HUSÁK, Martin, Jana KOMÁRKOVÁ, Elias BOU-HARB and Pavel ČELEDA. Survey of Attack Projection, Prediction, and Forecasting in Cyber Security. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS. PISCATAWAY: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019, vol. 21, No 1, p. 640-660. ISSN 1553-877X.
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Basic information
Original name Survey of Attack Projection, Prediction, and Forecasting in Cyber Security
Authors HUSÁK, Martin (203 Czech Republic, guarantor, belonging to the institution), Jana KOMÁRKOVÁ (203 Czech Republic, belonging to the institution), Elias BOU-HARB and Pavel ČELEDA (203 Czech Republic, belonging to the institution).
Edition IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, PISCATAWAY, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019, 1553-877X.
Other information
Original language English
Type of outcome Article in a journal
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14610/19:00108866
Organization Ústav výpočetní techniky – Repository – Repository
UT WoS 000459730200024
Keywords in English cyber security;intrusion detection;situational awareness;prediction;forecasting;model checking
Links EF16_019/0000822, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 6/9/2020 06:12.
Abstract
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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