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Found 16 papers, 8 papers with code
Intrusion Tolerance for Networked Systems through Two-Level Feedback Control
We formulate intrusion tolerance for a system with service replicas as a two-level optimal control problem.
Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
To address these limitations, we propose conjectural online learning (\textsc{col}), an online method for generic \textsc{aisg}s. \textsc{col} uses a forecaster-actor-critic (\textsc{fac}) architecture where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures.
IT Intrusion Detection Using Statistical Learning and Testbed Measurements
We study automated intrusion detection in an IT infrastructure, specifically the problem of identifying the start of an attack, the type of attack, and the sequence of actions an attacker takes, based on continuous measurements from the infrastructure.
Automated Security Response through Online Learning with Adaptive Conjectures
We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game.
Scalable Learning of Intrusion Responses through Recursive Decomposition
We study automated intrusion response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed stochastic game.
A Framework for dynamically meeting performance objectives on a service mesh
By first learning the system model and the operating region from testbed traces, we can train the agent for different management objectives in parallel.
Learning Near-Optimal Intrusion Responses Against Dynamic Attackers
We study automated intrusion response and formulate the interaction between an attacker and a defender as an optimal stopping game where attack and defense strategies evolve through reinforcement learning and self-play.
Dynamically meeting performance objectives for multiple services on a service mesh
We present a framework that lets a service provider achieve end-to-end management objectives under varying load.
Learning Security Strategies through Game Play and Optimal Stopping
We study automated intrusion prevention using reinforcement learning.
A System for Interactive Examination of Learned Security Policies
We present a system for interactive examination of learned security policies.
Online Feature Selection for Efficient Learning in Networked Systems
We present an online algorithm called Online Stable Feature Set Algorithm (OSFS), which selects a small feature set from a large number of available data sources after receiving a small number of measurements.
Intrusion Prevention through Optimal Stopping
We therefore develop a reinforcement learning approach to approximate an optimal threshold policy.
Learning Intrusion Prevention Policies through Optimal Stopping
We study automated intrusion prevention using reinforcement learning.
Online feature selection for rapid, low-overhead learning in networked systems
Data-driven functions for operation and management often require measurements collected through monitoring for model training and prediction.
Finding Effective Security Strategies through Reinforcement Learning and Self-Play
We present a method to automatically find security strategies for the use case of intrusion prevention.
Predicting SLA Violations in Real Time using Online Machine Learning
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation.
