Cybersecurity Dynamics: A Foundation for the Science of Cybersecurity

Xu, Shouhuai

doi:10.1007/978-3-030-10597-6_1

Cited by 41 publications

(23 citation statements)

References 114 publications

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“…Security is multidimensional, so measuring security would require a set of metrics for each attribute in consideration that is dependent on its intended purpose, audience, and goals 1 . Xu 4 thoroughly surveyed security quantification in the Cybersecurity Dynamics proposal, outlining different metrics per representation of security being measured. The general model identifies four key measurements: (a) the attack‐defense structure, which is the connectivity between attackers and defenders, (b) the susceptibility of the systems, that is, the exposure and vulnerabilities, (c) the defense capabilities, which is the effectiveness in detecting or stopping an attack, and (d) the attack capabilities, a representation of the proficiency of the attacker in evading detecting and exploiting vulnerabilities.…”

Section: Background and Related Workmentioning

confidence: 99%

MTD to set network slice security as a KPI

Cunha

Corujo

Barraca

et al. 2020

Internet Technology Letters

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Key Performance Indicators (KPIs) are a higher‐level characterization of the performance of a network slice, meant to be assessable at any time. Bodies such as the GSM Alliance have proposed the use of KPIs, including, but not limited to, latency, throughput, power consumption, and security. However, while latency, throughput, and power consumption are mensurable universally, security is much harder to measure. In this article, we propose using a Moving Target Defense (MTD) approach and measurable network properties to establish a new straightforward network security metric for underlying resilience against network‐centric attacks. We called it DynSec, a comprehensive model for basic network security within the network slice. Monte Carlo experimentation showed that DynSec is accurate and suitable as a KPI.

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Section: Background and Related Workmentioning

confidence: 99%

MTD to set network slice security as a KPI

Cunha

Corujo

Barraca

et al. 2020

Internet Technology Letters

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“…However, these studies do not consider features pertinent to the COVID-19 pandemic, which are we leverage. Nevertheless, the present study fall under the umbrella of cybersecurity data analytics [16]- [20], which in turn belong to the Cybersecurity Dynamics framework [21]- [25].…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Characterization and Detection of COVID-19 Themed Malicious Websites

Pritom

Schweitzer

Bateman

et al. 2020

2020 IEEE International Conference on Intelligence and Security Informatics (ISI)

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COVID-19 has hit hard on the global community, and organizations are working diligently to cope with the new norm of "work from home". However, the volume of remote work is unprecedented and creates opportunities for cyber attackers to penetrate home computers. Attackers have been leveraging websites with COVID-19 related names, dubbed COVID-19 themed malicious websites. These websites mostly contain false information, fake forms, fraudulent payments, scams, or malicious payloads to steal sensitive information or infect victims' computers. In this paper, we present a data-driven study on characterizing and detecting COVID-19 themed malicious websites. Our characterization study shows that attackers are agile and are deceptively crafty in designing geolocation targeted websites, often leveraging popular domain registrars and toplevel domains. Our detection study shows that the Random Forest classifier can detect COVID-19 themed malicious websites based on the lexical and WHOIS features defined in this paper, achieving a 98% accuracy and 2.7% false-positive rate.

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“…Intrusion detection is an important field of cybersecurity data analytics [28]- [36], which is one pillar underlying the Cybersecurity Dynamics framework [37], [38] that aims to model and quantify cybersecurity from a holistic perspective. The other two pillars are known as first-principle modeling and analysis [39]- [45] and cybersecurity metrics [46]- [49].…”

Section: B Related Workmentioning

confidence: 99%

A Case Study on using Deep Learning for Network Intrusion Detection

Fernández

2019

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)

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Deep Learning has been very successful in many application domains. However, its usefulness in the context of network intrusion detection has not been systematically investigated. In this paper, we report a case study on using deep learning for both supervised network intrusion detection and unsupervised network anomaly detection. We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection.

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Cybersecurity Dynamics: A Foundation for the Science of Cybersecurity

Cited by 41 publications

References 114 publications

MTD to set network slice security as a KPI

MTD to set network slice security as a KPI

Data-Driven Characterization and Detection of COVID-19 Themed Malicious Websites

A Case Study on using Deep Learning for Network Intrusion Detection

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