scholarly journals Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

2021 ◽  
Author(s):  
Kathiroli Raja ◽  
Krithika Karthikeyan ◽  
Abilash B ◽  
Kapal Dev ◽  
Gunasekaran Raja
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Production Management ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Sensitive Information ◽  
Smart Meters ◽  
Network Intrusion

Abstract The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified,
1
stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

Application of Deep Learning Technique in an Intrusion Detection System

2020 ◽  
Vol 19 (02) ◽  
pp. 2050016
Author(s):  
Shideh Saraeian ◽  
Mahya Mohammadi Golchi
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Visual Analysis ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Hybrid Network ◽  
Machine Learning Techniques ◽  
Learning Technique

Comprehensive development of computer networks causes the increment of Distributed Denial of Service (DDoS) attacks. These types of attacks can easily restrict communication and computing. Among all the previous researches, the accuracy of the attack detection has not been properly addressed. In this study, deep learning technique is used in a hybrid network-based Intrusion Detection System (IDS) to detect intrusion on network. The performance of the proposed technique is evaluated on the NSL-KDD and ISCXIDS 2012 datasets. We performed traffic visual analysis using Wireshark tool and did some experimentations to prove the superiority of the proposed method. The results have shown that our proposed method achieved higher accuracy in comparison with other useful machine learning techniques.

scholarly journals Intrusion Detection System using Hybrid SVM-RF and SVM-DT in Wireless Sensor Networks

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1926-1931
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Hybrid Algorithm ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Support Vector ◽  
Second Stage ◽  
Network Intrusion

Intrusion detection system (IDS) is one of the essential security mechanisms against attacks in WSN. Network intrusion detection system (NIDS) generally uses the classification techniques in order to obtain the best possible accuracy and attack detection rate. In this paper, Intrusion Detection System is designed which uses two-stage hybrid classification method. In the first stage it uses Support Vector Machine (SVM) as anomaly detection, and in the second stage it uses Random Forest (RF)/Decision Tree (DT) as misuse. The abnormal activities are detected in the first stage. These abnormal activities are further analyzed and the known attacks are identified in the second stage and are classified as Denial of Service (DoS) attack, Probe attack, Remote to Local (R2L) attack and User to Root (U2R) attack. Simulation results reveal that the proposed hybrid algorithm obtains better accuracy and detection rate than the single classifier namely, SVM, RF and DT algorithm. The experimental results also shows that hybrid algorithm can detect anomaly activity in a reliable way. Proposed technique uses the standard NSL KDD dataset to evaluate/calculate the performance of the proposed approach. Here the results show that the proposed Hybrid SVM-RF/DT IDS technique performs better in terms of detection rate, accuracy and recall than the existing SVM, RF and DT approaches.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

scholarly journals Deep Learning Approaches for Intrusion Detection

2021 ◽  
pp. 50-64
Author(s):  
Azar Abid Salih ◽  
Siddeeq Y. Ameen ◽  
Subhi R. M. Zeebaree ◽  
Mohammed A. M. Sadeeq ◽  
Shakir Fattah Kak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Learning ◽  
Detailed Comparison ◽  
Learning Approaches ◽  
Network Intrusion ◽  
Research Problems

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

scholarly journals Design Of Intrusion Detection System For Dos Attack In 6lowpan And RPL Based IoT Network

2019 ◽  
Vol 8 (11) ◽  
pp. 3840-3844
Keyword(s):  
Intrusion Detection ◽  
Low Power ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Area Network ◽  
Dos Attack ◽  
Resource Constrained ◽  
Ipv6 Protocol

Internet of Things (IoT) is a network spread globally and accommodates maximum things under it. All these things are connected globally using IPv6 protocol which satisfies the need of connecting maximum devices by supporting 2^128 addresses. Because of heavy-weight nature of IPv6 protocol, a compressed version of it known as IPv6 Low Power Personal Area Network (6LoWPAN) protocol is used for a resource-constrained network that communicates over low power and lossy links. In IoT, devices are resource-constrained in terms of low battery power, less processing power, less transceiver power, etc. Also these devices are directly connected to insecure internet hence it is very challenging to maintain security in IoT network. In this paper, we have discussed various attacks on 6LoWPAN and RPL network along with countermeasures to reduce the attacks. DoS attack is one of the severe attacks in IoT which has various patterns of execution. Out of various attacks we have designed Intrusion Detection System (IDS) for Denial of Service (DOS) attack detection using Contiki OS and Cooja simulator.

scholarly journals An Autoencoder-Based Network Intrusion Detection System for the SCADA System

2021 ◽  
pp. 210-216
Author(s):  
Mustafa Altaha ◽  
◽  
Jae-Myeong Lee ◽  
Muhammad Aslam ◽  
Sugwon Hong
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Communication Protocol ◽  
Detection System ◽  
Main Tool ◽  
Detection Model ◽  
Scada System ◽  
Network Intrusion ◽  
Power Substation

The intrusion detection system (IDS) is the main tool to do security monitoring that is one of the security strategies for the supervisory control and data acquisition (SCADA) system. In this paper, we develop an IDS based on the autoencoder deep learning model (AE-IDS) for the SCADA system. The target SCADA communication protocol of the detection model is the Distributed Network Protocol 3 (DNP3), which is currently the most commonly utilized communication protocol in the power substation. Cyberattacks that we consider are data injection or modification attacks, which are the most critical attacks in the SCADA systems. In this paper, we extracted 17 data features from DNP3 communication, and use them to train the autoencoder network. We measure accuracy and loss of detection and compare them with different supervised deep learning algorithms. The unsupervised AE-IDS model shows better performance than the other deep learning IDS models.

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