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 Long Short Term Memory Network-based Interference Recognition for Industrial Internet of Things

2020 ◽  
pp. 876-885
Author(s):  
Natthanan Promsuk ◽  
◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
Minimum Mean Square Error ◽  
Human Beings ◽  
Short Term ◽  
Term Memory ◽  
Industrial Iot ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Iot Devices

Nowadays, the rapid growth of wireless Internet of things (IoT) devices is one of the significant factors leading smart systems in various sectors, such as healthcare, education, and agriculture. This is, of course, not limited to the industrial sector, where the IoT concept is applied for real time monitoring and control of devices instead of human beings. Co-channel interferences occurs when two or more devices are using the same channel. It causes unnecessary contention as the devices will be forced to defer transmissions until the medium is clear causing a loss of throughput. Adjacent channel interference is even more serious and occurs when the devices are on overlapping channels causing corrupted data, which makes indispensable retransmissions. The more devices are added to an environment, the higher the likelihood of interference problem is. Due to a huge number of IoT devices, the interference issue becomes very serious. In this paper, a long short-term memory network-based interference recognition (LSTM-IR) is proposed. This method is integrated into the industrial IoT (IIoT) network in factory environments to mitigate the effect of interferences. The comparative results are done among three interference suppression techniques (IST) including the traditional minimum mean square error (MMSE) approach, the multi-layer perceptron (MLP), and the proposed LSTM-IR. Since the type of transmitting and receiving data is usually a sequencing data type. Therefore, the proposed method with the input data from a fast Fourier transform (FFT) algorithm provides better performances because it is based on an LSTM which is suitable for the sequences of data. The number of the devices in the factory is obviously the key factor because the smaller number of active devices causes less interferences.

scholarly journals Predicting Internet of Things (IOT) Security and Privacy Risks – A Proposal Model: توقع مخاطر أمن وخصوصية إنترنت الأشياء (IOT) – نموذج مقترح بحثي

2021 ◽  
Vol 5 (3) ◽  
pp. 133-112
Author(s):  
Awad Saad Al-Qahtani, Mohammad Ayoub Khan Awad Saad Al-Qahtani, Mohammad Ayoub Khan
Keyword(s):  
Internet Of Things ◽  
Security Management ◽  
Cyber Attacks ◽  
Security Requirements ◽  
Security And Privacy ◽  
The Internet ◽  
Security Risks ◽  
Iot Security ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of things (IOT) users lack awareness of IOT security infrastructure to handle the risks including Threats, attack and penetration associated with its use. IOT devices are main targets for cyber-attacks due to variable personally identifiable information (PII) stored and transmit in the cyber centers. The security risks of the Internet of Things aimed to damage user's security and privacy. All information about users can be collected from their related objects which are stored in the system or transferred through mediums among diverse smart objects and may exposed to exposed dangerous of attacks and threats if it lack authentication so there are essential need to make IOT security requirements as important part of its efficient implementation. These requirements include; availability, accountability, authentication, authorization, privacy and confidentiality, Integrity and Non-repudiation. The study design is a survey research to investigate the visibility of the proposed model of security management for IOT uses, the security risks of IOT devices, and the changes IOT technology on the IT infrastructure of IOT users through answering of the research questionnaires. This work proposes a model of security management for IOT to predict IOT security and privacy threats, protect IOT users from any unforeseen dangers, and determine the right security mechanisms and protocols for IOT security layers, as well as give the most convenient security mechanisms. Moreover, for enhancing the performance of IOT networks by selecting suitable security mechanisms for IOT layers to increase IOT user's security satisfaction.

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 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.

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhengqiang Ge ◽  
Xinyu Liu ◽  
Qiang Li ◽  
Yu Li ◽  
Dong Guo
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Short Term ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Term Memory ◽  
Protection Mechanism ◽  
Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

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