A New Ensemble-Based Intrusion Detection System for Internet of Things

Abbas, Adeel; Khan, Muazzam A.; Latif, Shahid; Ajaz, Maria; Shah, Awais Aziz; Ahmad, Jawad

doi:10.1007/s13369-021-06086-5

Cited by 95 publications

(30 citation statements)

References 53 publications

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“…The rapid development in the fields of the Internet of Things (IoT) and artificial intelligence (AI) has paved the way for improvements in various fields of life [ 3 , 4 ]. Over the past few decades, these technologies have also transformed the healthcare sector by lowering costs and increasing efficiency.…”

Section: Introductionmentioning

confidence: 99%

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Shah

Larijani

Gibson

et al. 2022

Sensors

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Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.

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Section: Introductionmentioning

confidence: 99%

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Shah

Larijani

Gibson

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…It provides an effective method for combining the outputs of the ML model to achieve a high accuracy score [32,33]. In Initialize the specific window size W i…”

Section: Ensemblermentioning

confidence: 99%

A federated learning framework for cyberattack detection in vehicular sensor networks

Driss

Almomani

Huma

et al. 2022

Complex Intell. Syst.

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Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.

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“…Table 2 lists the features that have a score for them. PacketLengthVariance 0.532 [26], [28], [29], [32], [33] 8 AveragePacketSize 0.81 [25], [26], [28], [29], [33], [34]…”

Section: Data Pre-processing and Feature Selectionmentioning

confidence: 99%

Fast and accurate classifying model for denial-of-service attacks by using machine learning

Kareem

Jasim

2022

Bulletin EEI

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A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.

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A New Ensemble-Based Intrusion Detection System for Internet of Things

Cited by 95 publications

References 53 publications

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

A federated learning framework for cyberattack detection in vehicular sensor networks

Fast and accurate classifying model for denial-of-service attacks by using machine learning

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