A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithm

2021 ◽  
Author(s):  
Robson V. Mendonça ◽  
Juan C. Silva ◽  
Renata L. Rosa ◽  
Muhammad Saadi ◽  
Demostenes Z. Rodriguez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Industrial Internet Of Things ◽  
Deep Learning Algorithm ◽  
Industrial Internet

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Abdelouahid Derhab ◽  
Arwa Aldweesh ◽  
Ahmed Z. Emam ◽  
Farrukh Aslam Khan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Feature Engineering ◽  
Training Time

In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time.

scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

scholarly journals Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

2020 ◽  
Vol 7 (2) ◽  
pp. 329
Author(s):  
Eka Lailatus Sofa ◽  
Subiyanto Subiyanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Routing Attacks

<p class="Abstrak"><em>Internet of Things</em> (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya <em>smart city, smart home, smart street, </em>dan<em> smart industry </em>yang memanfaatkan internet untuk memantau informasi yang dibutuhkan<em>.</em> Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh <em>routing attacks</em> yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> untuk menganalisis model <em>routing attacks</em> lainnya pada jaringan IoT. IDS menggunakan <em>compression header</em> 6LoWPAN sebagai fitur untuk <em>machine learning algorithm</em> dalam mempelajari jenis <em>routing attacks</em>. Skenario simulasi dikembangkan untuk mendeteksi <em>routing attacks</em> berupa <em>selective forwarding attack</em> dan <em>sinkhole attack</em>. Pengujian dilakukan menggunakan <em>feature selection</em> dan <em>machine learning algorithm</em>. <em>Feature selection</em> digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara <em>machine learning algorithm</em> digunakan untuk mengklasifikasikan <em>routing attacks</em> pada jaringan IoT. Ada tujuh <em>machine learning algorithm</em> yang digunakan dalam klasifikasi antara lain <em>Random Forest, Random Tree, J48, Bayes Net, JRip, SMO,</em> dan <em>Naive Bayes</em>. Hasil percobaan disajikan untuk menunjukkan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> dalam menganalisis <em>routing attacks</em>. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan <em>non-</em>serangan.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Internet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol  <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.</em><strong></strong></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals A Survey of Intrusion Detection Using Deep Learning in Internet of Things

2022 ◽  
pp. 83-93
Author(s):  
baraa I. Farhan ◽  
Ammar D.Jasim
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Accuracy Measurement ◽  
Malicious Behavior ◽  
Normal Behavior ◽  
Secure Network ◽  
Real Traffic

The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.

scholarly journals Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Joseph Bamidele Awotunde ◽  
Chinmay Chakraborty ◽  
Abidemi Emmanuel Adeniyi
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Suggested Method ◽  
Sensitive Information ◽  
Industrial Internet Of Things ◽  
Rule Based ◽  
Network Intrusion ◽  
Industrial Internet

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

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