scholarly journals Using deep learning to detecting abnormal behavior in internet of things

2022 ◽  
Vol 12 (2) ◽  
pp. 2108
Author(s):  
Mohammed Al-Shabi ◽  
Anmar Abuhamdah
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
High Accuracy ◽  
Abnormal Behavior ◽  
Short Term ◽  
Rapid Pace ◽  
Long Short Term Memory ◽  
The Internet Of Things

<span lang="EN-US">The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.</span>

scholarly journals Fake News Detection using RNN-LSTM

2021 ◽  
Vol 9 (10) ◽  
pp. 1731-1737
Author(s):  
Samarth Mengji
Keyword(s):  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Principal Factor ◽  
Online Media ◽  
Fake News ◽  
Short Term ◽  
Web Based ◽  
Personal Level ◽  
Long Short Term Memory

Abstract: Fake news distribution is a social phenomenon that can't be avoided on a personal level or through web-based social media like Facebook and Twitter. We're interested in counterfeit news because it's one of many sorts of double dealing in online media, but it's a more severe one because it's designed to deceive people. We're concerned about this now that we've seen what's going on. We are concerned about this issue because we have seen how, through the usage of social correspondence, this marvel has recently caused a shift in the direction of society and people groupings, as well as their opinions. Along these lines, we chose to confront and decrease this wonder, which is as yet the principal factor to pick a large portion of our choices. Our objective in this study is to develop a detector that can predict if a piece of news is false based just on its content, and then attack the problem using RNN method models LSTMs and Bi-LSTMs to tackle the problem from a basic deep learning viewpoint. Keywords: RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), Fake news detection, Deep learning

scholarly journals Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hasan Alkahtani ◽  
Theyazn H. H. Aldhyani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

scholarly journals Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 113-121
Author(s):  
Firman Pradana Rachman
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

scholarly journals Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Hasan Alkahtani ◽  
Theyazn H. H. Aldhyani
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Short Term ◽  
Security Risks ◽  
Optimal Accuracy ◽  
Long Short Term Memory ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.

scholarly journals Intelligent Air Pollution Prediction System using Internet of Things (Iot)

2019 ◽  
Vol 9 (1) ◽  
pp. 402-412
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Short Term Memory ◽  
Who Guidelines ◽  
Short Term ◽  
Air Contamination ◽  
Long Short Term Memory ◽  
The Internet Of Things ◽  
Air Pollution Prediction ◽  
New Framework

The internet of Things (IoT) is a path of action interconnected computes multiple procedures, mechanical along with sophisticated machines, things, and individuals to facilitate be certain remarkable identifiers and the ability of trade data over a framework lacking foreseeing human to human and human to machine correspondence, in these paper, an Internet of Things base framework is proposed, in favor of observing natural air contamination and forecast. This framework is able to exist used for observing air contaminations of specific zone and toward Air Quality examination just as gauging the air quality. We Proposed new framework resolve concentrate scheduled the observing of air contaminations, using the blend of IoT with Artificial Intelligence called Artificial Neural Network, and additional explicitly Long Short Term Memory (LSTM). The point in this paper is to discover the best expectation and prediction model for rise or fall of the specific air poisons like O3 , NO2 , SO2 , and CO which are altogether viewed as destructive as indicated by WHO guidelines.

Sign in / Sign up

Export Citation Format

Share Document