An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

Kasongo, Sydney Mambwe

doi:10.1109/access.2021.3104113

Cited by 93 publications

(72 citation statements)

References 48 publications

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“…Compared with Ludwig [21], our proposed approach enhanced the accuracy rate by 7.09% while significantly reducing the FPR by 14.31%. Our anomaly-based IDS method also outperformed a recently proposed method by Kasongo [19], who used the GA algorithm for feature selection and the RF model as the detection model. Our method improved the accuracy rate by 11.97%.…”

Section: Resultsmentioning

confidence: 78%

“…The proposed work employed two feature selection techniques to reduce data dimensionality, improve computational resources, and improve detection performance. We used a set of machine learning techniques in the classification phase to determine whether a given flow of traffic was Ludwig [21] 92.49% 14.71% Kasongo [19] 87.61% N/A Muna et al [17] 98.6% 1.8% Ali et al [34] 99.54% N/A Awotunde et al [22] 98.9% 1.1% Proposed Method 99.58% 0.4% normal or an attack. We evaluated, analyzed, and validated the proposed work using the X-IIoTID, the most recent and comprehensive data set for IIoT environments.…”

Section: Discussionmentioning

confidence: 99%

“…Their developed approach achieved an overall accuracy in detection anomalies with 99.71%. A study by [19] proposed an IDS for IIoT implemented for feature selection using genetic algorithm. Their model includes several classification techniques such as linear regression, NB, DT, ET, Extreme Gradient Boosting (EGB), and RF.…”

Section: Related Workmentioning

confidence: 99%

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Anomaly Detection for Industrial Internet of Things Cyberattacks

Alanazi¹,

Aljuhani²

2023

Computer Systems Science and Engineering

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The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates' feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.

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Section: Resultsmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

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Anomaly Detection for Industrial Internet of Things Cyberattacks

Alanazi¹,

Aljuhani²

2023

Computer Systems Science and Engineering

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“…One of the areas in which ML has been successfully applied to S&P is intrusion (anomaly, signature or hybrid) detection. ML can utilize the enormous amount of data generated by the IoT devices to identify inherent patterns and behaviours of the data (establish a norm in the system) and hence predict and detect vulnerabilities/threats/attacks (deviations from established norm/alien patterns and behaviour) in IoTbased systems; thereby flagging new trends of attacks (Hussain et al, 2020;Thamilarasu et al, 2020;Kasongo, 2021). Traditional means usually involve human intervention to set rules to which the system will live by to address threats that may arise.…”

Section: *Corresponding Authormentioning

confidence: 99%

Machine Learning Intrusion Detection as a Solution to Security and Privacy Issues in IoT: A Systematic Review

Darley¹,

Adenowo²,

Yussuff³

2022

FUOYEJET

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Billions of IoT devices are in use worldwide and generate a humongous amount of data for the IoT system. This continuous stream of data is open to attack during its collection, transportation, processing, dissemination and storage cycle. Also, IoT devices themselves are points of system vulnerability through which the system can be attacked. Machine learning (ML), due to its ability to identify inherent patterns and behaviour in data, has been applied by many researchers to IoT data such that strange patterns or intrusions into IoT systems can be speedily detected and real-time decisions on security and privacy (S&P) protection implemented in a timely manner. Different ML techniques with their different algorithms have provided solutions in various scenarios such that security and privacy requirements for the IoT system can be met. In particular, ML has been successfully applied in intrusion detection and has been shown to perform better than traditional means in flagging new trends of attacks. This paper presents a systematic literature review on ML intrusion detection in IoT. Academic journals from 2011 to 2021 from two databases (IEEE and Proquest) were explored using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A review of the final selected papers revealed that data preprocessing, feature extraction, model training and deployment of ML-based Intrusion Detection Systems (IDS) increase computational complexity resulting in greater resource requirement (CPU, memory, and energy); enable ML to be used in the execution of adversarial attacks on IoT devices and networks; require trade-offs between detection accuracy and false-positive events; and highlight the superior performance of deep learning methods over traditional ML ones in anomaly detection. Generally, the changing nature of attacks makes it difficult for any particular IDS to be able to detect all attack types thus making the development of IDS a continuing project.

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“…The IIoT has the potential to boost efficiency, production, and operational efficiency in a variety of sectors. The existing services are initially improved by IIoT with the ultimate goal of creating entirely intelligent and enhanced services and products [6][7][8][9][10]. This has enabled most of the organization to grab knowledge about how and where IIoT innovations and solutions have to lead to transformations in the organization, enhanced goods, and services quality.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation

Behera

Mohanty

Pattanayak

2022

IJEER

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Industrial Internet of Things (IIoT) is a new field of study that connects digital devices and services to physical systems. The IIoT has been utilized to create massive amounts of data from various sensors, and it has run into several problems. The IIoT has been subjected to a variety of hacks, putting its ability to provide enterprises with flawless operations in jeopardy. Businesses suffer financial and reputational losses as a result of such threats, as well as the theft of critical data. As a result, numerous Network Intrusion Detection Systems (NIDSs) have been created to combat and safeguard IIoT systems, but gathering data that can be utilized in the construction of an intelligent NIDS is a tough operation; consequently, identifying current and new assaults poses major issues. In this research work, a novel IIOT attack detection framework and mitigation model is designed by following four major phases “(a) pre-processing, (b) feature extraction, (c) feature selection and (d) attack detection”. Initially, the collected raw data (input) is subjected to pre-processing phase, wherein the data cleaning and data standardization operations take place. Subsequently, the features like “higher-order statistical features (Skewness, Kurtosis, Variance and Moments), technical indicator based features, mutual information, Improved Principal Component Analysis (IPCA)” based features are extracted from the pre-processed data. Further, from the extracted features, the most optimal features are selected using a new hybrid optimization model referred as Hunger Customized Individual Activity Model (HCIA) that hybrids the concepts of standard (Teamwork Optimization Algorithm (TOA) and Hunger Games Search (HGS)). The attack detection is carried out using the projected deep fusion model framework that encapsulates the Bi-GRU and Quantum Deep Neural Network (QDNN), respectively. The Bi-GRU and QDNN in the deep fusion model framework is trained with the optimal features selected using a new hybrid optimization model. The outcome acquired from Bi-GRU and QDNN is combined, and it will be the final detected outcome that portrays the presence/ absence of attacks in IIoT network. When an attack is being identified, the mitigation of such attack takes place via the Improved BIAT Framework. Further, the projected model is evaluated over the existing models to show its supremacy in the attack detection and mitigation process.

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An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

Cited by 93 publications

References 48 publications

Anomaly Detection for Industrial Internet of Things Cyberattacks

Anomaly Detection for Industrial Internet of Things Cyberattacks

Machine Learning Intrusion Detection as a Solution to Security and Privacy Issues in IoT: A Systematic Review

A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation

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