Federated Learning Models for Privacy-Preserving Cybersecurity Analytics

• Author(s): Iboro Akpan Essien ; Joshua Oluwagbenga Ajayi ; Eseoghene Daniel Erigha ; Ehimah Obuse ; Noah Ayanbode
• Paper ID: 1710370
• Page: 493-514
• Published Date: 31-03-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 9 March-2020

Abstract

The increasing sophistication of cyber threats, coupled with heightened data privacy concerns, has intensified the need for advanced, privacy-preserving analytics in cybersecurity. Federated Learning (FL) has emerged as a transformative paradigm that enables multiple distributed entities to collaboratively train machine learning models without directly sharing raw data. This study investigates the application of FL models in cybersecurity analytics, emphasizing their ability to preserve sensitive information while enabling robust threat detection, anomaly recognition, and predictive security intelligence. By leveraging decentralized data from diverse sources such as enterprise networks, cloud infrastructures, IoT ecosystems, and critical infrastructure systems FL facilitates the creation of global models that capture complex attack patterns while adhering to data protection regulations like GDPR, CCPA, and HIPAA. The paper examines FL’s integration with advanced algorithms, including deep neural networks, gradient boosting, and reinforcement learning, to enhance detection accuracy and reduce false positives in intrusion detection, malware classification, and phishing detection. It further addresses challenges such as statistical heterogeneity, communication overhead, and vulnerability to model poisoning or inference attacks, proposing mitigation strategies including secure aggregation, differential privacy, homomorphic encryption, and robust aggregation techniques. Case studies from sectors including finance, healthcare, and smart manufacturing illustrate real-world deployments, showcasing metrics like precision, recall, detection rate, and mean time to detect (MTTD). The analysis reveals that FL-based cybersecurity solutions not only maintain compliance with stringent privacy mandates but also offer scalability and adaptability to evolving threats. Additionally, the research highlights future directions such as combining FL with blockchain for auditability, adopting energy-efficient model architectures for edge environments, and developing standardized benchmarks for evaluating FL-enabled security systems. By bridging the gap between collaborative intelligence and privacy preservation, Federated Learning models represent a critical advancement in the pursuit of proactive, distributed, and regulation-compliant cybersecurity analytics capable of addressing the challenges of a rapidly evolving digital threat landscape.

Keywords

Federated Learning, Privacy-Preserving Analytics, Cybersecurity, Intrusion Detection, Anomaly Detection, Machine Learning, Deep Learning, Differential Privacy, Secure Aggregation, Model Poisoning Defense, GDPR Compliance, Decentralized Threat Intelligence, Edge Computing Security.

Citations

IRE Journals:
Iboro Akpan Essien , Joshua Oluwagbenga Ajayi , Eseoghene Daniel Erigha , Ehimah Obuse , Noah Ayanbode "Federated Learning Models for Privacy-Preserving Cybersecurity Analytics" Iconic Research And Engineering Journals Volume 3 Issue 9 2020 Page 493-514

IEEE:
Iboro Akpan Essien , Joshua Oluwagbenga Ajayi , Eseoghene Daniel Erigha , Ehimah Obuse , Noah Ayanbode "Federated Learning Models for Privacy-Preserving Cybersecurity Analytics" Iconic Research And Engineering Journals, 3(9)