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AISGA: Multi-objective parameters optimization for countermeasures selection through genetic algorithm

Published: 17 August 2021 Publication History

Abstract

Cyberattacks targeting modern network infrastructures are increasing in number and impact. This growing phenomenon emphasizes the central role of cybersecurity and, in particular, the reaction against ongoing threats targeting assets within the protected system. Such centrality is reflected in the literature, where several works have been presented to propose full-fledged reaction methodologies to tackle offensive incidents’ consequences. In this direction, the work in [18] developed an immuno-based response approach based on the application of the Artificial Immune System (AIS) methodology. That is, the AIS-powered reaction is able to calculate the optimal set of atomic countermeasure to enforce on the asset within the monitored system, minimizing the risk to which those are exposed in a more than adequate time. To further contribute to this line, the paper at hand presents AISGA, a multi-objective approach that leverages the capabilities of a Genetic Algorithm (GA) to optimize the selection of the input parameters of the AIS methodology. Specifically, AISGA selects the optimal ranges of inputs that balance the tradeoff between minimizing the global risk and the execution time of the methodology. Additionally, by flooding the AIS-powered reaction with a wide range of possible inputs, AISGA intends to demonstrate the robustness of such a model. Exhaustive experiments are executed to precisely compute the optimal ranges of parameters, demonstrating that the proposed multi-objective optimization prefers a fast-but-effective reaction.

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  • (2024)ACE-WARP: A Cost-Effective Approach to Proactive and Non-Disruptive Incident Response in Kubernetes ClustersIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344903819(8204-8219)Online publication date: 26-Aug-2024
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cover image ACM Other conferences
ARES '21: Proceedings of the 16th International Conference on Availability, Reliability and Security
August 2021
1447 pages
ISBN:9781450390514
DOI:10.1145/3465481
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 August 2021

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Author Tags

  1. Countermeasures selection
  2. Cyberattacks countermeasures
  3. Genetic algorithm
  4. Parameters optimization
  5. Reaction framework

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ARES 2021

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Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

View all
  • (2024)Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-LearningBiomimetics10.3390/biomimetics90603079:6(307)Online publication date: 21-May-2024
  • (2024)Intrusion Response Systems for the 5G Networks and Beyond: A New Joint Security-vs-QoS Optimization ApproachIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.335817011:3(3039-3052)Online publication date: May-2024
  • (2024)ACE-WARP: A Cost-Effective Approach to Proactive and Non-Disruptive Incident Response in Kubernetes ClustersIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344903819(8204-8219)Online publication date: 26-Aug-2024
  • (2024)Intrusion Response System for In-Vehicle Networks: Uncertainty-Aware Deep Reinforcement Learning-based ApproachMILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)10.1109/MILCOM61039.2024.10773966(827-832)Online publication date: 28-Oct-2024

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