Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Vaiyapuri, Thavavel; Sbaï, Zohra; Alaskar, Haya; Alaseem, Nourah

doi:10.14569/ijacsa.2021.0120411

Cited by 21 publications

(18 citation statements)

References 35 publications

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“…But it failed to apply the efficient machine learning technique for improving the accuracy of attack detection. A deep learning-based IDS method was developed in [15] for IIoT. However, securing IIoT network from various kinds of malicious activities was considered.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Network Vulnerability Analysis in IIoT

Sudhakar¹,

Senthilkumar²

2023

Computer Systems Science and Engineering

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Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the network traffic features are collected from the dataset. Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption. After the significant features selection, classification is performed using the Jaccardized Rocchio Emphasis Boost technique. Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output. Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack. Thus, proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error. This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage. Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy, precision, recall, F-measure and attack detection time. The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach for Network Vulnerability Analysis in IIoT

Sudhakar¹,

Senthilkumar²

2023

Computer Systems Science and Engineering

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“…First, a sigmoid layer is implemented that sets the output of the cell state based on the values received. Equation (5). represents this operation.…”

Section: Figure 1 Simple Recurrent Neural Network With Loops Characte...mentioning

confidence: 99%

“…The wide range of sensors in the IIoT network creates a significant volume of data, which has caught the interest of hackers worldwide. When it comes to protecting IIoT applications from cyberattacks, the intrusion detection system (IDS), which monitors network traffic and identifies network behavior, is regarded as one of the most important security measures [5]. As the IoT network technology continues to advance, cyber-attack detection measures are becoming more important in assuring the security of IoT networks.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of RNN Anomaly Detection Model for IoT Networks

Ullah

Mahmoud

2022

IEEE Access

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Cybersecurity is important today because of the increasing growth of the Internet of Things (IoT), which has resulted in a variety of attacks on computer systems and networks. As the number of various IoT devices and services grows, cyber security will become an increasingly difficult issue to manage. Malicious traffic identification using deep learning techniques has emerged as a key component of networkbased intrusion detection systems (IDS). Deep learning methods have been a research focus in network intrusion detection. A recurrent neural network is useful in a wide range of applications. This paper proposes a novel deep learning model for detecting anomalies in IoT networks using recurrent neural networks. The proposed model is implemented in IoT networks utilizing LSTM, BiLSTM, and GRU-based approaches for anomaly detection. A convolutional neural network can analyze input features without losing important information, making them particularly well suited for feature learning. In addition, we propose a hybrid deep learning model based on convolutional and recurrent neural networks. Finally, employing LSTM, BiLSTM, and GRU-based techniques, we propose a lightweight deep learning model for binary classification. The proposed deep learning models are validated using NSLKDD, BoT-IoT, IoT-NI, MQTT, MQTTset, IoT-23, and IoT-DS2 datasets. Our proposed binary and multiclass classification model achieved high accuracy, precision, recall, and F1 score compared to current deep learning implementations.

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“…From Figure 2 , the identification of industrial IoT intrusion by this model mainly includes the following three steps: Data preprocessing: build an industrial IoT environment and capture real-time network data, including source address, target address, connection attributes, and other relevant information [ 24 , 25 ]. The data are preprocessed and transformed into a format that can be processed by the stacked noise reduction convolutional autoencoder.…”

Section: Intrusion Detection Model Of Iiotmentioning

confidence: 99%

Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder

Zhang

2022

Computational Intelligence and Neuroscience

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With the gradual advancement of informatization and industrialization, the safety and controllability of industrial Internet of things (IoT) have attracted more and more attention. Aiming to improve the security of industrial IoT, a detection method using stacked sparse autoencoder network model is proposed. In this method, the basic units of the network model have been simplified and sparse, and some of basic features are combined with obtaining a higher-level abstract expression, so as to solve the problem of unbalanced network traffic data. The cascaded network structure is adopted to stack its sparse autoencoder network model, so as to improve the data ability of the detection model. In addition, the incorporation of Softmax classifier realizes the dynamic adjustment and optimization of the whole network parameters, which further ensures the efficiency of the detection method. The simulation experiment is based on NSL-KDD dataset. The experiment has proved that the proposed method has excellent network attack identification and detection performance. Its accuracy index is about 95.42%, and the detection time is about 3.42 s.

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Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Cited by 21 publications

References 35 publications

A Novel Approach for Network Vulnerability Analysis in IIoT

A Novel Approach for Network Vulnerability Analysis in IIoT

Design and Development of RNN Anomaly Detection Model for IoT Networks

Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder

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