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

• URL: http://arxiv.org/abs/2403.01507v1
• Date: Sun, 3 Mar 2024 13:13:06 GMT
• Title: ISSF: The Intelligent Security Service Framework for Cloud-Native Operation
• Authors: Yikuan Yan, Keman Huang, Michael Siegel,
• Abstract summary: 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. Experiments demonstrate that our framework can sufficiently model the security posture of a cloud-native system for defenders.
• Score: 0.2867517731896504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning [10.601458163651582]
  This paper addresses the absence of effective edge-blocking ACO strategies in dynamic, real-world networks. It specifically targets the cybersecurity vulnerabilities of organizational Active Directory (AD) systems. Unlike the existing literature on edge-blocking defenses which considers AD systems as static entities, our study counters this by recognizing their dynamic nature.
  arXiv  Detail & Related papers  (2024-06-28T01:37:46Z)
• Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods [0.3626013617212667]
  Reinforcement Learning (RL) learns to make decisions based on real-world experiences. This paper reviews the latest adversarial RL threats and outlines effective defense strategies tailored to safeguard these applications. By concentrating on the smart grid and smart home scenarios, this survey equips ML developers and researchers with the insights needed to secure RL applications.
  arXiv  Detail & Related papers  (2024-02-23T21:48:50Z)
• Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning [4.796742432333795]
  Team of intelligent agents in computer network defence roles may reveal promising avenues to safeguard cyber and kinetic assets. Agents are evaluated on their ability to jointly mitigate attacker activity in host-based defence scenarios.
  arXiv  Detail & Related papers  (2023-08-25T14:07:50Z)
• Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties [5.78419291062552]
  We propose a data-driven deep reinforcement learning framework to learn proactive, context-aware defense countermeasures. A dynamic defense optimization problem is formulated with multiple protective postures against different types of adversaries.
  arXiv  Detail & Related papers  (2023-02-03T08:33:33Z)
• A Framework for Understanding Model Extraction Attack and Defense [48.421636548746704]
  We study tradeoffs between model utility from a benign user's view and privacy from an adversary's view. We develop new metrics to quantify such tradeoffs, analyze their theoretical properties, and develop an optimization problem to understand the optimal adversarial attack and defense strategies.
  arXiv  Detail & Related papers  (2022-06-23T05:24:52Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training [71.7750435554693]
  We show that several state-of-the-art RL agents proposed for power system control are vulnerable to adversarial attacks. Specifically, we use an adversary Markov Decision Process to learn an attack policy, and demonstrate the potency of our attack. We propose to use adversarial training to increase the robustness of RL agent against attacks and avoid infeasible operational decisions.
  arXiv  Detail & Related papers  (2021-10-18T00:50:34Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defense [7.321728608775741]
  We present a new hybrid autonomous agent architecture that aims to optimize and verify defense policies of reinforcement learning (RL) We use constraints verification (using satisfiability modulo theory (SMT)) to steer the RL decision-making toward safe and effective actions. Our evaluation of the presented approach in a simulated CPS environment shows that the agent learns the optimal policy fast and defeats diversified attack strategies in 99% cases.
  arXiv  Detail & Related papers  (2021-04-19T01:08:30Z)
• Boosting Adversarial Training with Hypersphere Embedding [53.75693100495097]
  Adversarial training is one of the most effective defenses against adversarial attacks for deep learning models. In this work, we advocate incorporating the hypersphere embedding mechanism into the AT procedure. We validate our methods under a wide range of adversarial attacks on the CIFAR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2020-02-20T08:42:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.