diagnosis of photovoltaic strings by using machine learning‐based stacking classifier

Identify IoT Devices from Backbone Networks Using Lightweight Neural Networks

Contact Info

Product

Resources

About