In　cognitive　radio　network　(CRN),　effective　spectrum　management　provides　better　quality　of　service.　The　spectrum　is　limited　but　the　significance　of　the　spectrum　is　increasing　at　each　network　generation.　Due　to　the　ineffective　spectrum　allocation　policies,　several　researches　have　indicated　that　a　vast　segment　of　the　licensed　radio　is　not　viably　used.　A　CRN　is　an　intelligent　spectrum　utilization　innovation　that　provides　better　spectrum　interface.　Spectrum　sensing　detects　unused　spectrum　in　the　manner　that　protects　interferences　to　the　authorized　users.　In　principle,　the　secondary　user　(SU)　receives　the　primary　user　(PU)　signal　and　reports　it　to　the　fusion　center　for　decision　or　spectrum　allocation.　The　SU　cooperates　in　the　detection　of　the　presence　or　absence　of　the　PU.　This　type　of　spectrum　sensing　is　called　cooperative　spectrum　sensing.　However,　the　significance　of　this　type　of　spectrum　sensing　is　blurred　by　the　security　problems.　Malicious　users　can　deliberately　report　misleading　information　regarding　the　presence　of　the　PU.　Hence,　in　this　paper,　a　support　vector　machine　learning　algorithm　is　proposed　to　statistically　learn　the　behavior　of　the　malicious　users　and　it　classifies　the　legitimate　SU　and　malicious　users.　A　particle　swarm　optimization　algorithm　is　also　integrated　to　learn　the　smallest　possible　distinguishable　malicious　users＇　energy　report　deviation　from　the　legitimate　SUs.　The　probability　of　detection　and　energy　of　detection　have　been　applied　to　evaluate　the　contribution　of　the　proposed　method.　Finally,　the　simulation　results　have　confirmed　that　better　spectrum　management　can　be　derived　from　the　proposed　statistical　approach.