Utilizing Blockchain for Distributed Machine Learning based Intrusion Detection in Internet of Things

Cheema, Muhammad Asaad; Qureshi, Hassaan Khaliq; Chrysostomou, Chrysostomos; Lestas, Marios

doi:10.1109/dcoss49796.2020.00074

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…Conclusions/findings 2020 [87] Journal SVM Ethereum IoT T he result is Evaluation Metrics (0.99), Accuracy (0.9998), Recall (1) and F1-Score (0.9998) 2020 [88] Journal Ordered Boosting Ethereum Ponzi Scheme On a real-world dataset, the new model obtains a 98 percent F-score, greatly outperforming existing techniques. 2020 [89] Journal RF Cryptocurrency Cryptojacking On our dataset, the BRENNT DROID tool can detect miners with 95 percent accuracy.…”

Section: Ye Ar Re Fe Rence Type Model Blockchain Applicationmentioning

confidence: 96%

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

2022

IJATEE

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Blockchain technology has changed the global trading of assets. A blockchain can be viewed as a connected ledger managed by a distributed peer-to-peer (P2P) network. Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others. These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) [3,4], and supply chain management (SCM) [5,6], among others. The increasing desire for technological advancements stimulated the development of BT.

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Section: Ye Ar Re Fe Rence Type Model Blockchain Applicationmentioning

confidence: 96%

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

2022

IJATEE

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“…BC is used to provide a distributed way to share data among multiple providers. In [18], the authors presented a distributed ML for intrusion detection in IoT network. The IoT network is divided into autonomous systems which are monitored for intrusion detection.…”

Section: Literlature Reviewmentioning

confidence: 99%

A Secure IoT Framework Based on Blockchain and Machine Learning

Shahin¹,

Sabri²

2022

IJCDS

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Internet of Things (IoT) network consists of many devices that communicate together and exchange data. IoT network has many applications especially in smart city and smart campus. IoT devices usually produce a huge amount of data that are stored in the cloud to be analyzed later. Data in general and IoT devices data in particular suffer from major security issues such as the availability and the integrity of data. Blockchain is a new technology that offers an interesting solution for the security of sensitive IoT data by protecting data against malicious tampering. However, the data stored in the blockchain cannot be altered, therefore, it should be validated before being stored in the blockchain especially that IoT devices are vulnerable to attacks. Machine learning algorithms are very useful to detect compromised IoT devices to ensure that only reliable data are stored in the blockchain.The main aim of this paper is to propose a framework for storing reliable IoT data in a secure way that preserves the integrity and availability properties. First, to detect compromised IoT devices, several machine learning algorithms are evaluated. The IoTID20 dataset for anomalous activity detection in IoT network is used to build our machine learning model. IoTID20 dataset contains 80 network features and 625783 records. For our experiment, 4000 records are selected randomly from the dataset. Two algorithms are used to select features namely Logistic Regression and Pearson's correlation. A total of 15 features are selected in order to classify packets as (Normal, Anomaly). Several machine learning algorithms are trained: Logistic Regression, Random Forest, Decision Tree, K Nearest Neighbors, AdaBoost, and Naïve Bayes. These algorithms are compared based on the following measurements: accuracy, precision, recall, F1 score, and the time for classification. The results reveal that Random Forest and AdaBoost classifiers give very close results and are considered the best classifiers based on all performance metrics used in the paper except the time. Regarding the time required to detect malicious records, Decision Tree and Naïve Bayes are the best as the time required to predict 4000 records is 0.015 seconds.A private blockchain is built to store normal data received from IoT devices after being filtered. The data is signed and then distributed to all nodes in the network for verification. To provide scalability, the consensus algorithm of the blockchain is based on the proof of authority algorithm. As a proof of concepts, the blockchain is developed using Python programming language and the time required to verify the signatures is computed.

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“…Thus, several works have been using machine learning algorithms to build Intrusion Detection Systems (IDS). Cheema et al [16] presented an intrusion detection system based on distributed machine learning using Blockchain technology, which divides the IoT network into autonomous systems. The classification technique -support vector machine (SVM) was trained using the datasets obtained from each of the nodes of the IoT network.…”

Section: Introductionmentioning

confidence: 99%

Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier

Souza

Medeiros

et al. 2022

Soft Comput

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The detection of intrusions in IoT networks is essential to maintain the availability and integrity of data transmitted and generated by devices connected to these networks. This is primarily when the data originates from critical activities, such as activities in the military, financial, industrial, and health sectors. Machine learning techniques have been adopted to create ways to detect or improve the accuracy of existing models for automatic intrusion detection. However, it is difficult to find in the literature an accurate intrusion detection technique in an IoT environment, as there are different types of attacks that can happen in different ways. Therefore, to solve this problem, this work proposes applying Fuzzy OPF (Optimum-Path Forest) as a new detection algorithm for any threat that escapes the regular traffic of an IoT network. We evaluate our proposed approach by using five different ML algorithms: Linear Discriminant Analysis, Support Vector Machine, Bayes, K-Nearest Neighbors, and Optimum-Path Forest. Experimental results analysis showed that our proposed model outperforms well-known algorithms in the literature regarding the Accuracy, Recall, and F1 metrics.

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Utilizing Blockchain for Distributed Machine Learning based Intrusion Detection in Internet of Things

Cited by 25 publications

References 22 publications

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

A Secure IoT Framework Based on Blockchain and Machine Learning

Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier

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