Predation and the cost of replication: New approaches to malware prevention?

Ford, Richard; Bush, Mark B.; Булатов, А. С.

doi:10.1016/j.cose.2006.02.002

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…For example, [53] is an empirical study of malware evolution. Arguments for employing nature-inspired technologies for cyber security that mention how biological and ecological systems use information to adapt in an unpredictable world include [34,45,60,85,114].…”

Section: Cyber Securitymentioning

confidence: 99%

Adversarial genetic programming for cyber security: a rising application domain where GP matters

O’Reilly¹,

Toutouh²,

Pertierra³

et al. 2020

Genet Program Evolvable Mach

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Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security encompasses extant and immediate research efforts in a vital problem domain, arguably occupying a position at the frontier where GP matters. Additionally, it prompts research questions around evolving complex behavior by expressing different abstractions with GP and opportunities to reconnect to the Machine Learning, Artificial Life, Agent-Based Modeling and Cyber Security communities. We present a framework called RIVALS which supports the study of network security arms races. Its goal is to elucidate the dynamics of cyber networks under attack by computationally modeling and simulating them.

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Section: Cyber Securitymentioning

confidence: 99%

Adversarial genetic programming for cyber security: a rising application domain where GP matters

O’Reilly¹,

Toutouh²,

Pertierra³

et al. 2020

Genet Program Evolvable Mach

View full text Add to dashboard Cite

show abstract

“…Based upon interactions and behaviors that exist between a predator and its prey, a range of innovations have been developed. To this end, [77] applies the predator-prey dynamic; the principles of the cost of predation in particular, as a new approach for malware prevention. Similarly…”

Section: Enhancing Cyber Security Using Artificial Intelligencementioning

confidence: 99%

A Bio-inspired Approach To Cyber Security

Mthunzi¹,

Benkhelifa²,

Bosakowski³

et al. 2019

Machine Learning for Computer and Cyber Security

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Owing to a growing reliance on information, technology and connectivity, Cyberspace has become the lifeline and interactive place for modern life. As such, Cyber security challenges are a global phenomenon whose adverse implications are catastrophic. Cyberspace is complex and unpredictable; its global connectedness and an explosion of data increases the threat surface as cyber infrastructures become highly complex and dynamic. Managing, i.e. ensuring and assuring security in cyberspace requires inspiration from advanced complex systems. Through evolution, nature has developed natural propensities in complex systems (including animalia and plants) that enable survival through adaptation. Predation-avoidance and anti-predation techniques employed by non-extinct preys could be exploited/adopted as mechanisms for adaptation through their application in Cyber security. This chapter presents an overall review of the current state of the Cyber security landscape. In addition, it demonstrates through further review, significant trends towards bio-inspired approaches as unconventional solutions to problems in other fields. Drawing from survivable preys in nature, the chapter speculates solutions for Cyberspace and Cyber security as follows; given an old problem (Pold) with an old solutions (Sold), a new problem (Pnew) Computer and Cyber Security: Principles, Algorithm, Applications and Perspectives can be conceptualized with new partial and perhaps null solutions (Snew) in the solutions space Sold to Snew.

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“…They considered each type of a vulnerable device as a heterogeneous species and investigated what level of species diversification is necessary to prevent a malicious attack from causing a failure to the entire network. Subsequently, in [5] it was discovered that the cost to the predator in seeking its prey drastically impacts the predation process. In particular it has been observed that even fairly simple strategies for raising the cost of predation can result in significant reduction in outbreak size.…”

Section: Bio-inspired Cybersecurity Inspired By Organisms' Interactionsmentioning

confidence: 99%

Towards a Systematic View on Cybersecurity Ecology

Mazurczyk

Drobniak

Moore

2016

Advanced Sciences and Technologies for Security Applications

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Current network security systems are progressively showing their limitations. One credible estimate is that only about 45% of new threats are detected. Therefore it is vital to find a new direction that cybersecurity development should follow. We argue that the next generation of cybersecurity systems should seek inspiration in nature. This approach has been used before in the first generation of cybersecurity systems; however, since then cyber threats and environment have evolved significantly, and accordingly the first-generation systems have lost their effectiveness. A next generation of bioinspired cybersecurity research is emerging, but progress is hindered by the lack of a framework for mapping biological security systems to their cyber analogies. In this paper, using terminology and concepts from biology, we describe a cybersecurity ecology and a framework that may be used to systematically research and develop bio-inspired cybersecurity.

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Predation and the cost of replication: New approaches to malware prevention?

Cited by 8 publications

References 12 publications

Adversarial genetic programming for cyber security: a rising application domain where GP matters

Adversarial genetic programming for cyber security: a rising application domain where GP matters

A Bio-inspired Approach To Cyber Security

Towards a Systematic View on Cybersecurity Ecology

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