%0 Journal Article %1 noauthororeditor %A Verma, Rahul Deo %A Samaddar, Shefalika Ghosh %A Samaddar, A. B. %D 2021 %J International Journal of Computer Networks & Communications (IJCNC) %K (BGP) (ELM) Anomaly Border Detection Extreme Gateway Learning Machine Protocol %N 6 %P 41-52 %R 10.5121/ijcnc.2021.13603 %T Securing BGP by Handling Dynamic Network Behavior and Unbalanced Datasets %U https://aircconline.com/ijcnc/V13N6/13621cnc03.pdf %V 13 %X The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.

@article{noauthororeditor, abstract = {The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique. }, added-at = {2021-12-22T08:04:35.000+0100}, author = {Verma, Rahul Deo and Samaddar, Shefalika Ghosh and Samaddar, A. B.}, biburl = {https://www.bibsonomy.org/bibtex/29b75231b97643d33642a6138bf518e8c/laimbee}, doi = {10.5121/ijcnc.2021.13603}, interhash = {39d317428892220fdd73ef9d52cdee5b}, intrahash = {9b75231b97643d33642a6138bf518e8c}, issn = {ISSN 0974 - 9322 (Online); 0975 - 2293 (Print)}, journal = {International Journal of Computer Networks & Communications (IJCNC)}, keywords = {(BGP) (ELM) Anomaly Border Detection Extreme Gateway Learning Machine Protocol}, language = {english}, month = {November}, number = 6, pages = {41-52}, timestamp = {2021-12-22T08:04:35.000+0100}, title = {Securing BGP by Handling Dynamic Network Behavior and Unbalanced Datasets }, url = {https://aircconline.com/ijcnc/V13N6/13621cnc03.pdf}, volume = 13, year = 2021 }