HAL 9000: a Risk Manager for ITSs

HAL 9000 is an Intrusion Tolerant Systems (ITSs) Risk Manager, which assesses configuration risks against potential intrusions. It utilizes gathered threat knowledge and remains operational, even in the absence of updated information. Based on its advice, the ITSs can dynamically and proactively adapt to recent threats to minimize and mitigate future intrusions from malicious adversaries.Our goal is to reduce the risk linked to the exploitation of recently uncovered vulnerabilities that have not been classified and/or do not have a script to reproduce the exploit, considering the potential that they may have already been exploited as zero-day exploits. Our experiments demonstrate that the proposed solution can effectively learn and replicate National Vulnerability Database’s evaluation process with 99% accuracy.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/tps-isa62245.2024.00044
Primary Topic
Big Data and Business Intelligence
Type
article
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article

HAL 9000: a Risk Manager for ITSs

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Big Data and Business Intelligence
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HAL 9000: a Risk Manager for ITSs

Rolando Martins, Inês Dutra, Manuel E. Correia, Tadeu Freitas, João Soares, Carlos Novo, Behnam Shariati
article en

Abstract

HAL 9000 is an Intrusion Tolerant Systems (ITSs) Risk Manager, which assesses configuration risks against potential intrusions. It utilizes gathered threat knowledge and remains operational, even in the absence of updated information. Based on its advice, the ITSs can dynamically and proactively adapt to recent threats to minimize and mitigate future intrusions from malicious adversaries.Our goal is to reduce the risk linked to the exploitation of recently uncovered vulnerabilities that have not been classified and/or do not have a script to reproduce the exploit, considering the potential that they may have already been exploited as zero-day exploits. Our experiments demonstrate that the proposed solution can effectively learn and replicate National Vulnerability Database’s evaluation process with 99% accuracy.

Universidade do Porto (PT), University of Maryland, Baltimore County (US)
Openalex Percentile: Top 59%
Big Data and Business Intelligence
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