Improving cyber-attack predictions through information foraging

Dalton, Adam; Dorr, Bonnie J.; Liang, Leon; Hollingshead, Kristy

doi:10.1109/bigdata.2017.8258509

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Sentiment analysis of social networks was also a data source for Shu et al [17] in 2018. Information foraging for improving cyber attack predictions was also discussed by Dalton et al [105] in 2017. The authors, however, discuss various strategies for information foraging and only briefly mention the data sources with which they work.…”

Section: Other Approachesmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

227

110

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This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.

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Section: Other Approachesmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

227

110

View full text Add to dashboard Cite

show abstract

“…These findings can be used to improve the profile of certain cyber criminals and better predict cyber incidents initiated by these particular cyber threat actors. 30 Dalton et al (2017) propose to improve cyber incident forecasting through cognitive augmentation and information foraging using publicly available data sources, such as the data available on the website Hackmageddon, that have not been typically used for cyber incident prediction. 31 27 Palash Goyal, Tozammel Hossain, Ashok Deb, Nazgol Tavabi, Nathan Barley, Andrés Abeliuk, Emilio Ferrara, and Kristina Lerman, "Discovering Signals from Web Sources to Predict Cyber Attacks," arXiv, preprint, August 2018.…”

Section: Novel Data Collection Domains and Methodological Trends That...mentioning

confidence: 99%

RAND's Scalable Warning and Resilience Model (SWARM): Enhancing Defenders' Predictive Power in Cyberspace

Lilly¹,

Moore²,

Hodgson³

et al. 2021

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“…For example, back up matches the class template containing advance, and pivot matches the class template containing rotate. We then considered the Non-Spatial Subset for generation of cyber-related notifications, a sub-task of an ongoing cyber-attack prediction project (Dalton et al, 2017). We determined that the Fill, Overlap, and Block patterns apply to members of the same classes that were relevant to robot navigation.…”

Section: Stylus: Spatial and Non-spatial Subset Of Lvd Classesmentioning

confidence: 99%

The Case for Systematically Derived Spatial Language Usage

Dorr¹,

Voss²

2018

Proceedings of the First International Workshop on Spatial Language Understanding

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This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexicalsemantic resources. Furthermore, even with access to such resources, effective solutions to many application areas such as robot navigation and narrative generation also require additional knowledge at the syntax-semantics interface to cover the wide range of spatial expressions observed and available to natural language speakers. We ground our insights in, and present our extensions to, an existing lexico-semantic resource, covering 500 semantic classes of verbs, of which 219 fall within a spatial subset. We demonstrate that these extensions enable systematic derivation of regular patterns of spatial language without requiring manual annotation.

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Improving cyber-attack predictions through information foraging

Cited by 9 publications

References 11 publications

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

RAND's Scalable Warning and Resilience Model (SWARM): Enhancing Defenders' Predictive Power in Cyberspace

The Case for Systematically Derived Spatial Language Usage

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