Optimal Source-Based Filtering of Malicious Traffic

TL;DR

This paper develops optimal algorithms for source-based filtering of malicious Internet traffic using ACLs, balancing effectiveness and resource constraints, with practical evaluation showing significant benefits.

Contribution

It introduces computationally efficient algorithms for optimal source prefix filtering tailored to various attack scenarios and policies.

Findings

Algorithms significantly reduce malicious traffic while preserving legitimate traffic.

Evaluation on real logs demonstrates practical effectiveness.

Optimal filtering strategies outperform heuristic approaches.

Abstract

In this paper, we consider the problem of blocking malicious traffic on the Internet, via source-based filtering. In particular, we consider filtering via access control lists (ACLs): these are already available at the routers today but are a scarce resource because they are stored in the expensive ternary content addressable memory (TCAM). Aggregation (by filtering source prefixes instead of individual IP addresses) helps reduce the number of filters, but comes also at the cost of blocking legitimate traffic originating from the filtered prefixes. We show how to optimally choose which source prefixes to filter, for a variety of realistic attack scenarios and operators' policies. In each scenario, we design optimal, yet computationally efficient, algorithms. Using logs from Dshield.org, we evaluate the algorithms and demonstrate that they bring significant benefit in practice.