Anomaly-based intrusion detection system in IoT using kernel extreme learning machine

Bacha, Sawssen; Aljuhani, Ahamed; Abdellafou, Khawla Ben; Taouali, Okba; Liouane, Noureddine; Alazab, Mamoun

doi:10.1007/s12652-022-03887-w

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…Results showed a high accuracy in Binary classification. The prediction output was classified as either normal or attack BoT-IoT LSTM Binary classification: 98% C RNN Binary classification: 97% 26 2019 Authors provide a survey for security of IoT Networks – Neural Network 99% C–D 27 2022 Authors used kernel extreme learning machine (KELM) for classification to detect anomalies in IoT – KELM 99.40% C–D 28 2022 Authors aims at detecting intrusion in IIoT using classification and detection methods. They used XGBoost (eXtremely Gradient Boosting) model TON_IoT XGBoost 96.5% C–D 29 2021 Ullah et al proposed a DL model IDS based on a CNN for multicast and binary classifications – CNN 99.7% C–D 30 2022 Saba et al proposed a DL IDS model for anomaly-based IDS based on a CNN technique.…”

Section: State Of the Artmentioning

confidence: 99%

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Meziane,

Ouerdi

2023

Sci Rep

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Security is an important field in the Internet of Things (IoT) systems. The IoT and security are topical domains. Because it was obtained 35,077 document results from the Scopus database. Hence, the AI (Artificial Intelligence) has proven its efficiency in several domains including security, digital marketing, healthcare, big data, industry, education, robotic, and entertainment. Thus, the contribution of AI to the security of IoT systems has become a huge breakthrough. This contribution adopts the artificial intelligence (AI) as a base solution for the IoT security systems. Two different subsets of AI algorithms were considered: Machine Learning (ML) and Deep Learning (DL) methods. Nevertheless, it is difficult to determine which AI method and IoT dataset are best (more suitable) for classifying and/or detecting intrusions and attacks in the IoT domain. The large number of existing publications on this phenomenon explains the need for the current state of research that covers publications on IoT security using AI methods. Thus, this study compares the results regarding AI algorithms that have been mentioned in the related works. The goal of this paper is to compare the performance assessment of the existing AI algorithms in order to choose the best algorithm as well as whether the chosen algorithm can be used for classifying or/and detecting intrusions and attacks in order to improve security in the IoT domain. This study compares these methods in term of accuracy rate. Evaluating the current state of IoT security, AI and IoT datasets is the main aim for considering our future work. After that, this paper proposes, as result, a new and general taxonomy of AI techniques for IoT security (classification and detection techniques). Finally, the obtained results from this assessment survey that was dedicated to research conducted between 2018 and 2023 were satisfactory. This paper provides a good reference for researchers and readers in the IoT domain.

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Section: State Of the Artmentioning

confidence: 99%

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Meziane,

Ouerdi

2023

Sci Rep

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“…The Internet of Things (IoT) has revolutionized several critical domains such as transportation, healthcare, energy, and agriculture, by providing smart, cost-effective solutions [1,2]. The IoT is a promising technology that uses wireless communication technologies to connect various objects to transmit and receive data without the need for human involvement.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

Alenezi¹,

Aljuhani²

2023

Computer Systems Science and Engineering

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The rapid growth of the Internet of Things (IoT) in the industrial sector has given rise to a new term: the Industrial Internet of Things (IIoT). The IIoT is a collection of devices, apps, and services that connect physical and virtual worlds to create smart, cost-effective, and scalable systems. Although the IIoT has been implemented and incorporated into a wide range of industrial control systems, maintaining its security and privacy remains a significant concern. In the IIoT contexts, an intrusion detection system (IDS) can be an effective security solution for ensuring data confidentiality, integrity, and availability. In this paper, we propose an intelligent intrusion detection technique that uses principal components analysis (PCA) as a feature engineering method to choose the most significant features, minimize data dimensionality, and enhance detection performance. In the classification phase, we use clustering algorithms such as K-medoids and K-means to determine whether a given flow of IIoT traffic is normal or attack for binary classification and identify the group of cyberattacks according to its specific type for multi-class classification. To validate the effectiveness and robustness of our proposed model, we validate the detection method on a new driven IIoT dataset called X-IIoTID. The performance results showed our proposed detection model obtained a higher accuracy rate of 99.79% and reduced error rate of 0.21% when compared to existing techniques.

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“…Anomaly detection based on machine learning (ML) and deep learning (DL) techniques has been necessary and effective in detecting cyber-attacks in real time [7]. ML and DL methods can be used to determine whether the traffic flow is benign or malicious through binary classification, and also to classify the malicious attacks into groups of different attack types as a multi-classification task [8]. ML classifiers such as decision tree (DT), random forest (RF), and naïve Bayes (NB) can provide acceptable performance when the training and testing data share similar data distribution; however, when tested on new data with a different distribution, they fail to provide good results [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks

Alaghbari,

Lim,

Saad

et al. 2023

IoT

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The intrusion detection system (IDS) is a promising technology for ensuring security against cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and feature extraction are performed by two different models. In this paper, we propose a new integrated model based on deep autoencoder (AE) for anomaly detection and feature extraction. Firstly, AE is trained based on normal network traffic and used later to detect anomalies. Then, the trained AE model is employed again to extract useful low-dimensional features for anomalous data without the need for a feature extraction training stage, which is required by other methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). After that, the extracted features are used by a machine learning (ML) or deep learning (DL) classifier to determine the type of attack (multi-classification). The performance of the proposed unified approach was evaluated on real IoT datasets called N-BaIoT and MQTTset, which contain normal and malicious network traffics. The proposed AE was compared with other popular anomaly detection techniques such as one-class support vector machine (OC-SVM) and isolation forest (iForest), in terms of performance metrics (accuracy, precision, recall, and F1-score), and execution time. AE was found to identify attacks better than OC-SVM and iForest with fast detection time. The proposed feature extraction method aims to reduce the computation complexity while maintaining the performance metrics of the multi-classifier models as much as possible compared to their counterparts. We tested the model with different ML/DL classifiers such as decision tree, random forest, deep neural network (DNN), conventional neural network (CNN), and hybrid CNN with long short-term memory (LSTM). The experiment results showed the capability of the proposed model to simultaneously detect anomalous events and reduce the dimensionality of the data.

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Anomaly-based intrusion detection system in IoT using kernel extreme learning machine

Cited by 13 publications

References 34 publications

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks

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