為了提高信息系統(tǒng)的安全性，本文將基于統(tǒng)計(jì)學(xué)習(xí)理論的支持向量機(jī)方法應(yīng)用到入侵
檢測系統(tǒng)中，保證了在先驗(yàn)知識(shí)不足的情況下,支持向量機(jī)分類器仍有較好的分類正確率，達(dá)到了能夠?qū)ο到y(tǒng)異常情況準(zhǔn)確預(yù)測的目的。該方法避免了基于傳統(tǒng)機(jī)器學(xué)習(xí)的局限性，保證了較強(qiáng)的推廣能力，從而使整個(gè)入侵檢測系統(tǒng)具有較好的檢測性能。
關(guān)鍵詞：入侵檢測；支持向量機(jī)；機(jī)器學(xué)習(xí)
Abstract: In order to improve the safety of information system, the method of SVM based on
statistics learning theory is used in the intrusion detection system, which ensures that SVM
classifier has the higher accuracy in the absence of transcendent knowledge, and achieve the aim that SVM can accurately predict the abnormal state of system. By the use of this method, the limitation of traditional machine learning method is avoided and ensure the stronger extension ability.,which make intrusion detection system to have the better detecting performance.
Key Word: intrusion detecting; support vector machine; machine learning