Comparat i ve St udy of Feat ure sel ect i on by Based I nt rusi on Det ect i on Approach Usi ngMachi ne Learni ng Al gori t hms

I nt rusi on To The Network And Unaut hori zed Access To I nf ormat i on, Al t ers The Cont ent OfThe I nf ormat i on And Avoi d Sendi ng Them. Wi t h Eval uat i ng Di f f erent Met hods I n Thi s Fi el dAnd Met hods Compari son, And The Approach Percept ron- Based Network, Wi t h A Choi ce OfFeat ures And Di scret e, Has Caused A Lot Of Space I n The Perf ormance Of Cl assi f i ers. OnThe Ot her Hand, At The Deci si on Tree Model Wi t h Usi ng Less Feat ure Sel ect i on AndDi scret e Thei r Val ues, Reduced The Syst em Det ect i on Rat e And Tends To I ncrease TheMi scl assi f i cat i on. Al so The Markov Hi dden Model , Wi t h A Choi ce Of Feat ures And Thei rDi scret e, Cause Lot Of Error I n The Perf ormance Of The Cl assi f i er. Sel f -Organi zi ngMappi ng Dat a Met hod Al so Has The Abi l i t y To Show Sol i dari t y And I nt eract i on BetweenDat a And I nf ormat i on And The I nt eract i on Between Them On Each Ot her And At The Same Ti me. And To Reduce The Rat e Of Fal se Posi t i ves And I ncrease The Det ect i on Rat e I s UsedCorrect l y.Top Of Formi n Thi s Thesi s, To Cl assi f y I nt rusi on Det ect i on Model s, FromCombi nat i on Of St at i st i cal Met hods And Neural Networks Sel f -Organi zi ng Maps Are UsedFor Anomal y Det ect i on.

Keywords:

Probabi l i st i c SOM , Bayesi an SOM , PCA f i l t eri ng