The dissertation mainly aims at applying several active machine learning strategies to intrusion detection and systematically studies signal analysis techniques of intrusion detection based on statistical learning theory ( slt ) , symbol inductive learning theory and genetic learning method . meanwhile , performance comparison and evaluation among intrusion detection techniques based on different machine learning strategies are presented according to computational learning theory and statistical hypothesis test methodology . intrusion detection is regarded as a pattern recognition problem in term of statistical learning theory ; i 本文的主要工作是將目前幾種有生命力的機(jī)器學(xué)習(xí)策略應(yīng)用于入侵檢測(cè)技術(shù)中,論文從入侵檢測(cè)的不同視角出發(fā),系統(tǒng)深入地研究了統(tǒng)計(jì)學(xué)習(xí)理論、基于符號(hào)的歸納學(xué)習(xí)理論和遺傳學(xué)習(xí)方法在入侵檢測(cè)信號(hào)分析中的應(yīng)用技術(shù),并在可能近似正確( pac )學(xué)習(xí)框架下,利用計(jì)算學(xué)習(xí)理論和統(tǒng)計(jì)假設(shè)檢驗(yàn)方法對(duì)基于不同機(jī)器學(xué)習(xí)策略的入侵檢測(cè)方法進(jìn)行了性能比較和評(píng)估。