☆ 4.7 Article

Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Related references

Note: Only part of the references are listed.

Article Engineering, Civil

A Privacy-Preserving-Based Secure Framework Using Blockchain-Enabled Deep-Learning in Cooperative Intelligent Transport System

Randhir Kumar et al.

Summary: This paper presents a secure framework based on blockchain and deep learning modules to provide privacy and security in C-ITS infrastructure. The framework provides two levels of security and privacy, with a blockchain module for secure data transmission and a deep-learning module for encoding data to prevent inference attacks.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Add to Collection

Article Computer Science, Information Systems

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Yang Zhao et al.

Summary: This study introduces a federated learning system leveraging a reputation mechanism to assist home appliance manufacturers in developing a smart home system. The system involves two stages: centralized training of customer data and model sharing among manufacturers through a reputation-based mechanism.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Information Systems

Omni SCADA Intrusion Detection Using Deep Learning Algorithms

Jun Gao et al.

Summary: This article investigates deep-learning-based omnidirectional intrusion detection systems (IDS) for SCADA networks, capable of detecting both correlated and uncorrelated attacks. FNN and LSTM each excel at detecting uncorrelated and correlated attacks, with improved performance when combined.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Information Systems

Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication

Yongliang Cheng et al.

Summary: This article introduces a new semisupervised hierarchical stacking temporal convolutional network (HS-TCN), which improves the performance and efficiency of anomaly detection in IoT communication.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Automation & Control Systems

Fiden: Intelligent Fingerprint Learning for Attacker Identification in the Industrial Internet of Things

Yuanfang Chen et al.

Summary: This article studies the attacker identification issue in IIoT and proposes an algorithm called Fiden for fingerprinting heterogeneous devices without relying on periodic signal transmission. The algorithm demonstrates effectiveness in a real case study in the vehicle industry, showing increased accuracy, precision, recall, and F1 score compared to a clock-based intrusion detection system.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Computer Science, Information Systems

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Yulei Wu et al.

Summary: Critical infrastructure systems in Industry 4.0 are facing challenges in security and scalability. The convergence of blockchain and edge computing is expected to address these issues and provide secure and scalable critical infrastructures. Current research is mainly focused on security, privacy, and scalability in this area.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Information Systems

Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme

Tiehua Zhang et al.

Summary: This article introduces a fog-based collaborative learning scheme, utilizing a voting strategy to elect a coordinator node and proposing a collaborative learning algorithm to generalize different deep learning models, demonstrating the feasibility and effectiveness of this approach.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Information Systems

SAIoT: Scalable Anomaly-Aware Services Composition in CloudIoT Environments

Mohammadreza Razian et al.

Summary: Cloud computing and the Internet of Things (CloudIoT) work together to create smart cities and application services. However, previous approaches fail to accurately model QoS values, leading to service-level agreement violations. A proposed anomaly-aware approach significantly improves QoS values by addressing anomalies in QoS records.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Information Systems

MEdge-Chain: Leveraging Edge Computing and Blockchain for Efficient Medical Data Exchange

Alaa Awad Abdellatif et al.

Summary: This article proposes a medical-edge-blockchain framework (MEdge-Chain) that utilizes edge computing and blockchain technologies to secure and efficiently exchange and store medical data. The framework includes an automated patient monitoring scheme and a blockchain-based optimization model for enhancing medical data exchange between different entities.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Automation & Control Systems

Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

Xiaokang Zhou et al.

Summary: The article introduces a VLSTM model to address imbalance and high-dimension issues in industrial big data, which significantly improves accuracy and reduces false positives in anomaly detection.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Computer Science, Information Systems

Intrusion Detection for Cyber-Physical Systems Using Generative Adversarial Networks in Fog Environment

Paulo Freitas de Araujo-Filho et al.

Summary: This article introduces a fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs, which calculates reconstruction loss based on data samples mapped to latent space for higher detection rates. Experimental results show that the proposed solution is at least 5.5 times faster than baseline methods.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Automation & Control Systems

Privacy-Preserving Tucker Train Decomposition Over Blockchain-Based Encrypted Industrial IoT Data

Jun Feng et al.

Summary: This article proposes a privacy-preserving Tucker train decomposition method based on gradient descent and blockchain encryption, which enables reliable and secure sharing of IIoT data through blockchain technology and builds efficient Tucker decomposition using tensor train theory. Experimental results on synthetic datasets and real-world datasets demonstrate the efficiency and feasibility of this method.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Automation & Control Systems

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Xiaokang Zhou et al.

