Malware Detection System using Machine Learning and DATA-Mining Techniques

Malware Detection System using Machine Learning and DATA-Mining Techniques

Ai seriousi threati thesei daysi isi maliciousi executables.i It’si designedi toi wrecki computingi systemi andi ai fewi ofi themi coveri networki whilei noti thei informationi ofi thei owneri victimisationi thei system.iTwoi approachesi arei derivedi fori iti i.e.i Signaturei primarilyi basedi Detectioni andi Heuristici primarilyi basedi Detection.i Thesei approachesi performedi welli againsti celebratedi maliciousi programsi howeveri cannoti catchi thei newi maliciousi programs.i Totallyi differenti researchersi havei plannedi waysi victimisationi datai processingi andi machinei learningi fori policei investigationi newi maliciousi programs.i Thei strategyi supportedi datai processingi andi machinei learningi hasi showni sensiblei resultsi comparedi toi alternativei approaches.i Thisi worki presentsi statici malwarei detectioni systemi victimisationi datai processingi techniquesi likei datai Gain,i Principali parti analysis,i andi 3i classifiers:i SVM,i J48,i andi Naïvei mathematician.i Fori overcomingi thei dearthi ofi usuali antivirusi product,i thisi paperi hasi ai tendencyi toi usei waysi ofi statici analysisi toi extracti valuablei optionsi ofi Windowsi letteri filei asi welli asi toi extracti rawi optionsi ofi Windowsi executablesi thati areai uniti letteri headeri data,i DLLs,i andi APIi functionsi withini everyi DLLi ofi Windowsi letteri file.i Thereafter,i datai Gain,i jobi frequenciesi ofi thei rawi optionsi areai uniti calculatedi toi picki outi valuablei seti options,i soi principali parti analysisi isi employedi fori spatiali propertyi reductioni ofi thei choseni options.i Byi adoptingi thei ideasi ofi machinei learningi andi data-mining,i thisi researchi worki constructsi ai statici malwarei detectioni systemi thati featuresi ai detectioni ratei ofi 99.6%.
8 30 2019 2102 2109 F8480088619 10.35940/ijeat.F8480.088619 https://www.ijeat.org/wp-content/uploads/papers/v8i6/F8480088619.pdf https://www.ijeat.org/wp-content/uploads/papers/v8i6/F8480088619.pdf