Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

Thomas, Laiby; Bhat, Sudhindra

doi:10.47992/ijmts.2581.6012.0172

Cited by 13 publications

(17 citation statements)

References 47 publications

(44 reference statements)

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“…The simulation is performed in Orange data analytics tool [14] on a machine of 8GB RAM and a Core-i3 processor. The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Resultsmentioning

confidence: 99%

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Bhoi

2022

IJAEML

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Purpose: Identification of Internet of Thing (IoT) devices in smart home is the most important function for a local server/controller to administer and control the home smoothly. The IoT devices continuously send and receive requests, acknowledgments, packets, etc. for efficient data communication and these communication patterns need to be classified. Design/Methodology/Approach: Therefore, to run the smart home smoothly, in this work a framework using cloud computing is proposed to identify the correct IoT device communicating with the local server based on supervised machine learning. The best supervised machine intelligence model will be installed at the local server to classify the devices on the basis of data communication patterns. Findings/Result: Simulation is performed using Orange 3.26 data analytics tool by considering an IoT devices data communication dataset collected from Kaggle data repository. From the simulation results it is observed that Random Forest (RF) shows better performance than existing supervised machine learning models in terms of classification accuracy (CA) to classify the IoT devices with high accuracy. Originality/Value: A cloud based framework is proposed for a smart home to identify the correct IoT device communicating with the local server based on supervised machine learning. Based on the data communication pattern of the IoT devices, an IoT device is accurately identified. Paper Type: Methodology Paper.

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Section: Resultsmentioning

confidence: 99%

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

Bhoi

2022

IJAEML

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“…detection(knowledge-based) [5], Anomaly detection (behavior-based) [6], and Hybrid detection are the different detection types of IDSs. An intrusion prevention system (IPS) [7] is used to keep off intruders.…”

Section: Signaturementioning

confidence: 99%

A Distributed Intrusion Detection System using Machine Learning for IoT based on ToN-IoT Dataset

Gad¹,

Haggag²,

Nashat³

et al. 2022

IJACSA

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The internet of things (IoT) is a collection of common physical things which can communicate and synthesize data utilizing network infrastructure by connecting to the internet. IoT networks are increasingly vulnerable to security breaches as their popularity grows. Cyber security attacks are among the most popular severe dangers to IoT security. Many academics are increasingly interested in enhancing the security of IoT systems. Machine learning (ML) approaches were employed to function as intrusion detection systems (IDSs) to provide better security capabilities. This work proposed a novel distributed detection system based on machine ML approaches to detect attacks in IoT and mitigate malicious occurrences. Furthermore, NSL-KDD or KDD-CUP99 datasets are used in the great majority of current studies. These datasets are not updated with new attacks. As a consequence, the ToN-IoT dataset was used for training and testing. It was created from a large-scale, diverse IoT network. The ToN-IoT dataset reflects data from each layer of the IoT system, such as cloud, fog, and edge layer. Various ML methods were tested in each specific partition of the ToN-IoT dataset. The proposed model is the first suggested model based on the collected data from the same IoT system from all layers. The Chi2 technique was used to pick features in a network dataset. It reduced the number of features to 20.Another feature selection tool employed in the windows dataset was the correlation matrix, which was used to extract the most relevant features from the whole dataset. To balance the classes, the SMOTE method was used. This paper tests numerous ML approaches in both binary and multi-class classification problems. According to the findings, the XGBoost approach is superior to other ML algorithms for each node in the suggested model.

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“…The AI technology has many algorithms for solving the classification, regression, and clustering problems. The algorithms mostly used in machine learning (ML) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are supervised, unsupervised, and hybrid. So, ML is also an important component of the proposed design where the classifier installed in the cloud will detect the actual disease.…”

Section: Introductionmentioning

confidence: 99%

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

Bhoi¹,

K²,

Thinakaran³

2022

STAIQC

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Rice is an important food in our day-to-day life. It has rich sources of carbohydrates that are highly essential for body growth and development. Rice is an important crop in agriculture, where it enhances a country’s economy. However, if rice plants arediseased and not monitored regularly then the crop in the field is wasted and it reduces the proper production rate. Therefore, there should be a mechanism which regularly monitors the crop in a field to detect any disease to rice plant. In this paper, a framework is proposed for identification of rice leaf disease using IoT based Unmanned Aerial Vehicle (UAV) system. Here, the UAV monitors an entire field, capture the images and sends the images to the machine intelligent cloud for detection of rice leaf diseases. The cloud is installed with a proposed stacking classifier that classify the diseased rice plant images received from UAV into different categories. The dataset of these rice leaf diseases is collected from Kaggle source. The performance of the stacking classifier installed at the cloud is evaluated using Python based Orange 3.26 tool. It is observed form the results that stacking classifier outperforms the conventional machine learning models in detecting the actual disease with a classification accuracy (CA) of 86.7%.

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Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

Cited by 13 publications

References 47 publications

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

A Distributed Intrusion Detection System using Machine Learning for IoT based on ToN-IoT Dataset

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

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