Security, Trust, and Privacy in Machine Learning-Based Internet of Things
Abstract
No abstract available.
References
[1]
[2]
W. Li, W. Meng, and M. H. Au, “Enhancing Collaborative Intrusion Detection via Disagreement-Based Semi-Supervised Learning in IoT Environments,” Journal of Network and Computer Applications, vol. 161, pp. 1–9, 2020.
[3]
W. Meng, W. Li, and L. F. Kwok, “Towards Effective Trust-Based Packet Filtering in Collaborative Network Environments,” IEEE Transactions on Network and Service Management, vol. 14, no. 1, pp. 233–245, 2017.
[4]
W. Meng, K.-K. R. Choo, S. Furnell, A. V. Vasilakos, and C. W. Probst, “Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks,” IEEE Transactions on Network and Service Management, vol. 15, no. 2, pp. 761–773, 2018.
[5]
W. Li and W. Meng, “PMFA: Toward Passive Message Fingerprint Attacks on Challenge-Based Collaborative Intrusion Detection Networks,” in Proceedings of the 10th International Conference on Network and System Security (NSS 2016), pp. 433–449, Taipei, Taiwan, September 2016.
[6]
W. Meng, “Intrusion Detection in the Era of IoT: Building Trust via Traffic Filtering and Sampling,” Computer, vol. 51, no. 7, pp. 36–43, July 2018.
[7]
A. Rezapour and W.-G. Tzeng, “A Robust Intrusion Detection Network Using Thresholdless Trust Management System with Incentive Design,” Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 255, no. 2, pp. 139–154, 2018.
[8]
H. Liang, J. Wu, S. Mumtaz, J. Li, X. Lin, and M. Wen, “MBID: Micro-Blockchain-Based Geographical Dynamic Intrusion Detection for V2X,” IEEE Communications Magazine, vol. 57, no. 10, pp. 77–83, 2019.
[9]
O. Alkadi, N. Moustafa, and B. Turnbull, “A Review of Intrusion Detection and Blockchain Applications in the Cloud: Approaches, Challenges and Solutions,” IEEE Access, vol. 8, pp. 104893–104917, 2020.
[10]
R. Švihrová and C. Lettner, “A Semi-Supervised Approach for Network Intrusion Detection,” in Proceedings of the 15th International Conference on Availability, Reliability and Security, vol. 93, pp. 1–6, Ireland, August 2020.
[11]
Y. Zong and G. Huang, “Application of Artificial Fish Swarm Optimization Semi-Supervised Kernel Fuzzy Clustering Algorithm in Network Intrusion,” Journal of Intelligent and Fuzzy Systems, vol. 39, no. 2, pp. 1619–1626, 2020.
[12]
Y. Jiang, K. Zhang, Y. Qian, and L. Zhou, “Reinforcement Learning-Based Query Optimization in Differentially Private IoT Data Publishing,” IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11163–11176, 2021.
[13]
C. Wu and W. Li, “Enhancing Intrusion Detection with Feature Selection and Neural Network,” International Journal of Intelligent Systems, vol. 36, no. 7, pp. 3087–3105, 2021.
[14]
N. Gupta, V. Jindal, and P. Bedi, “CSE-IDS: Using Cost-Sensitive Deep learning and Ensemble Algorithms to Handle Class Imbalance in Network-Based Intrusion Detection Systems,” Computers & Security, vol. 112, 2022.
[15]
S. Hershkop and S. J. Stolfo, “Combining email Models for False Positive Reduction,” in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining-KDD ‘05, pp. 98–107, Chicago IL USA, August 2005.
[16]
A. Abusnaina, A. Khormali, H. Alasmary, J. Park, A. Anwar, and A. Mohaisen, “Adversarial Learning Attacks on Graph-Based IoT Malware Detection Systems,” in Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems, ICDCS, pp. 1296–1305, Dallas, TX, USA, July 2019.
[17]
A. Singh and B. Sikdar, “Adversarial Attack and Defence Strategies for Deep-Learning-Based IoT Device Classification Techniques,” IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2602–2613, 2022.
[18]
H. Yang, R. Zeng, F. Wang, G. Xu, and J. Zhang, “An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things,” Security and Communication Networks, vol. 2020, 11 pages, 2020.
[19]
L. Xie, H. Ni, H. Yang, and J. Zhang, “A Key Business Node Identification Model for Internet of Things Security,” Security and Communication Networks, vol. 2020, pp. 1–11, 2020.
[20]
Y. Zhong, Z. Li, and L. Liao, “A Privacy-Preserving Caching Scheme for Device-to-Device Communications,” Security and Communication Networks, vol. 2021, 8 pages, 2021.
[21]
B. Jiang, “Two-Party Secure Computation for Any Polynomial Function on Ciphertexts under Different Secret Keys,” Security and Communication Networks, vol. 2021, pp. 1–6695304, 2021.
[22]
F. Li, P. Ren, G. Yang, Y. Sun, Y. Wang, Y. Wang, S. Li, and H. Zhou, “An Efficient Anonymous Communication Scheme to Protect the Privacy of the Source Node Location in the Internet of Things,” Security and Communication Networks, vol. 2021, 16 pages, 2021.
[23]
J. Man and G. Sun, “A Residual Learning-Based Network Intrusion Detection System,” Security and Communication Networks, vol. 2021, 9 pages, 2021.
[24]
Q. Li, L. Zhang, R. Zhou, Y. Xia, W. Gao, and Y. Tai, “Machine Learning-Based Stealing Attack of the Temperature Monitoring System for the Energy Internet of Things,” Security and Communication Networks, vol. 2021, 8 pages, 2021.
[25]
G. Lu and X. Tian, “An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM,” Security and Communication Networks, vol. 2021, 21 pages, 2021.
[26]
Y. Li, Y. Li, H. Xu, and S. Ren, “An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks,” Security and Communication Networks, vol. 2021, 16 pages, 2021.
[27]
Z. Li, X. Cheng, L. Sun, J. Zhang, and B. Chen, “A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks,” Security and Communication Networks, vol. 2021, 14 pages, 2021.
[28]
J. Xie, W. Tan, B. Fang, and Z. Huang, “Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification,” Security and Communication Networks, vol. 2021, 12 pages, 2021.
[29]
W. Li, S. Sun, S. Zhang, H. Zhang, and Y. Shi, “Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data,” Security and Communication Networks, vol. 2021, 11 pages, 2021.
Recommendations
Security, privacy and trust in Internet of Things
Internet of Things (IoT) is characterized by heterogeneous technologies, which concur to the provisioning of innovative services in various application domains. In this scenario, the satisfaction of security and privacy requirements plays a fundamental ...
Internet of Things security
The Internet of things (IoT) has recently become an important research topic because it integrates various sensors and objects to communicate directly with one another without human intervention. The requirements for the large-scale deployment of the IoT ...
Privacy preserving Internet of Things
The Internet of Things (IoT) is the latest web evolution that incorporates billions of devices that are owned by different organisations and people who are deploying and using them for their own purposes. IoT-enabled harnessing of the information that ...
Comments
Information & Contributors
Information
Published In
Copyright © 2022 Weizhi Meng et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 01 January 2022
Qualifiers
- Editorial
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in