Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms

Saheed, Yakub Kayode; Arowolo, Micheal Olaolu

doi:10.1109/access.2021.3128837

Cited by 99 publications

(45 citation statements)

References 47 publications

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“…So, the authors utilized the Deep Recurrent Neural Network (DRNN) and Supervised Machine Learning (SML) models to create an effective IDS system for the IoMT environment. In the analysis, the authors show that the proposed model outperformed existing techniques [11].…”

Section: Literature Reviewmentioning

confidence: 95%

Cyberattacks Detection in IoMT using Machine Learning Techniques

Tauqeer

Iqbal

Ali³

et al. 2022

JCBI

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Information and Communication Technology (ICT) has changed the computing paradigm. Various new channels for communication are created through these developments, and the Internet of Things (IoT) is one of them. Internet of Medical Things (IoMT) is a part of IoT in which medical devices are connected through a network. IoMT has resolved many traditional health-related problems and has some security concerns. This article uses three Machine Learning algorithms, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), to detect cyberattacks. Machine Learning models are best for performing cyberattack detection. Proposed Machine Learning models are evaluated on the WUSTL EHMS 2020 dataset, which consists of main in-themiddle, data injection, and spoofing attacks. The evaluation of the result analysis shows that the proposed Machine Learning models outperformed existing techniques.

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Section: Literature Reviewmentioning

confidence: 95%

Cyberattacks Detection in IoMT using Machine Learning Techniques

Tauqeer

Iqbal

Ali³

et al. 2022

JCBI

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“…To solve this problem, an intrusion detection and prevention system must be widely deployed across the network. There has been some development in the design of IDSs, but despite this, intrusion detection remains a difficult challenge [ 6 , 18 ] [ 19 ] due to the vast volume of network traffic, the variety of network features available, and the plethora of attacking patterns. It is obvious that false-negative mistakes could happen when using network-based detection algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Although many previous works have successfully developed Internet of Healthcare [ 5 ], intrusion detection is still challenging due to the high volume of network traffic data, numerous available network features, and various cyberattack patterns[ 6 ]. Despite this, there have been many previous works that have made some progress.…”

Section: Introductionmentioning

confidence: 99%

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Abdulwahid

2022

Computational Intelligence and Neuroscience

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The huge number of network traffic data, the abundance of available network features, and the diversity of cyber-attack patterns mean that intrusion detection remains difficult even though many earlier efforts have succeeded in building the Internet of Healthcare Things (IoHT). The implementation of an effective algorithm to filter out most of the probable outliers of Round Trip Time (RTT) of packets recorded in the Internet environment is urgently required. Congestion and interference in networks can arise when numerous biosensors in an IoHT system all attempt to communicate at once. Internet of Health Things networks are susceptible to both intra- and internetwork interference. In this research, the Server-Side Includes (SSI) attack is a key issue because it allows for network compromise as part of Internal Attacks. Despite recent advancements, SSI detection remains difficult due to the vast amounts of network traffic data, the abundance of network features, and the diversity of cyber-attack patterns (DDoS, DoS, Satan, spoofing, etc.). With the help of sensors, physiological data may be collected and sent to distant servers, where they can be analyzed in real time by doctors to help them catch diseases in their earliest stages. This is made possible by the Internet of medical things (IoMT). Wireless data transfer, however, leaves it vulnerable to hackers, especially if the data being transferred are particularly private or sensitive. Security measures designed for devices with more storage space and processing power will not work on those with less. However, machine learning for intrusion detection can give a tailored security response to the needs of IoMT systems. For SSI detection, current methods are either inefficient because of the large number of packets that need to be caught and analyzed or unsuccessful because of outlier values in the RTTs obtained from the captured TCP packets. To the same end, “downstream detection” refers to the process of calculating the total length of all connections made after a certain point. As a means of improving the SSI detection algorithm’s throughput in a network environment, packet RTT outliers will be eliminated. Flow records are used as inputs by flow-based NIDS to determine whether or not a given flow is malicious. In order to detect middlebox-based attacks from two Medical Health IoT datasets, this paper proposes a unique architecture of explainable neural networks (XNN). The model’s accuracy in classifying attacks in dataset 1 of the IoHT is 99.7%t, besides achieving 99.4% accuracy in categorising attacks on IoHT dataset 2.

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“…EXPLORE THE ACCURATE EVALUATION MECHANISM OF INFORMATION LITERACY college students' information literacy is a complex highlevel ability literacy, especially the scientific and accurate evaluation of college students is a very challenging task. Based on machine learning technology and data mining technology, we can predict the measured data of college students' information literacy and establish an evaluation model [66], [67], which will greatly promote the improvement of the blended learning model of information literacy. At the same time, we can accurately evaluate the learning status of college students, so that we can improve the information literacy of individual college students, and carry out datadriven information literacy research of college students in a sustainable and effective manner.…”

Section: )mentioning

confidence: 99%

A Blended Learning Model Based on Smart Learning Environment to Improve College Students’ Information Literacy

2022

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Under the epidemic situation, it is more and more important to improve college students' information literacy.In this paper, we are the first to propose an information literacy improvement model for college students based on smart learning environment. On the basis of previous studies and literature analysis, we describe the elements of the smart learning environment for the cultivation of college students' information literacy.These elements, which we summarize as CIAP, consist of four aspects: conceptual level, intelligent level , action level and process level.Based on CIAP,we propose a new blended learning model to improve college students' information literacy sustainably. The first is to expand learning resources by special topics; The second is to create a learning environment intelligently; The third is to clarify the interactive learning activities; The fourth is the innovative mutual learning process; The fifth is the timely verification of learning feedback; The sixth is the multiple optimization of learning evaluation.We have carried out targeted experiments to test the validity of the blended mode.Through the concrete empirical study of college students majoring in engineering technology in Chinese university, it is concluded that there is a statistically significant difference between the post-test data of the experimental class and the control class.The results prove that the blended learning based on smart learning environment proposed in this paper has a significant effect on the cultivation of information literacy of college students.This paper discusses the spiral development of information literacy enabled by the smart learning environment. Our work involving studies of the thinking and direction of the sustainable development of information literacy training proved to be encouraging.

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Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms

Cited by 99 publications

References 47 publications

Cyberattacks Detection in IoMT using Machine Learning Techniques

Cyberattacks Detection in IoMT using Machine Learning Techniques

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

A Blended Learning Model Based on Smart Learning Environment to Improve College Students’ Information Literacy

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