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

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7016
Author(s):  
Muhammad Almas Khan ◽  
Muazzam A. Khan ◽  
Sana Ullah Jan ◽  
Jawad Ahmad ◽  
Sajjad Shaukat Jamal ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Short Term Memory ◽  
Detection System ◽  
Binary Classification ◽  
Smart Devices ◽  
Man In The Middle ◽  
Different Types ◽  
Abstract Level ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

scholarly journals Deep Learning Algorithmic Approach for Operational Anomaly Based Intrusion Detection System in Wireless Sensor Networks

2021 ◽  
Author(s):  
Ashwini Bhaskar Abhale ◽  
S S Manivannan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Short Term ◽  
Term Memory ◽  
Deep Convolution Neural Network ◽  
Long Short Term Memory

Abstract Because of the ever increasing number of Internet users, Internet security is becoming more essential. To identify and detect attackers, many researchers utilized data mining methods. Existing data mining techniques are unable to provide a sufficient degree of detection precision. An intrusion detection system for wireless networks is being developed to ensure data transmission security. The Network Intrusion Detection Algorithm (NIDS) uses a deep classification system to classify network connections as good or harmful. Deep Convolution Neural Network (DCNN), Deep Recurrent Neural Network (DRNN), Deep Long Short-Term Memory (DLSTM), Deep Convolution Neural Network Long Short-Term Memory (DCNN LSTM), and Deep Gated Recurrent Unit (DGRU) methods that use NSLKDD data records to train models are proposed. The experiments were carried out for a total of 1000 epochs. During the experiment, we achieved a model accuracy of more than 98 percent. We also discovered that as the number of layers in a model grows, so does the accuracy.

scholarly journals Intrusion detection systems using long short-term memory (LSTM)

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
FatimaEzzahra Laghrissi ◽  
Samira Douzi ◽  
Khadija Douzi ◽  
Badr Hssina
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Principal Component ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

AbstractAn intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and multiclass classification.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

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