Distributed Architecture of an Intrusion Detection System in Industrial Control Systems

Abid, Ahlem; Farah, Joumana; Korbaa, Ouajdi

doi:10.1007/978-3-031-16210-7_39

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…The main idea of bagging is to train multiple models independently on different subsets of the training data, and then combine their predictions in some way to form a nal prediction. This can help reduce the variance of the ensemble and lead to more stable and accurate predictions [23].…”

Section: B Deep Neural Network Algorithmmentioning

confidence: 99%

Deep Learning for Intrusion Detection in IoT Networks

Selem,

Jemili,

Korbaa

2024

Preprint

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The rapid proliferation of Internet of Things (IoT) devices has transformed our daily lives, introducing innovations like smart homes, wearables, and advanced industrial automation. While these interconnected systems offer convenience and efficiency, they also present significant security challenges. With the expansion of the IoT network comes an increased risk of malicious attacks, making safeguarding these networks a pressing concern. Intrusion detection serves as a crucial defense mechanism, detecting abnormal activities and triggering appropriate responses. In our study, we harness the power of ensemble learning through a technique known as bagging. By combining the strengths of Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), we aim to capitalize on their unique advantages and enhance the overall capability of intrusion detection systems.

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Section: B Deep Neural Network Algorithmmentioning

confidence: 99%

Deep Learning for Intrusion Detection in IoT Networks

Selem,

Jemili,

Korbaa

2024

Preprint

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“…Abid et al [77] propose a novel method of using a distributed IDS, defining the challenges as a big data problem, and using machine learning to sift through both legitimate and attack data from disparate sources in a centralized cloud-based environment, then feeding the analyzed/classified data back to the distributed nodes in the IDS to be acted upon. The value proposition of this method is to provide the Machine Learning (ML) model with richer data sources than can be obtained from a single viewpoint on the network, thus improving the classification accuracy of the IDS.…”

Section: Using Ids/ips For Anomaly Detectionmentioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

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“…This step is about Mawilab data preprocessing using Spark on Azure HDInsight Spark cluster [21]. This step consists in [23]:…”

Section: Data Preprocessingmentioning

confidence: 99%

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

Jemili,

Jouini,

Korbaa

2023

Preprint

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Intrusions are constantly evolving and changing, and to keep up with these changes, it is necessary to have models that detect these changes, also known as concept drifts, and offer the ability to update the model without starting the learning process from scratch. In our contribution, we have opted for a new approach to intrusion detection based on concept drift detection and online incremental learning, named DDM-ORF. Our approach is based on the Detection Drift Method (DDM) and Online Random Forest algorithm (ORF). The model has shown very good accuracy compared to traditional approaches and an ability to handle massive data, providing multi-class classification that allows for determining insights. The proposed system achieves very good classification results, along with good processing speed that meets real-world scenarios. Apache Spark Structured Streaming provides important functionalities for dealing with streaming data and enables the deployment of the proposed system DDM-ORF in real-world applications.

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Distributed Architecture of an Intrusion Detection System in Industrial Control Systems

Cited by 9 publications

References 9 publications

Deep Learning for Intrusion Detection in IoT Networks

Deep Learning for Intrusion Detection in IoT Networks

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Intrusion Detection based on Concept Drift Detection & Online Incremental Learning

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Distributed Architecture of an Intrusion Detection System in Industrial Control Systems

Cited by 9 publications

References 9 publications

Deep Learning for Intrusion Detection in IoT Networks

Deep Learning for Intrusion Detection in IoT Networks

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Intrusion Detection based on Concept Drift Detection &amp; Online Incremental Learning

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Product

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Intrusion Detection based on Concept Drift Detection & Online Incremental Learning