Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review

Kheddar, Hamza; Himeur, Yassine; Awad, Ali Ismail

doi:10.1016/j.jnca.2023.103760

Cited by 37 publications

(4 citation statements)

References 84 publications

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“…Our scheme demonstrated promising results in evaluation metrics, by exhibiting the effectiveness of our IDS in detecting and classifying attacks on in-vehicle networks compared to the proposed methods. For future work, we will investigate the potential of collaborative learning, federated learning, and transfer learning within domain transfer learning-based intrusion detection systems (DTL-IDSs) to enhance the efficiency of security threat prevention and detection [14].…”

Section: Discussionmentioning

confidence: 99%

“…IDSs are highly suitable for detecting anomalies in common networks due to their essential features and capabilities. Furthermore, the integration of IDS is crucial in order to detect and prevent malicious activities automatically, as highlighted in [14]. These systems analyze network traffic and security records, and audit data to find potential security breaches.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for an In-Vehicle Network

Alalwany,

Mahgoub

2024

Electronics

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The emergence of connected and autonomous vehicles has led to complex network architectures for electronic control unit (ECU) communication. The controller area network (CAN) enables the transmission of data inside vehicle networks. However, although it has low latency and enjoys data broadcast capability, it is vulnerable to attacks on security. The lack of effectiveness of conventional security mechanisms in addressing these vulnerabilities poses a danger to vehicle safety. This study presents an intrusion detection system (IDS) that accurately detects and classifies CAN bus attacks in real-time using ensemble techniques and the Kappa Architecture. The Kappa Architecture enables real-time attack detection, while ensemble learning combines multiple machine learning classifiers to enhance the accuracy of attack detection. The scheme utilizes ensemble methods with Kappa Architecture’s real-time data analysis to detect common CAN bus attacks. This study entails the development and evaluation of supervised models, which are further enhanced using ensemble techniques. The accuracy, precision, recall, and F1 score are used to measure the scheme’s effectiveness. The stacking ensemble technique outperformed individual supervised models and other ensembles with accuracy, precision, recall, and F1 of 0.985, 0.987, and 0.985, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for an In-Vehicle Network

Alalwany,

Mahgoub

2024

Electronics

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“…Design and develop intrusion detection systems (IDS) specifically tailored for agricultural networks [236]- [239]. Create IDS algorithms and models capable of detecting and mitigating attacks targeting IoT devices, sensor networks, and communication channels in smart agriculture systems.…”

Section: Intrusion Detection Systems For Agricultural Networkmentioning

confidence: 99%

An extensive survey of smart agriculture technologies: Current security posture

Otieno¹

2023

World J. Adv. Res. Rev.

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Smart agriculture, enabled by advanced technologies such as internet of things, artificial intelligence and data analytics, offers immense potential for optimizing farming practices and increasing agricultural productivity. However, as the adoption of smart agriculture systems continues to grow, it brings forth various security issues that need to be addressed to protect farming operations, data integrity, and privacy. This paper provides an overview of the security issues in smart agriculture, including vulnerabilities in internet of things devices, lack of standardized security protocols, limited security awareness among farmers, and challenges in securing data and communication networks. In addition, it highlights the need for robust security measures to mitigate risks such as unauthorized access, data breaches, and disruption of operations. Moreover, emphasis should be laid on the importance of collaborative efforts between technology providers, agricultural stakeholders, researchers, and policymakers to develop effective security solutions and standards that ensure the trustworthiness and resilience of smart agriculture systems.

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“…The objective is to provide preemptive intervention, guaranteeing the dependability of the network and mitigating any interruptions in service. [11][12][13][14][15] This research specifically focuses on the utilization of machine learning methods, including a wide range of techniques such as clustering, classification, and time-series analysis, in order to effectively identify abnormalities in electric car charging stations. The scope comprehensively embraces several charging circumstances, taking into account elements such as location, power rating, and user behavior.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Anomaly Detection in Electric Transportation Networks

Usanova,

Rani,

Mishra

et al. 2024

E3S Web Conf.

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This study introduces a sophisticated anomaly detection system based on machine learning. The system is specifically developed to enhance the dependability and safeguard the security of electric transportation networks, with a particular emphasis on the charging infrastructure for electric vehicles (EVs). Utilizing extensive datasets, the research examines several facets of charging stations, charging records, identified abnormalities, and following maintenance measures. The examination of the charging station demonstrates the system’s versatility in accommodating many charging circumstances, as seen by the range of power ratings, consumption patterns, and energy provided. Further examination of charging records provides comprehensive understanding of individual charging sessions, enabling the detection of irregularities such as atypical energy surges and extended charging durations. The machine learning system, having been trained and verified using this data, has a commendable degree of precision in identifying anomalies, as shown by the congruence between anticipated abnormalities and real results. The maintenance and repair measures carried out in reaction to identified abnormalities highlight the practical ramifications of the system, with proactive tactics utilized to reduce downtime and enhance charging station operations. The performance measures, including accuracy, recall, and F1 score, unequivocally validate the resilience of the anomaly detection system, guaranteeing precise identification while mitigating the occurrence of false positives and negatives. The seamless incorporation of machine learning into electric transportation networks, as shown by the results, not only amplifies the dependability and safeguarding of EV charging infrastructure but also establishes the system as an invaluable instrument for practical implementations. The research, in addition to offering a thorough examination of the system’s performance, elucidates forthcoming avenues for scalability, real-time monitoring, and interpretability, thereby making a valuable contribution to the wider discussion on the revolutionary capabilities of machine learning in the ever-changing realm of electric transportation.

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Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review

Cited by 37 publications

References 84 publications

An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for an In-Vehicle Network

An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for an In-Vehicle Network

An extensive survey of smart agriculture technologies: Current security posture

Machine Learning for Anomaly Detection in Electric Transportation Networks

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