On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context ofWeb Attacks
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Díaz Verdejo, Jesús Esteban; Muñoz-Calle, Javier; Estepa Alonso, Antonio; Estepa Alonso, Rafael; Madinabeitia, GermánEditorial
MDPI
Materia
cybersecurity intrusion detection signature-based IDS
Date
2022-01-14Referencia bibliográfica
Díaz Verdejo, J. et. al. Appl. Sci. 2022, 12, 852. [http://doi.org/10.3390/app12020852]
Sponsorship
grant PID2020-115199RB-I00 provided by the Spanish ministry of Industry under the contract MICIN/AEI/10.13039/501100011033; FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades under project PYC20-RE-087-USEAbstract
Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal
of security components of most organizations. They can find traces of known attacks in the network
traffic or host events for which patterns or signatures have been pre-established. SIDS include
standard packages of detection rulesets, but only those rules suited to the operational environment
should be activated for optimal performance. However, some organizations might skip this tuning
process and instead activate default off-the-shelf rulesets without understanding its implications and
trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the
performance of SIDS. We experimentally explore the performance of three SIDS in the context of web
attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort,
ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of
alert generated by each detector in a real-life case using a large trace from a public webserver. Results
show that the maximum detection rate achieved by the SIDS under test is insufficient to protect
systems effectively and is lower than expected for known attacks. Our results also indicate that the
choice of predefined settings activated on each detector strongly influences its detection capability
and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating
the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also
found that using various SIDS for a cooperative decision can improve the precision or the detection
rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS
with default configurations as core elements for protection in the context of web attacks. Finally, we
provide an efficient method for systematically determining which rules deactivate from a ruleset to
significantly reduce the false alarm rate for a target operational environment. We tested our approach
using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h
of work.