In　large-scale　network　scenarios,　network　security　data　are　characterized　by　complex　association　and　redundancy,　forming　network　security　big　data,　which　makes　network　security　attack　and　defense　more　complicated.　In　this　paper,　the　authors　propose　a　framework　for　network　attack　risk　control　in　large-scale　network　topology,　called　NARC.　Using　NARC,　a　user　can　determine　the　influence　level　of　different　nodes　on　the　diffusion　of　attack　risk　in　complex　network　topology,　thus　giving　optimal　risk　control　decisions.　Specifically,　this　paper　designs　a　topology-oriented　node　importance　assessment　model,　combined　with　node　vulnerability　correlation　analysis,　to　construct　a　diffusion　network　of　attack　risks　for　identifying　potential　attack　paths.　Furthermore,　the　optimal　risk　control　node　selection　method　based　on　game　theory　is　proposed　to　obtain　the　optimal　set　of　defense　nodes.　The　experimental　results　demonstrate　the　feasibility　of　the　proposed　NARC,　which　helps　to　ease　the　risk　of　network　attacks.　©　2022,　IGI　Global.