Please use this identifier to cite or link to this item:
https://dspace.iiti.ac.in/handle/123456789/10910
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chaudhari, Narendra S.; | en_US |
dc.date.accessioned | 2022-11-03T19:48:34Z | - |
dc.date.available | 2022-11-03T19:48:34Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Sharmila, S. P., & Chaudhari, N. S. (2022). Conceptual study of prevalent methods for cyber-attack prediction doi:10.1007/978-981-19-2500-9_47 Retrieved from www.scopus.com | en_US |
dc.identifier.isbn | 978-9811924996 | - |
dc.identifier.issn | 2367-3370 | - |
dc.identifier.other | EID(2-s2.0-85136923651) | - |
dc.identifier.uri | https://doi.org/10.1007/978-981-19-2500-9_47 | - |
dc.identifier.uri | https://dspace.iiti.ac.in/handle/123456789/10910 | - |
dc.description.abstract | Autonomous proactive intrusion prediction system (IPrS) for predicting a upcoming attack by monitoring the network for every instant and for every event is needed for security of systems. There are intrusion detection system (IDS) and intrusion prevention system (IPS) for detecting and preventing intrusions; however, analysis of integrated functionality for various components in such systems has not been fully reported in the literature. In this paper, we review various techniques for attack prediction as reported by various researchers for IDS and IPS. We identify the limitations of these existing techniques for attack prediction. We propose model to predict attack before it occurs by integrating the functionalities of various components in IDS and IPS, namely security database, attack graphs, hidden Markov model, proactive IDS and automated IRS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.source | Lecture Notes in Networks and Systems | en_US |
dc.title | Conceptual Study of Prevalent Methods for Cyber-Attack Prediction | en_US |
dc.type | Conference Paper | en_US |
Appears in Collections: | Department of Computer Science and Engineering |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
Altmetric Badge: