D 2022

Limiting the Size of a Predictive Blacklist While Maintaining Sufficient Accuracy

ŠUĽAN, Samuel and Martin HUSÁK

Basic information

Original name

Limiting the Size of a Predictive Blacklist While Maintaining Sufficient Accuracy

Authors

ŠUĽAN, Samuel and Martin HUSÁK

Edition

Vienna, The 17th International Conference on Availability, Reliability and Security (ARES 2022), p. "22:1"-"22:6", 6 pp. 2022

Publisher

ACM

Other information

Language

English

Type of outcome

Proceedings paper

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL

Marked to be transferred to RIV

Yes

RIV identification code

RIV/00216224:14610/22:00126039

Organization

Ústav výpočetní techniky – Repository – Repository

ISBN

978-1-4503-9670-7

DOI

https://doi.org/10.1145/3538969.3539007

UT WoS

001122620500022

EID Scopus

2-s2.0-85136945289

Keywords in English

cybersecurity;blacklist;limitation;prediction

Links

EF16_019/0000822, research and development project.
Changed: 15/5/2024 03:40, RNDr. Daniel Jakubík

Abstract

In the original language

Blacklists (blocklists, denylists) of network entities (e.g., IP addresses, domain names) are popular approaches to preventing cyber attacks. However, the limited capacity of active network defense devices may not hold all the entries on a blacklist. In this paper, we evaluated two strategies to limit the size of a blacklist and their impact on the blacklist's accuracy. The first strategy is setting the maximal size of a blacklist; the second is setting an expiration time to blacklist items. Short-term attack predictions are typically more precise, and, thus, the recent blacklist entries should be more valuable than older ones. Our experiment shows that the blacklists reduced to half of the size via either strategy achieve only a 25% drop in accuracy.
Displayed: 2/5/2026 18:53