A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

Khan, Muhammad Almas; Khan, Muazzam A.; Jan, Sana Ullah; Ahmad, Jawad; Jamal, Sajjad Shaukat; Shah, Awais Aziz; Pitropakis, Nikolaos; Buchanan, William J.

doi:10.3390/s21217016

Cited by 84 publications

(53 citation statements)

References 52 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

“…The CNN allows the model both time and space correlations for better performance. NSL-KDD 86.00 [25] UNSW-NB 15 88.00 [17] KDD Cup 99, CIC IDS-2017 92.5 [29] BoT-IoT 94.00 [30] MQTT-IoT-IDS2020 97.…”

Section: Resultsmentioning

confidence: 99%

Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset

Jose

2023

IJECE

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Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset.

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“…It was trained using the training dataset, and its hyperparameters were adjusted using the Adam optimizer and the validation dataset. The CNN-LSTM model was next implemented on the test dataset, including features of each testing record to its real class: normal or a particular class of attack [ 67 ]. The training and optimization processes of the CNN-LSTM model consisted of two one-dimensional convolution layers that cross the input vectors with 32 filters and a kernel size of 4, two fully connected dense layers composed of 256 hidden neurons, and an output layer that applies the nonlinear SoftMax activation function used for multiclass classification tasks.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

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With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment.

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A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

Cited by 84 publications

References 52 publications

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

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