Cited by 457Network Security and Intrusion DetectionPrivacy-Preserving Technologies in DataInternet Traffic Analysis and Secure E-voting

Federated Learning for intrusion detection system: Concepts, challenges and future directions

Open access
Agrawal, Shaashwat, Sarkar, Sagnik, Aouedi, Ons, Yenduri, Gokul, Piamrat, Kandaraj, Alazab, Mamoun, Bhattacharya, Sweta, Maddikunta, Praveen Kumar Reddy +1 more·2022·Computer Communications

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Crossref · 10.1016/j.comcom.2022.09.012

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  • A deep reinforcement learning based algorithm is presented where DRL is responsible for allocation awards at the aggregator and selecting devices to suggest the learning model for their data.

    “To tackle that drawback, a deep reinforcement learning (DRL) based algorithm is presented in [68], where DRL is responsible for allocation awards at the aggregator and selecting devices to suggest the learning model for their data.”

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Research works printed but not linked: 12

  1. Al-Marri et al.(2020)Al-Marri, Ciftler, and Abdallah] Noor Ali Al-Athba Al-Marri, Bekir S Ciftler, and Mohamed M Abdallah. Federated mimic learning for privacy preserving intrusion detection. In 2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pages 1-6. IEEE, 2020.
  2. Aouedi et al.(2018)Aouedi, Tobji, and Abraham] Ons Aouedi, Mohamed Anis Bach Tobji, and Ajith Abraham. Internet of things and ambient intelligence for mobile health monitoring: A review of a decade of research. Int J Comput Inf Syst Ind Manag Appl, 10:261-270, 2018.
  3. Asad, Moustafa, Ito, and Aslam] Muhammad Asad, Ahmed Moustafa, Takayuki Ito, and Muhammad Aslam. Evaluating the communication efficiency in federated learning algorithms. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 552-557. IEEE, 2021.
  4. Dinh, Tran, Nguyen, Hong, Bao, Zomaya, and Gramoli] Canh Dinh, Nguyen H Tran, Minh NH Nguyen, Choong Seon Hong, Wei Bao, Albert Y Zomaya, and Vincent Gramoli. Federated learning over wireless networks: Convergence analysis and resource allocation. arXiv preprint arXiv:1910.13067, 2019. · arXiv
  5. Gong et al.(2020)Gong, Xie, Pan, Feng, and Qin] Maoguo Gong, Yu Xie, Ke Pan, Kaiyuan Feng, and Alex Kai Qin. A survey on differentially private machine learning. IEEE Computational Intelligence Magazine, 15(2):49-64, 2020.
  6. Pouyanfar et al.(2018b)Pouyanfar, Sadiq, Yan, Tian, Tao, Reyes, Shyu, Chen, and Iyengar] Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and SS Iyengar. A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5):1-36, 2018b.
  7. Qin, Poularakis, Leung, and Tassiulas] Qiaofeng Qin, Kon- stantinos Poularakis, Kin K Leung, and Leandros Tassiulas. Line-speed and scalable intrusion detection at the network edge via federated learning. In 2020 IFIP Networking Conference (Networking), pages 352-360. IEEE, 2020.
  8. Samrin and Vasumathi(2017)] Rafath Samrin and D Vasumathi. Review on anomaly based network intrusion detection system. In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pages 141-147, 2017. doi: 10.1109/ICEECCOT.2017.8284655. · DOI
  9. Sun et al.(2020b)Sun, Ochiai, and Esaki] Yuwei Sun, Hideya Ochiai, and Hiroshi Esaki. Intrusion detection with segmented federated learning for large-scale multiple lans. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2020b.
  10. Vishal Vishal and Rahul Johari. Soaice: simulation of attacks in cloud computing environment. In 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Con- fluence), pages 14-15. IEEE, 2018.
  11. Wang, Garg, Lin, Hu, Kaddoum, Piran, and Hossain] Xiaoding Wang, Sahil Garg, Hui Lin, Jia Hu, Georges Kaddoum, Md Jalil Piran, and M Shamim Hossain. Towards accurate anomaly detection in industrial internet-of-things using hierarchical federated learning. IEEE Internet of Things Journal, 2021.
  12. Xu et al.(2021)Xu, Ali, and Yue] Qinghua Xu, Shaukat Ali, and Tao Yue. Digital twin-based anomaly detection in cyber-physical systems. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST), pages 205-216. IEEE, 2021.

Cited by

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Authors
Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R.
Affiliations
  • Vellore Institute of Technology University (IN)
  • Laboratoire des Sciences du Numérique de Nantes (FR)
  • Charles Darwin University (AU)
Corresponding author
Gadekallu, Thippa Reddy
Venue
Computer Communications
Publisher
Elsevier BV
Published
2022-11
Volume
195
Pages
346-361
ISSN
0140-3664 (ISSN-L)
OpenAlex
W3171802458
Cited by
457
References
120
Citations by year
2026: 80 · 2025: 165 · 2024: 114 · 2023: 67 · 2022: 28 · 2021: 3
FWCI
41.50
Type
journal article
Language
en
Open access
Locations
  • Computer Communications (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • HAL (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Keywords
federated learning, intrusion detection systems, privacy-preserving machine learning, anomaly detection, edge computing, decentralized learning, network security, distributed networks

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