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ISSF: The Intelligent Security Service Framework for Cloud-Native Operation

Yikuan Yan, Keman Huang, Michael Siegel

TL;DR

ISSF tackles security in cloud-native operations by unifying attacker and defender perspectives under a multi-agent deep reinforcement learning framework. It introduces a dynamic access graph and a comprehensive action and reward model to simulate intelligent offense and defense services, coupled with a training/publishing/evaluation pipeline and an $ELO$-based benchmarking scheme. A three-service-chain cloud-native case demonstrates the framework’s ability to model security posture, train diverse services, and quantitatively compare them. The results indicate that exposure to diverse adversaries during training improves defense capability and that ISSF provides a scalable pathway for systematic security service optimization in complex cloud environments.

Abstract

The growing system complexity from microservice architectures and the bilateral enhancement of artificial intelligence (AI) for both attackers and defenders presents increasing security challenges for cloud-native operations. In particular, cloud-native operators require a holistic view of the dynamic security posture for the cloud-native environment from a defense aspect. Additionally, both attackers and defenders can adopt advanced AI technologies. This makes the dynamic interaction and benchmark among different intelligent offense and defense strategies more crucial. Hence, following the multi-agent deep reinforcement learning (RL) paradigm, this research develops an agent-based intelligent security service framework (ISSF) for cloud-native operation. It includes a dynamic access graph model to represent the cloud-native environment and an action model to represent offense and defense actions. Then we develop an approach to enable the training, publishing, and evaluating of intelligent security services using diverse deep RL algorithms and training strategies, facilitating their systematic development and benchmark. The experiments demonstrate that our framework can sufficiently model the security posture of a cloud-native system for defenders, effectively develop and quantitatively benchmark different services for both attackers and defenders and guide further service optimization.

ISSF: The Intelligent Security Service Framework for Cloud-Native Operation

TL;DR

ISSF tackles security in cloud-native operations by unifying attacker and defender perspectives under a multi-agent deep reinforcement learning framework. It introduces a dynamic access graph and a comprehensive action and reward model to simulate intelligent offense and defense services, coupled with a training/publishing/evaluation pipeline and an -based benchmarking scheme. A three-service-chain cloud-native case demonstrates the framework’s ability to model security posture, train diverse services, and quantitatively compare them. The results indicate that exposure to diverse adversaries during training improves defense capability and that ISSF provides a scalable pathway for systematic security service optimization in complex cloud environments.

Abstract

The growing system complexity from microservice architectures and the bilateral enhancement of artificial intelligence (AI) for both attackers and defenders presents increasing security challenges for cloud-native operations. In particular, cloud-native operators require a holistic view of the dynamic security posture for the cloud-native environment from a defense aspect. Additionally, both attackers and defenders can adopt advanced AI technologies. This makes the dynamic interaction and benchmark among different intelligent offense and defense strategies more crucial. Hence, following the multi-agent deep reinforcement learning (RL) paradigm, this research develops an agent-based intelligent security service framework (ISSF) for cloud-native operation. It includes a dynamic access graph model to represent the cloud-native environment and an action model to represent offense and defense actions. Then we develop an approach to enable the training, publishing, and evaluating of intelligent security services using diverse deep RL algorithms and training strategies, facilitating their systematic development and benchmark. The experiments demonstrate that our framework can sufficiently model the security posture of a cloud-native system for defenders, effectively develop and quantitatively benchmark different services for both attackers and defenders and guide further service optimization.
Paper Structure (22 sections, 2 equations, 3 figures, 1 table)

This paper contains 22 sections, 2 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. cloud-native Security Modeling.
  4. Intelligent Cybersecurity Operations and Optimizations.
  5. The Agent-based Intelligent Security Service Model
  6. Dynamic Access Graph Model for Cloud-Native System
  7. Action Model for Offenses and Defenses
  8. Action Space for Attackers.
  9. Action Space for Defenders
  10. Reward
  11. Security Service Training, Publishing and Evaluating
  12. Training a Security Service from Scratch or Pre-trained one
  13. Publishing a Security Service to the Security Service Pool
  14. Evaluating Security Services based on ELO Rating
  15. Episode-based Metrics.
  16. ...and 7 more sections

Figures (3)

  • Figure 1: The Approach for Security Service Training, Publishing and Evaluating.
  • Figure 2: A three-service-chain cloud-native System. The right part is the access graph model for the environment, including the nodes, their links through API endpoint information (dot link) or access credential (solid link), and vulnerabilities associated with each node. The upper left part is the JSON-based configuration for $service A2$, provided as an example. The lower part reports the associated CVEs and their CVSS scores in the environment.
  • Figure 3: Performance Benchmark for Selected Security Services in Table 1.