Automating Privilege Escalation with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2110.01362v1
• Date: Mon, 4 Oct 2021 12:20:46 GMT
• Title: Automating Privilege Escalation with Deep Reinforcement Learning
• Authors: Kalle Kujanp\"a\"a, Willie Victor, Alexander Ilin
• Abstract summary: In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.
• Score: 71.87228372303453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI-based defensive solutions are necessary to defend networks and information assets against intelligent automated attacks. Gathering enough realistic data for training machine learning-based defenses is a significant practical challenge. An intelligent red teaming agent capable of performing realistic attacks can alleviate this problem. However, there is little scientific evidence demonstrating the feasibility of fully automated attacks using machine learning. In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our results show that the autonomous agent can escalate privileges in a Windows 7 environment using a wide variety of different techniques depending on the environment configuration it encounters. Hence, our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.

Related papers

• Planning for Learning Object Properties [117.27898922118946]
  We formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem. We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. We provide an experimental evaluation in both a simulated and a real environment.
  arXiv  Detail & Related papers  (2023-01-15T09:37:55Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• 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)
• Autonomous Attack Mitigation for Industrial Control Systems [25.894883701063055]
  Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. We present a deep reinforcement learning approach to autonomous response and recovery in large industrial control networks.
  arXiv  Detail & Related papers  (2021-11-03T18:08:06Z)
• Informing Autonomous Deception Systems with Cyber Expert Performance Data [0.0]
  This paper explores the potential to use Inverse Reinforcement Learning (IRL) to gain insight into attacker actions, utilities of those actions, and ultimately decision points which cyber deception could thwart. The Tularosa study, as one example, provides experimental data of real-world techniques and tools commonly used by attackers, from which core data can be leveraged to inform an autonomous cyber defense system.
  arXiv  Detail & Related papers  (2021-08-31T20:28:09Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• An Empirical Review of Adversarial Defenses [0.913755431537592]
  Deep neural networks, which form the basis of such systems, are highly susceptible to a specific type of attack, called adversarial attacks. A hacker can, even with bare minimum computation, generate adversarial examples (images or data points that belong to another class, but consistently fool the model to get misclassified as genuine) and crumble the basis of such algorithms. We show two effective techniques, namely Dropout and Denoising Autoencoders, and show their success in preventing such attacks from fooling the model.
  arXiv  Detail & Related papers  (2020-12-10T09:34:41Z)
• Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning [91.13113161754022]
  We introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop.
  arXiv  Detail & Related papers  (2020-02-20T21:39:25Z)
• NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion [0.3007949058551534]
  Before the rise of machine learning, network anomalies which could imply an attack, were detected using well-crafted rules. With the advancements of machine learning for network anomaly, it is not easy for a human to understand how to bypass a cyber-defence system. In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system.
  arXiv  Detail & Related papers  (2020-02-20T01:54:45Z)

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.