Summary: This article proposes a few-shot learning model with Siamese convolutional neural network (FSL-SCNN) to enhance the accuracy of intelligent anomaly detection in industrial cyber-physical systems by alleviating over-fitting issues. Experimental results demonstrate that the proposed model can significantly improve the false alarm rate (FAR) and F1 scores in detecting intrusion signals for industrial CPS security protection.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Automation & Control Systems

DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber-Physical Systems

Beibei Li et al.

Summary: The study introduces a novel federated deep learning scheme named DeepFed for detecting cyber threats against industrial CPSs. By designing a new intrusion detection model and federated learning framework, the research successfully achieves secure detection of various cyber threats.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Engineering, Civil

Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

Jiangang Shu et al.

Summary: Vehicular Ad hoc Network (VANET) is vulnerable to intrusion attacks, and a collaborative intrusion detection system (CIDS) utilizing deep learning and generative adversarial networks has been designed to address this issue. The CIDS enables global monitoring of abnormal network behaviors in the entire VANET.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Blockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing

Hong Liu et al.

Summary: This research proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices, reducing resource utilization of the central server while ensuring security and privacy. It uses blockchain for storing and sharing training models, achieving cooperative privacy-preservation for vehicles while reducing communication overhead and computation cost.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

SeDID: An SGX-enabled decentralized intrusion detection framework for network trust evaluation

Gao Liu et al.

Summary: The paper proposes SeDID, a decentralized intrusion detection framework based on blockchain and SGX for network trust evaluation. By introducing a novel consensus mechanism and incentives based on node contributions, SeDID achieves high efficiency, true decentralization, and privacy preservation.

INFORMATION FUSION (2021)

Add to Collection

Article Computer Science, Information Systems

A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

Osama Alkadi et al.

Summary: Significant research has been done on combining blockchain and intrusion detection for enhanced data privacy and detection of cyberattacks. Learning-based ensemble models can identify complex malicious events while ensuring data privacy, providing additional security during VM migration and IoT network protection. The deep blockchain framework proposed in this study outperforms peer models and has potential as a decision support system for secure data migration.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Information Systems

A Cluster-Based Multidimensional Approach for Detecting Attacks on Connected Vehicles

Gianni D'Angelo et al.

Summary: Modern vehicles are becoming more connected and intelligent, but this connectivity poses serious safety risks, particularly in relation to security weaknesses in the CAN bus. This article introduces two algorithms for data-driven anomaly detection, which outperforms other anomaly detection-based approaches according to experimental results.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Automation & Control Systems

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

Faezeh Farivar et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Add to Collection

Article Computer Science, Information Systems

A Lightweight Intrusion Detection for Sybil Attack Under Mobile RPL in the Internet of Things

Sarumathi Murali et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Add to Collection

Article Telecommunications

In-vehicle network intrusion detection using deep convolutional neural network

Hyun Min Song et al.

VEHICULAR COMMUNICATIONS (2020)

Add to Collection

Article Computer Science, Information Systems

Managing IoT Cyber-Security Using Programmable Telemetry and Machine Learning

Arunan Sivanathan et al.

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT (2020)

Add to Collection

Article Computer Science, Information Systems

Enabling Secure Cross-Modal Retrieval Over Encrypted Heterogeneous IoT Databases With Collective Matrix Factorization

Cheng Guo et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Add to Collection

Article Computer Science, Information Systems

Optimal Probabilistic Caching in Heterogeneous IoT Networks

Shubin Zhang et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Alejandro Barredo Arrieta et al.

INFORMATION FUSION (2020)

Add to Collection

Article Computer Science, Information Systems

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Shuiguang Deng et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Add to Collection

Article Computer Science, Information Systems

CE-IoT: Cost-Effective Cloud-Edge Resource Provisioning for Heterogeneous IoT Applications

Zhi Zhou et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Add to Collection

Article Computer Science, Information Systems

Semisupervised-Learning-Based Security to Detect and Mitigate Intrusions in IoT Network

Nagarathna Ravi et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Add to Collection

Article Computer Science, Information Systems

Machine Learning in IoT Security: Current Solutions and Future Challenges

Fatima Hussain et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2020)

Add to Collection

Article Computer Science, Information Systems

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Mohammed Ali Al-Garadi et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2020)

Add to Collection

Article Computer Science, Information Systems

TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems

Abdullah Alsaedi et al.

IEEE ACCESS (2020)

Add to Collection

© Peeref 2019-2024. All rights reserved.