mlsanityData to 7 Oct 2026

Cerberus: Exploring Federated Prediction of Security Events

arXiv 2209.03050 ↗First posted 7 Sep 2022cs.CR cs.AIcited by 4

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Abstract (arXiv)

Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible. In this paper, we explore the feasibility of using Federated Learning (FL) to predict future security events. To this end, we introduce Cerberus, a system enabling collaborative training of Recurrent Neural Network (RNN) models for participating organizations. The intuition is that FL could potentially offer a middle-ground between the non-private approach where the training data is pooled at a central server and the low-utility alternative of only training local models. We instantiate Cerberus on a dataset obtained from a major security company's intrusion prevention product and evaluate it vis-a-vis utility, robustness, and privacy, as well as how participants contribute to and benefit from the system. Overall, our work sheds light on both the positive aspects and the challenges of using FL for this task and paves the way for deploying federated approaches to predictive security.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 68 references that are in the map. First posted on arXiv 7 Sep 2022.

Builds on

32 of 68 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  6. 2019Deep Leakage from Gradientscited by 1,004
  7. 2018How To Backdoor Federated Learningcited by 745
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  9. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  10. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  11. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  12. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
20 more
  1. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  2. 2019Can You Really Backdoor Federated Learning?cited by 250
  3. 2020Threats to Federated Learning: A Surveycited by 171
  4. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  5. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  6. 2019Measure Contribution of Participants in Federated Learningcited by 78
  7. 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 72
  8. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  9. 2021Provably Secure Federated Learning against Malicious Clientscited by 60
  10. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  11. 2019On Generalization Bounds of a Family of Recurrent Neural Networkscited by 42
  12. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  13. 2021A comparison of LSTM and GRU networks for learning symbolic sequencescited by 24
  14. 2021Excess Capacity and Backdoor Poisoningcited by 24
  15. 2019Tiresias: Predicting Security Events Through Deep Learningcited by 10
  16. 2020Less is More: A privacy-respecting Android malware classifier using Federated Learningcited by 8
  17. 2018Distributed Stochastic Optimization via Adaptive SGDcited by 7
  18. 2019ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattackscited by 6
  19. 2021Privacy Assessment of Federated Learning using Private Personalized Layerscited by 5
  20. 2016Recurrent Memory Array Structurescited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  2. 2023BadVFL: Backdoor Attacks in Vertical Federated Learningcited by 9
  3. 2024A Systematic Review of Federated Generative Modelscited by 5
  4. 2025Toward Malicious Clients Detection in Federated Learningcited by 4