TU Darmstadt / ULB / TUbiblio

FedCRI: Federated Mobile Cyber-Risk Intelligence

Fereidooni, Hossein ; Dmitrienko, Alexandra ; Rieger, Phillip ; Miettinen, Markus ; Sadeghi, Ahmad-Reza ; Madlener, Felix (2022):
FedCRI: Federated Mobile Cyber-Risk Intelligence.
Network and Distributed Systems Security (NDSS) Symposium 2022, San Diego, USA, 24.-28.04.2022, DOI: 10.14722/ndss.2022.23153,
[Conference or Workshop Item]

Abstract

In the present era of ubiquitous digitization more and more services are becoming available online which is amplified by the Corona pandemic. The fast-growing mobile service market opens up new attack surfaces to the mobile service ecosystem. Hence, mobile service providers are faced with various challenges to protect their services and in particular the associated mobile apps. Defenses for apps are, however, often limited to (lightweight) application-level protection such as app hardening and monitoring and intrusion detection. Therefore, effective risk management is crucial to limit the exposure of mobile services to threats and potential damages caused by attacks. In this paper, we present FedCRI, a solution for sharing Cyber-Risk Intelligence (CRI). At its core, FedCRI transforms mobile cyber-risks into machine learning (ML) models and leverages ML-based risk management to evaluate security risks on mobile devices. FedCRI enables fast and autonomous sharing of actionable ML-based CRI knowledge by utilizing Federated Learning (FL). FL allows collaborative training of effective risk detection models based on information contributed by different mobile service providers while preserving the privacy of the training data of the individual organizations. We extensively evaluate our approach on several real-world user databases representing 23.8 million users of security-critical mobile apps (since Android 4 and iOS 6) provided by nine different service providers in different European countries. The datasets were collected over the course of six years in the domains of financial services, payments, and insurances. Our approach can successfully extract accurate CRI models, allowing effective identification of cybersecurity risks on mobile devices. Our evaluation shows that the federated risk detection model can achieve better than 99% accuracy in terms of F1-score in most risk classification tasks with a very low number of false positives.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Fereidooni, Hossein ; Dmitrienko, Alexandra ; Rieger, Phillip ; Miettinen, Markus ; Sadeghi, Ahmad-Reza ; Madlener, Felix
Title: FedCRI: Federated Mobile Cyber-Risk Intelligence
Language: English
Abstract:

In the present era of ubiquitous digitization more and more services are becoming available online which is amplified by the Corona pandemic. The fast-growing mobile service market opens up new attack surfaces to the mobile service ecosystem. Hence, mobile service providers are faced with various challenges to protect their services and in particular the associated mobile apps. Defenses for apps are, however, often limited to (lightweight) application-level protection such as app hardening and monitoring and intrusion detection. Therefore, effective risk management is crucial to limit the exposure of mobile services to threats and potential damages caused by attacks. In this paper, we present FedCRI, a solution for sharing Cyber-Risk Intelligence (CRI). At its core, FedCRI transforms mobile cyber-risks into machine learning (ML) models and leverages ML-based risk management to evaluate security risks on mobile devices. FedCRI enables fast and autonomous sharing of actionable ML-based CRI knowledge by utilizing Federated Learning (FL). FL allows collaborative training of effective risk detection models based on information contributed by different mobile service providers while preserving the privacy of the training data of the individual organizations. We extensively evaluate our approach on several real-world user databases representing 23.8 million users of security-critical mobile apps (since Android 4 and iOS 6) provided by nine different service providers in different European countries. The datasets were collected over the course of six years in the domains of financial services, payments, and insurances. Our approach can successfully extract accurate CRI models, allowing effective identification of cybersecurity risks on mobile devices. Our evaluation shows that the federated risk detection model can achieve better than 99% accuracy in terms of F1-score in most risk classification tasks with a very low number of false positives.

Uncontrolled Keywords: Cyber-Risk Intelligence (CRI), Federated Learning (FL), Cyber-Threat Intelligence (CTI), Mobile Platform
Divisions: 20 Department of Computer Science
20 Department of Computer Science > System Security Lab
Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Event Title: Network and Distributed Systems Security (NDSS) Symposium 2022
Event Location: San Diego, USA
Event Dates: 24.-28.04.2022
Date Deposited: 28 Apr 2022 08:42
DOI: 10.14722/ndss.2022.23153
URL / URN: https://www.ndss-symposium.org/wp-content/uploads/2022-153-p...
PPN: 499680448
Projects: Intel Private AI Center, BMBF
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details