Attack Detection in IoT using Machine Learning

Anwer, Mardin A.; Khan, Shariq Mahmood; Farooq, M.; Waseemullah,

doi:10.48084/etasr.4202

Cited by 38 publications

(19 citation statements)

References 35 publications

(32 reference statements)

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“…The results showed that the SLFN classification approach outperformed other classification algorithms. Maryam et al [75] proposed that three ML algorithms RF, GDBT, and SVM were applied to the NSL-KDD dataset using binary classification. The results showed that the RF obtained the highest accuracy on the fog layer while SVM obtained lowest accuracy.…”

Section: Analysis and Comparison Of Supervised ML Algorithms For Iot ...mentioning

confidence: 99%

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

Abdulla

Jameel

2023

UHD J SCI TECH

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Physical objects that may communicate with one another are referred to “things” throughout the Internet of Things (IoT) concept. It introduces a variety of services and activities that are both available, trustworthy and essential for human life. The IoT necessitates multifaceted security measures that prioritize communication protected by confidentiality, integrity and authentication services; data inside sensor nodes are encrypted and the network is secured against interruptions and attacks. As a result, the issue of communication security in an IoT network needs to be solved. Even though the IoT network is protected by encryption and authentication, cyber-attacks are still possible. Consequently, it’s crucial to have an intrusion detection system (IDS) technology. In this paper, common and potential security threats to the IoT environment are explored. Then, based on evaluating and contrasting recent studies in the field of IoT intrusion detection, a review regarding the IoT IDSs is offered with regard to the methodologies, datasets and machine learning (ML) algorithms. In This study, the strengths and limitations of recent IoT intrusion detection techniques are determined, recent datasets collected from real or simulated IoT environment are explored, high-performing ML methods are discovered, and the gap in recent studies is identified.

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Section: Analysis and Comparison Of Supervised ML Algorithms For Iot ...mentioning

confidence: 99%

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

Abdulla

Jameel

2023

UHD J SCI TECH

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“…Based on machine learning, Anwer et al [16] developed a framework for detecting attacks in network flow. Their framework used three widespread machine learning classifiers: a Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and RF.…”

Section: Anomaly-based Intrusion Detection Systems (Ads) Related Workmentioning

confidence: 99%

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Alshede¹,

Nassef²,

Alowidi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Advanced Metering Infrastructure (AMI) is the metering network of the smart grid that enables bidirectional communications between each consumer's premises and the provider's control center. The massive amount of data collected supports the real-time decision-making required for diverse applications. The communication infrastructure relies on different network types, including the Internet. This makes the infrastructure vulnerable to various attacks, which could compromise security or have devastating effects. However, traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks. The objective of this paper is to develop an Anomaly Detection System (ADS) based on deep learning using the CIC-IDS2017 dataset. However, this dataset is highly imbalanced; thus, a two-step sampling technique: random under-sampling and the Synthetic Minority Oversampling Technique (SMOTE), is proposed to balance the dataset. The proposed system utilizes a multiple hidden layer Auto-encoder (AE) for feature extraction and dimensional reduction. In addition, an ensemble voting based on both Random Forest (RF) and Convolutional Neural Network (CNN) is developed to classify the multiclass attack categories. The proposed system is evaluated and compared with six different state-ofthe-art machine learning and deep learning algorithms: Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGboost), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and bidirectional LSTM (biLSTM). Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy (98.29%), precision (99%), recall (98%), F 1 score (98%), and the UNDetection rate (UND) (8%).

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“…Further, this model is en-hanced with the Seagull Adopted Elephant Herding optimization (SAEHO) model to tune the weights for better detection. Anwer et al [4] evaluated the ML-based approaches for malicious traffic attack detection. It used three approaches Support vector machine (SVM), Gradient boosted decision trees (GB-DT), and Random forest (RF) for attack detection in IoT.…”

Section: Related Workmentioning

confidence: 99%

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

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Attack Detection in IoT using Machine Learning

Cited by 38 publications

References 35 publications

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

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