2 research outputs found
An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
Attack graphs are one of the main techniques used to automate the risk
assessment process. In order to derive a relevant attack graph, up-to-date
information on known attack techniques should be represented as interaction
rules. Designing and creating new interaction rules is not a trivial task and
currently performed manually by security experts. However, since the number of
new security vulnerabilities and attack techniques continuously and rapidly
grows, there is a need to frequently update the rule set of attack graph tools
with new attack techniques to ensure that the set of interaction rules is
always up-to-date. We present a novel, end-to-end, automated framework for
modeling new attack techniques from textual description of a security
vulnerability. Given a description of a security vulnerability, the proposed
framework first extracts the relevant attack entities required to model the
attack, completes missing information on the vulnerability, and derives a new
interaction rule that models the attack; this new rule is integrated within
MulVAL attack graph tool. The proposed framework implements a novel pipeline
that includes a dedicated cybersecurity linguistic model trained on the the NVD
repository, a recurrent neural network model used for attack entity extraction,
a logistic regression model used for completing the missing information, and a
novel machine learning-based approach for automatically modeling the attacks as
MulVAL's interaction rule. We evaluated the performance of each of the
individual algorithms, as well as the complete framework and demonstrated its
effectiveness.Comment: 16 pages, 11 figure
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE Β«CORPUS LINGUISTICSβ2019Β», June 24β28, 2019, St. Petersburg
Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ Π΄ΠΎΠΊΠ»Π°Π΄ΠΎΠ², ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ Β«ΠΠΎΡΠΏΡΡΠ½Π°Ρ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΠΊΠ°-2019Β» 24β28 ΠΈΡΠ½Ρ 2019 Π³. Π² Π‘Π°Π½ΠΊΡ-ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³Π΅.
Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΡΠΏΡΡΠΎΠ² ΡΠ΅ΠΊΡΡΠΎΠ² ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΏΡΠΈΠΎΡΠΈΡΠ΅ΡΠ½ΡΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΠΊΠ΅. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ΅ Π·Π½Π°ΠΊΠΎΠΌΠΈΡ ΡΡΠ΅Π½ΡΡ
Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ°ΠΌΠΈ ΠΈ Π½ΠΎΠ²ΡΠΌΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΡΠΌΠΈ Π² ΡΡΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ,
Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΎΠΏΡΡΠ° Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΠΎ ΠΊΠΎΡΠΏΡΡΠ½ΠΎΠΉ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΠΊΠ΅