<div> <div> <div> <p>Support Vector Machines (SVMs) have generally been effective in detecting instances of network intrusion. However, from a practical point of view, a standard SVM is not able to handle large-scale data efficiently due to the computation complexity of the algorithm and extensive memory requirements. To cope with the limitation, this study presents an incremental SVM method combined with a k-nearest neighbors (KNN) based candidate support vectors (CSV) selection strategy in order to speed up training and test process. The proposed incremental SVM method constructs or updates the pattern classes by incrementally incorporating new signatures without having to load and access the entire previous dataset in order to cope with evolving DoS and DDoS attacks. Performance of the proposed method is evaluated with experiments and compared with the standard SVM method and the simple incremental SVM method in terms of precision, recall, F1-score, and training and test duration.<br></p> </div> </div> </div>

台灣股票市場屬於淺碟型，因此外界的訊息易於影響股價波動；同時台灣是一個以個別投資人為主的散戶市場，外界的訊息會影響市場投資。因此，重大訊息的發布對公司股價變化的影響，值得我們進一步探討。 本研究以公開資訊觀測站之重大訊息為資料來源，蒐集2005~2009年間統一、中華電信、長榮航空以及臺灣企銀四間上市公司之重大訊息共1382篇。利用文字探勘kNN演算法將四間公司之重大訊息加以分群，分析出各訊息的發布對於股價之影響程度，並找出不同群組之重大訊息的漲跌趨勢，期能對未來即時重大訊息的發布，分析出其對於股價之漲跌影響，進一步得到訊息發布日後兩日之報酬率走勢，成為日後投資標的之選擇參考。 本研究結果顯示取樣公司於發布前兩日至發布後兩日，交易量有顯著之異常，顯示訊息發布對於公司股票確有影響；而不同的重大訊息內容，將會被分於不同之群組當中，各群組也各有其不同之漲跌趨勢，本研究於測試資料之分類結果，整體平均有六成五之準確率，在於上漲類別之準確率更高達八成；最後於發布後累積報酬率之影響，投資正確率平均高於六成。 本研究透過系統化之分析與預測，省去投資者對於重大訊息之搜尋以及解讀的時間，提供投資者一個可供參考之依據。 / In this study we used the technique of text mining to classify the material information of companies and analyze how the disclosure of it affects the market. Hence, we would be able to predict the price of stock based on disclosures of the material information and then use the outcome as reference of investment. This study chose the Market Observation Post System as the source of information to its justice. We chose UNI-PRESIDENT ENTERPRISES CORP, Chunghwa Telecom Co., Ltd, EVA AIRWAYS CORPORATION and Taiwan Business Bank for their great evaluation of the information disclosure. We collected 1382 material information from 2005 to 2009 and for the better performance, we selected kNN algorithm as our rule of classification. We conducted three experiments in this study. In these experiments, we have approved that the trading volume of two periods were with significant differences. We have over 60% accuracy of the all data to classify the tested data. As a result, we found that the return rate of the “up” group has over 60% upside probability and the “down” group has over 60% downside probability. In this study, we built a time-saving automatic system to group material information and find out those that are valuable. Based on our result, we provided a reference to investors for their investment strategy. At the same time, we also came up with some inspiration for future research.