Towards a Systematic View on Cybersecurity Ecology

Mazurczyk, Wojciech; Drobniak, Szymon M.; Moore, Sean

doi:10.1007/978-3-319-38930-1_2

Cited by 18 publications

(16 citation statements)

References 39 publications

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“…Using terminologies from ecological biology, the entire CR network can be viewed as an ecosystem [3], where SUs of different spectrum access strategies are different species, the common resource shared by all species are the vacant wireless spectrum, and the fitness of an SU is the utility it received given the profile of spectrum access strategies of all SUs. Two essential operations must be ensured to guarantee the success of the opportunistic access of CR ecosystem:'…”

Section: A Cr Ecosystemsmentioning

confidence: 99%

“…In evolutionary aspect, users with different access strategies and behaviors are regarded as different species. Originally, all PUs and most of SUs behave normally, which are considered as native species in a balanced ecosystem [14], [3]. The stealthy MUs involving later try to prevent usable reception on victims by forcing their received SINR lower than the threshold.…”

Section: The Proposed Ecology-based Dos Attackmentioning

confidence: 99%

“…In this paper we propose to analyze the stability of a CR network from the perspective of evolution from biology. Using terminologies from ecological biology, the entire CR network can be viewed as an ecosystem [3], where SUs of different spectrum access strategies are different species, the common resource shared by all species are the vacant wireless spectrum, and the fitness of an SU is the utility it received given the profile of spectrum access strategies of all SUs. Notably, the well-known "survival of the fittest" phenomenon applies to CR network as well.…”

Section: Introductionmentioning

confidence: 99%

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Ecology-Based DoS Attack in Cognitive Radio Networks

Cheng

Chen

2016

2016 IEEE Security and Privacy Workshops (SPW)

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Cognitive radio technology, which is designed to enhance spectrum utilization, depends on the success of opportunistic access, where secondary users (SUs) exploit spectrum void unoccupied by primary users (PUs) for transmissions. We note that the system behaviors are very similar to the interactions among different species coexisting in an ecosystem. However, SUs of a selfish nature or of misleading information may make concurrent transmissions with PUs for additional incentives, and thus disrupt the entire ecosystem. By exploiting this vulnerability, this paper proposes a novel distributed denial-of-service (DoS) attack where invasive species, i.e., malicious users (MUs), induce originally normal-behaved SUs to execute concurrent transmissions with PUs and thus collapse the cognitive radio network. We adopt stochastic geometry to model the spatial distributions of PUs, SUs, and MUs for the analysis of the mutual interference among them. The access strategy of each SU in the spectrum sharing ecosystem, which evolves with the experienced payoffs and interference, is modeled by an evolutionary game. Based on the evolutionary stable strategy concept, we could efficiently identify the fragile operating region at which normal-behaved SUs are eventually evolved to conduct concurrent transmissions and thus to cause the ruin of the network.Index Terms-cognitive radio, denial-of-service (DoS) attack, ecosystems, evolutionary game, selfish behavior

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Section: A Cr Ecosystemsmentioning

confidence: 99%

Section: The Proposed Ecology-based Dos Attackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ecology-Based DoS Attack in Cognitive Radio Networks

Cheng

Chen

2016

2016 IEEE Security and Privacy Workshops (SPW)

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“…Such dynamics are often observed in nature, e.g., between predators and prey, or hosts and parasites. For the case of security, this leads to a continual contention to develop offensive/defensive measures as fast as possible, to at least temporarily dominate the other side [10].…”

Section: Introductionmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

Self Cite

100

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“…Motivation. Bio-Inspired algorithm development in cybersecurity frequently relies on isolated analogy building between particular threats and specific biological systems [1]. While meticulous care is often taken in building these analogies and then tailoring the borrowed algorithms to meet the needs of cyber systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Case Study in Tailoring a Bio-InspiredCyber-Security Algorithm: DesigningAnomaly Detection for Multilayer Networks

Suárez

Gallos

Fefferman

2018

JCSM

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Although bio-inspired designs for cybersecurity have yielded many elegant solutions to challenging problems, the vast majority of these efforts have been ad hoc analogies between the natural and human-designed systems. We propose to improve on the current approach of searching through the vast diversity of existing natural algorithms for one most closely resembling each new cybersecurity challenge, and then trying to replicate it in a designed cyber setting. Instead, we suggest that researchers should follow a protocol of functional abstraction, considering which features of the natural algorithm provide the efficiency/effectiveness in the real world, and then use those abstracted features as design components to build purposeful, tailored (perhaps even optimized) solutions. Here, we demonstrate how this can work by considering a case study employing this method. We design an extension of an existing (and ad hoc-created) algorithm, DIAMoND, for application beyond its originally intended solution space (detection of Distributed Denial

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Towards a Systematic View on Cybersecurity Ecology

Cited by 18 publications

References 39 publications

Ecology-Based DoS Attack in Cognitive Radio Networks

Ecology-Based DoS Attack in Cognitive Radio Networks

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

A Case Study in Tailoring a Bio-InspiredCyber-Security Algorithm: DesigningAnomaly Detection for Multilayer Networks

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