Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
May 17, 2026Journal of Multiscale Modelling

Fortifying the Cloud: AI-Driven Federated Learning for Intrusion Detection and Cyber Resilience

View Full Paper
Ask AI
Bookmark
Share

Authors

VIVenkataramesh InduruKingston Technology (United States)PRP. RadhakrishnanIBM (United States)VRVijai Anand RamarDelta Air Lines (United States)

Discussion

Loading...

Member takes

Implication

Randomized trial demonstrates effective intrusion detection in cloud computing, highlighting improved privacy and accuracy.

Key Points

  • The main aim is to enhance intrusion detection in cloud environments using federated learning while preserving data privacy.
  • Developed a Fed-LSTM framework for distributed model training across nodes without sharing raw data.
  • Used benchmark datasets like CICIDS2017, NSL-KDD, and UNSW-NB15 for heterogeneous traffic analysis.
  • Implemented Min-Max normalization and entropy-based feature selection for data preprocessing.
  • Achieved 95.1% detection accuracy, outperforming traditional and deep learning methods.
  • Showed improved stability in model performance across distributed nodes.
  • Enhanced privacy through Differential Privacy and Secure Aggregation techniques.

Cite This Study

Induru et al. (2026) studied this question.

synapsesocial.com/papers/6a095ba67880e6d24efe1740https://doi.org/10.1142/s1756973726400299
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches2024 · 6 citations
  2. 2Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks2024 · 14 citations
  3. 3Exploring the Synergy: AI Enhancing Blockchain, Blockchain Empowering AI, and Their Convergence Across IoT Applications and Beyond2024 · 55 citations
  4. 4A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy2024 · 57 citations
  5. 5Intruder Detection in VANET Data Streams Using Federated Learning for Smart City Environments2023 · 72 citations