65 citations · 153 across the 12 of their papers we have counts for
12 papers
A Framework for dynamically meeting performance objectives on a service mesh
Forough Shahab Samani, Rolf Stadler
We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techni…
Learning Near-Optimal Intrusion Responses Against Dynamic Attackers
Kim Hammar, Rolf Stadler
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 throug…
Dynamically meeting performance objectives for multiple services on a service mesh
Forough Shahab Samani, Rolf Stadler
We present a framework that lets a service provider achieve end-to-end management objectives under varying load. Dynamic control actions are performed by a reinforcement learning (…
Learning Security Strategies through Game Play and Optimal Stopping
Kim Hammar, Rolf Stadler
We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stop…
A System for Interactive Examination of Learned Security Policies
Kim Hammar, Rolf Stadler
We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track…
Online Feature Selection for Efficient Learning in Networked Systems
Xiaoxuan Wang, Rolf Stadler
Current AI/ML methods for data-driven engineering use models that are mostly trained offline. Such models can be expensive to build in terms of communication and computing cost, an…
Intrusion Prevention through Optimal Stopping
Kim Hammar, Rolf Stadler
We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping…
Learning Intrusion Prevention Policies through Optimal Stopping
Kim Hammar, Rolf Stadler
We study automated intrusion prevention using reinforcement learning. In a novel approach, we formulate the problem of intrusion prevention as an optimal stopping problem. This for…
Online feature selection for rapid, low-overhead learning in networked systems
Xiaoxuan Wang, Forough Shahab Samani, Rolf Stadler
Data-driven functions for operation and management often require measurements collected through monitoring for model training and prediction. The number of data sources can be very…
Finding Effective Security Strategies through Reinforcement Learning and Self-Play
Kim Hammar, Rolf Stadler
We present a method to automatically find security strategies for the use case of intrusion prevention. Following this method, we model the interaction between an attacker and a de…
Predicting SLA Violations in Real Time using Online Machine Learning
Jawwad Ahmed, Andreas Johnsson, Rerngvit Yanggratoke +3
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SL…
The Accuracy of Tree-based Counting in Dynamic Networks
Supriya Krishnamurthy, John Ardelius, Erik Aurell +3
Tree-based protocols are ubiquitous in distributed systems. They are flexible, they perform generally well, and, in static conditions, their analysis is mostly simple. Under churn,…