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AuthorKholidy, Hisham A.
AuthorErradi, Abdelkarim
AuthorAbdelwahed, Sherif
Available date2023-04-10T09:10:05Z
Publication Date2014
Publication NameProceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014
ResourceScopus
URIhttp://dx.doi.org/10.1109/AIMS.2014.64
URIhttp://hdl.handle.net/10576/41821
AbstractIn spite of the functional and economic benefits of the cloud-computing systems, they also expose entirely several attacks. Most of the current cloud security technologies do not provide early warnings about such attacks. The early warnings give the cloud administrator or the auto response controller ample time to take preventive measures. This paper discusses our three prediction models that are integrated to our Autonomic Cloud Intrusion Detection Framework (ACIDF) namely, The Finite State Hidden Markov prediction model (FSHMPM), The Finite Context Prediction Model (FCPM) that uses a Variable Order Markov Model (VMM) with a Probabilistic Suffix Tree (PST), and HoltWinter Prediction Model (HWPM). We compare these models and highlight the pros and cons of each one. The prediction models were evaluated against DARPA 2000 dataset. The FSHMPM has successfully fired the early warnings 39.6 minutes before the launching of the LLDDoS1.0 attack. The FCPM has successfully fired the early warnings 58.98 minutes before the launching of the same attack. The HWPM has an error rate of 42.07% for HTTP flow forecast and 44.02% for FTP one. 2014 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCloud computing
HMM
HoltWinter
intrusion prediction
multi-staged attacks
Probability Suffix Tree
VMM
TitleAttack Prediction Models for Cloud Intrusion Detection Systems
TypeConference Paper
Pagination270-275
dc.accessType Abstract Only


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