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Malicious Traffic Flow Detection in IOT Using Ml Based Algorithms
2021
International Research Journal on Advanced Science Hub
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Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network. ...
To overcome this problem, a new structure in machine learning (ML) is introduced. So, for thisa novel features selection metric CorrAUC is suggested. ...
Malicious, intrusion, and cyber-attacks in IOT networks can all be detected and categorized using machine learning methods. ...
doi:10.47392/irjash.2021.142
fatcat:pzmbtwjc2ffdplrxise6ubwa2u
RESEARCH OF MACHINE LEARNING BASED METHODS FOR CYBERATTACKS DETECTION IN THE INTERNET OF THINGS INFRASTRUCTURE
2022
Computer Systems and Information Technologies
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The paper presents an overview of modern methods aimed at detecting cyberattacks and anomalies in the Internet of Things using machine learning methods. ...
Advantages of applying the machine-based methods in comparison with signature analysis are the higher detection accuracy and fewer false positive, the possibility of detecting both anomalies and new features ...
The method includes four steps: (1) a new CorrAUC approach to select features that provide sufficient information to detect attack traffic; (2) based on CorrAUC, a new Corrauc feature selection algorithm ...
doi:10.31891/csit-2021-5-15
fatcat:xdunccobhveqxbuz4soyj6qdvm
Guest Editorial: Special Issue on AI-Enabled Internet of Dependable and Controllable Things
2021
IEEE Internet of Things Journal
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The article titled "CorrAUC: A malicious Bot-IoT traffic detection method in IoT network using machine-learning techniques" addresses the problem of detecting malicious Bot-IoT traffic and proposes a new ...
feature selection scheme to find effective features for accurate malicious traffic detection in IoT. ...
doi:10.1109/jiot.2021.3053713
fatcat:wnsgkuohhvg4fitk6ixreddsly
Feature Entropy Estimation (FEE) for Malicious IoT Traffic and Detection Using Machine Learning
2021
Mobile Information Systems
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Many machine learning (ML) algorithms proved their efficiency to detect intrusion in IoT networks. ...
Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for IoT security. ...
In the past few years, many researchers have implemented machine learning (ML) techniques to track and block malicious IoT traffic. ...
doi:10.1155/2021/8091363
fatcat:ygqhd6iwere6nnamjbln3soapy
Learning-Based Methods for Cyber Attacks Detection in IoT Systems: Methods, Analysis, and Future Prospects
2022
Electronics
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For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. ...
Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. ...
Machine Learning Methods Different types of machine learning methods have been used for the detection of malicious attacks in the literature. ...
doi:10.3390/electronics11091502
fatcat:stweql4ru5behg2scpumznzy6i
Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
2021
Sensors
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Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. ...
Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. ...
A wrapper-based FS method called, CorrAUC was developed by [29] for malicious traffic detection for IoT environments, using Bot-IoT datasets. ...
doi:10.3390/s22010140
pmid:35009682
pmcid:PMC8749550
fatcat:pm6ji2j7jzcnjna5wakwnavpxu
Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)
2022
Electronics
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Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. ...
A meta-heuristic optimizer was used in the future to increase the system's ability to forecast attacks. ...
These features are extracted according to the convolution neural network that avoids RSU attacks. Machine learning techniques are utilized in [41] to detect malicious bots in the IoT. ...
doi:10.3390/electronics11030494
fatcat:34eoknscrzh2pajqhpv2u63ijm
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks
[article]
2021
arXiv
pre-print
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module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. ...
And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs. ...
soft set technique to validate the features identified by CorrAUC, experimentally demonstrate that >96% results can be achieved used CorrAUC on the Bot-IoT dataset. ...
arXiv:2110.03445v1
fatcat:ellvvqpn4fbr7e2scyo3dkai3u
2021 Index IEEE Internet of Things Journal Vol. 8
2021
IEEE Internet of Things Journal
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
Note that the item title is found only under the primary entry in the Author Index. ...
., +, JIoT June 1, 2021 8657-8666 Entropy CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques. ...
doi:10.1109/jiot.2022.3141840
fatcat:42a2qzt4jnbwxihxp6rzosha3y
Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments
2021
Anais do XXI Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg 2021)
unpublished
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learning techniques. ...
Thus, this work proposes an approach with a two-stage analysis architecture based on One-Vs-All (OVA) and Artificial Neural Networks (ANN) to detect and identify intrusions in fog and IoT computing environments ...
The results obtained through experiments with the Bot-IoT intrusion dataset demonstrate that the approach achieved promising results compared to machine learning methods and reduced false positives compared ...
doi:10.5753/sbseg.2021.17308
fatcat:hcvcu5n3g5a4nbqbzbv73efc64