A Robust Control Framework for Malware Filtering

TL;DR

This paper introduces a robust control framework for malware filtering that balances security and usability, using H infinity-optimal control theory to develop dynamic feedback algorithms verified through network simulations.

Contribution

It presents a novel control-based approach for malware filtering that improves over heuristic methods by applying H infinity control theory to network security.

Findings

Dynamic feedback filter outperforms heuristic approaches

Numerical analysis shows improved security-utility tradeoff

Packet-level simulations validate the proposed framework

Abstract

We study and develop a robust control framework for malware filtering and network security. We investigate the malware filtering problem by capturing the tradeoff between increased security on one hand and continued usability of the network on the other. We analyze the problem using a linear control system model with a quadratic cost structure and develop algorithms based on H infinity-optimal control theory. A dynamic feedback filter is derived and shown via numerical analysis to be an improvement over various heuristic approaches to malware filtering. The results are verified and demonstrated with packet level simulations on the Ns-2 network simulator.