【摘要】：網(wǎng)絡(luò)技術(shù)飛速發(fā)展,導(dǎo)致各種網(wǎng)絡(luò)安全問題越來越嚴(yán)重,所需的安全防護(hù)措施也越來越重要。入侵檢測(cè)技術(shù)是一種基于預(yù)防的動(dòng)態(tài)安全防范措施,它一直是信息安全領(lǐng)域研究的熱點(diǎn),有著舉足輕重的地位。 本文針對(duì)傳統(tǒng)KNN算法學(xué)習(xí)效率低下的缺點(diǎn),提出一種快速KNN (F-KNN)算法。其主要作了以下三個(gè)方面的改進(jìn)： 第一,刪減訓(xùn)練樣本庫。刪除訓(xùn)練集中的大量重復(fù)數(shù)據(jù),以減少算法學(xué)習(xí)過程中的計(jì)算量,從而提高學(xué)習(xí)效率。 第二,建立索引模型。隨機(jī)選取一個(gè)訓(xùn)練樣本作為基準(zhǔn)點(diǎn),計(jì)算其他訓(xùn)練樣本與該基準(zhǔn)點(diǎn)的距離,且由小到大進(jìn)行排序,得到一個(gè)有序線性表,并抽取有序線性表中間隔相等的樣本建立索引表,根據(jù)索引表和有序線性表快速查找待分類測(cè)試樣本的k個(gè)最近鄰,以縮小查找范圍,從而提高學(xué)習(xí)效率。 第三,設(shè)緩存功能。對(duì)待分類測(cè)試樣本時(shí),先與緩存的已分類測(cè)試樣本比對(duì),若有相同,則直接賦予緩存樣本的類標(biāo),若無相同,再行分類學(xué)習(xí),從而提高學(xué)習(xí)效率。 本文選用KDD CUP99數(shù)據(jù)集作為實(shí)驗(yàn)數(shù)據(jù),首先對(duì)該數(shù)據(jù)集進(jìn)行預(yù)處理；然后使用基于互信息的特征約簡(jiǎn)算法進(jìn)行特征選擇；最后使用F-KNN算法對(duì)特征約簡(jiǎn)后的數(shù)據(jù)集進(jìn)行異常檢測(cè)。實(shí)驗(yàn)結(jié)果表明,F-KNN算法在不降低分類精度的前提下,大幅度提高了分類學(xué)習(xí)效率。
[Abstract]:With the rapid development of network technology, all kinds of network security problems become more and more serious, and the security protection measures are more and more important. Intrusion detection technology is a kind of dynamic security measures based on prevention. It has always been a hot spot in the field of information security and plays an important role in the field of information security. In this paper, a fast KNN (F-KNN) algorithm is proposed to overcome the disadvantage of low learning efficiency of traditional KNN algorithm. It mainly makes the following three aspects of improvement: first, delete the training sample database. In order to reduce the computational complexity in the learning process of the algorithm, a large number of duplicated data in the training set are deleted so as to improve the learning efficiency. Secondly, the index model is established. A training sample is randomly selected as the reference point, the distance between the other training samples and the reference point is calculated, and the order linear table is obtained from small to large, and the sample with equal interval in the ordered linear table is taken to set up the index table. According to the index table and the ordered linear table, k nearest neighbors of the test samples to be classified are quickly searched to reduce the search range and thus improve the learning efficiency. Third, set up cache function. When the classification test sample is treated, it is first compared with the cached classified test sample. If there is the same, the class label of the cache sample is directly assigned to the cache sample. If there is not the same, then the classification learning is performed, so as to improve the learning efficiency. In this paper, the KDD CUP99 data set is chosen as the experimental data, firstly, the data set is pre-processed, then the feature reduction algorithm based on mutual information is used for feature selection. Finally, the F-KNN algorithm is used to detect the anomaly of the reduced data set. The experimental results show that the F-KNN algorithm greatly improves the classification learning efficiency without reducing the classification accuracy.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08

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