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AuthorChkirbene Z.
AuthorErbad A.
AuthorHamila R.
AuthorGouissem A.
AuthorMohamed A.
AuthorGuizani M.
AuthorHamdi M.
Available date2022-04-21T08:58:25Z
Publication Date2020
Publication NameIEEE Wireless Communications and Networking Conference, WCNC
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/WCNC45663.2020.9120706
URIhttp://hdl.handle.net/10576/30096
AbstractRecently, machine learning techniques are gaining a lot of interest in security applications as they exhibit fast processing with real-time predictions. One of the significant challenges in the implementation of these techniques is the collection of a large amount of training data for each new potential attack category, which is most of the time, unfeasible. However, learning from datasets that contain a small training data of the minority class usually produces a biased classifiers that have a higher predictive accuracy for majority class(es), but poorer predictive accuracy over the minority class. In this paper, we propose a new designed attacks weighting model to alleviate the problem of imbalanced data and enhance the accuracy of minority classes detection. In the proposed system, we combine a supervised machine learning algorithm with the node1 past information. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. Then, the system stores these decisions in a database and exploits them for the weighted attacks classification model. Thus, for each attack class, the weight that maximizes the detection of the minority classes will be computed and the final combined decision is generated. In this work, we use the UNSW dataset to train the supervised machine learning model. The simulation results show that the proposed model can effectively detect intrusion attacks and provide better accuracy, detection rates and lower false alarm rates compared to state-of-the art techniques.1In this document we will use the words 'node' to represent computing, storage, physical, and virtual machines. 2020 IEEE.
SponsorQatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectClassification (of information)
Digital storage
Intrusion detection
Learning systems
Network security
Supervised learning
Classification models
False alarm rate
Machine learning techniques
Predictive accuracy
Real-time prediction
Security application
State-of-the-art techniques
Supervised machine learning
Learning algorithms
TitleWeighted Trustworthiness for ML Based Attacks Classification
TypeConference Paper
Volume Number2020-May
dc.accessType Abstract Only


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