Biomimethics: a critical perspective on the ethical implications of biomimetics in technological innovation

Broeckhoven, Chris; Winters, S. R.

doi:10.1088/1748-3190/ace7a2

Cited by 4 publications

(2 citation statements)

References 76 publications

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“…Artificial intelligence (AI) plays a pivotal role in this evolution, especially through the development of bio-inspired optimization algorithms. These algorithms, inspired by natural processes, have been instrumental in enhancing cyber risk management strategies by offering innovative solutions and efficiencies [4]. Despite their effectiveness in solving complex problems, these algorithms can encounter limitations, such as stagnation at local optima, which poses a challenge to achieving global optimization [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Olivares,

Salinas,

Ravelo

et al. 2024

Preprint

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Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Olivares,

Salinas,

Ravelo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) plays a pivotal role in this evolution, especially through the development of bio-inspired optimization algorithms. These algorithms, inspired by natural processes, have been instrumental in enhancing cyber-risk management strategies by offering innovative solutions and efficiencies [ 4 ]. Despite their effectiveness in solving complex problems, these algorithms can encounter limitations, such as stagnation at local optima, which poses a challenge to achieving global optimization [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

Olivares,

Salinas,

Ravelo

et al. 2024

Biomimetics

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In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm—with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning’s potential to boost cybersecurity measures in rapidly evolving threat environments.

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Imagining sustainable futures: a response to Buhr

Gerola

2024

Philos. Technol.

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In this response, I express sympathy for Buhr's proposal to expand our typology into an ethical framework of eco-normative profiling of (sustainable) technologies. I reflect on crucial issues that this framework should include, offering some words of caution against taking concepts such as Anthropocene and sustainability too lightly. I end with an invitation to include multiple and diverse perspectives about what sustainable futures could look like.

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Biomimethics: a critical perspective on the ethical implications of biomimetics in technological innovation

Cited by 4 publications

References 76 publications

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

Imagining sustainable futures: a response to Buhr

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