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Cost analysis for classification-based autonomous response systems
Purwanto Y.a,b, Kuspriyantoa, Hendrawana, Rahardjo B.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 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]© The Japan Society for Analytical Chemistry.Recently, cost-based Autonomous Response System (ARS) proposals are based on intrusion detection analy- sis. However, the implementation of the analysis in multi- class classification-based ARS potentially leads to a wrong response action set decision. This is because the analysis may produces irrelevant response value, as it is not consid- ering the false possibility in a true positive condition. In this paper, we introduce ARS based on cost analysis from a multi-class classification output. The analysis is not only considering the possibility of a right response, but also the possibility of a wrong response from false classi- fication prediction. The response value and expected lost rate are introduced to quantitatively estimate the best response action set. Our simulation for Denial of Service (DoS) attack cases, confirmed the capability of response action set decision algorithm. Our proposed system pro- vides more accurate estimation of response value which leads to lower expected lost rate.[/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]Accurate estimation,Autonomous response,Best response,Cost analysis,Decision algorithms,Denial of Service,Multi-class classification,True positive[/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]Autonomous response system,Classification,Decision analysis,Denial of service,Intrusion detection[/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][/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/10.6633/IJNS.201801.20(1).13[/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]