[vc_empty_space][vc_empty_space]
Minimal triangle area mahalanobis distance for stream homogeneous group-based DDoS classification
Purwanto Y.a,b, Kuspriyantoa, Hendrawana, Rahardjo B.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia
b Telkom University, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018, School of Electrical Engineering and Informatics. All rights reserved.An Intrusion Detection System (IDS) which implement a group-based classification algorithm, theoretically has the benefit of higher accuracy. Unfortunately, higher accuracy only achieved if the observed group is homogeneous from a certain distribution. Recently, a distributed denial of service (DDoS) attack consists of multiple botnets which produce multi types of traffic in one attack session. It makes the IDS suffers from decreasing accuracy as the increasing heterogeneity within the observed group. To address the problem, we propose homogeneous grouping algorithm based on triangle area Mahalanobis distance to support IDS which implement group-based data analysis. First, the Mahalanobis distance measurement was used to construct homogeneous groups. Then, the covariance matrix of each homogeneous group was classified using a decision tree classifier. Classification performance was evaluated using known KDDCup 99 dataset. The results pointed out that the used of homogeneous grouping algorithm improve the classification performance for natural and mixed random DDoS traffic.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification,Covariance,Decision tree,Distributed denial of service,Intrusion detection system,Mahalanobis distance[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Directorate of Research and Community Service partially supported this research, the General Directorate of Research and Development Strengthening, the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia under the research contract FY 2018 No. 014/PNLT3/PPM/2018.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.15676/ijeei.2018.10.2.12[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]