本文選題：網(wǎng)絡釣魚 + 特征提取�。� 參考：《電子科技大學》2017年碩士論文

【摘要】：大數(shù)據(jù)時代,網(wǎng)絡安全仍是舉足輕重的話題。在海量信息中,不乏非法分子利用網(wǎng)絡騙取用戶信任并從中獲利,釣魚網(wǎng)站就是其中之一�！搬烎~”網(wǎng)站的網(wǎng)址、網(wǎng)頁內(nèi)容、布局等與真實網(wǎng)站極其相似,沒有安全意識的網(wǎng)民容易因此上當受騙,造成嚴重后果。有效遏制“釣魚網(wǎng)站”是網(wǎng)絡安全的保障。目前,國內(nèi)外在防御釣魚網(wǎng)站的研究上各有建樹,然而都存在缺陷�，F(xiàn)有的比較典型的檢測釣魚網(wǎng)站的方法有:基于黑白名單機制的檢測、基于文本特征或網(wǎng)頁圖像特征的匹配檢測、基于機器學習的分類檢測。然而,基于黑白名單的檢測方法時效性較差、名單范圍也存在著不足,基于特征的算法的準確性和魯棒性又不是很理想。近年來,機器學習應用于各領域并取得巨大成功,尤其是將深度學習應用于檢測識別可以有效得提高檢測效率。鑒于以上,本文研究已有的技術方法,提出基于深度學習的、具有魯棒性的釣魚網(wǎng)站檢測方法。基于深度學習的釣魚網(wǎng)站檢測主要研究以下內(nèi)容:釣魚網(wǎng)站的特征提取是識別釣魚網(wǎng)站的基礎也是關鍵的一步,一個好的特征提取方法對檢測結果起著至關重要的作用。通過對釣魚網(wǎng)站特征的調研,以及對前人研究的總結,本文把網(wǎng)站頁面和網(wǎng)頁網(wǎng)址相結合,分別提取關于網(wǎng)頁內(nèi)容異常和鏈接異常的關鍵特征。為了提高檢測速度和減少誤判率采用了URL過濾器,并對爬取的URL進行相似度檢測進一步提高檢測的準確性,將網(wǎng)址特征和網(wǎng)頁特征進行預處理并保存成特征向量以待下一模塊的檢測識別。近幾年深度學習技術的提出以及其出色的特征學習能力使其在各領域的應用中取得巨大成功。因此,本文研究基于深度學習的釣魚網(wǎng)站分類識別方法,并提出多層結構的DBN-KNN模型,將其運用到釣魚網(wǎng)站特征的識別中,再對上述提取的特征向量進行學習、訓練和分類,最后根據(jù)分類結果判別出釣魚網(wǎng)站。綜上,本學術論文針對現(xiàn)有檢測方法的缺陷,研究基于深度學習的釣魚網(wǎng)站檢測方法。首先,爬取釣魚網(wǎng)站數(shù)據(jù)并進行URL過濾和相似度檢測;然后,人工分析并提取釣魚網(wǎng)站的關鍵特征再對特征進行預處理;最后,提出深度學習模型DBN-KNN對特性向量進行訓練分類,識別出釣魚網(wǎng)站。
[Abstract]:In the era of big data, network security is still an important topic. Among the vast amount of information, there are many illegal elements who use the network to deceive users to trust and profit from it, among which phishing websites are one of them. "fishing" website URL, page content, layout and so on are very similar to the real site, no security awareness of the Internet users are easy to be deceived, resulting in serious consequences. Effective containment of "phishing website" is the guarantee of network security. At present, domestic and foreign research in the defense of fishing sites have their own achievements, but there are shortcomings. There are several typical methods to detect phishing websites: black-and-white list based detection, text feature or page image feature matching detection, machine learning based classification detection. However, the method based on black-and-white list is of poor timeliness, and the scope of the list is also inadequate. The accuracy and robustness of the feature-based algorithm are not ideal. In recent years, machine learning has been applied to various fields with great success, especially the application of depth learning in detection and recognition can effectively improve the detection efficiency. In view of the above, this paper studies the existing technical methods, and proposes a robust fishing site detection method based on depth learning. The research of phishing website detection based on deep learning is as follows: feature extraction of phishing website is the basis and key step to identify phishing website. A good feature extraction method plays an important role in the detection results. By investigating the features of phishing websites and summarizing the previous studies, this paper combines the web pages and web addresses to extract the key features of abnormal page content and link anomalies respectively. In order to improve the detection speed and reduce the error rate, the URL filter is adopted, and the similarity detection of crawling URL is carried out to further improve the accuracy of the detection. The URL features and web page features are preprocessed and stored as feature vectors to be detected and identified by the next module. In recent years, with the development of deep learning technology and its excellent feature learning ability, it has achieved great success in various fields. Therefore, this paper studies the classification and recognition method of phishing websites based on deep learning, and puts forward a multi-layer structure DBN-KNN model, which is applied to the recognition of phishing site features, and then studies, trains and classifies the extracted feature vectors. Finally, the fishing site is identified according to the classification results. In summary, aiming at the defects of existing detection methods, this paper studies the detection method of phishing website based on deep learning. First of all, crawl the fishing site data and carry out URL filtering and similarity detection; then manually analyze and extract the key features of the fishing site and preprocess the features; finally, the depth learning model DBN-KNN is proposed to train and classify the feature vector. Identify fishing sites.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.092

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