A Botnet is a network of compromised devices that are controlled by malicious “botmaster” in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters ...A Botnet is a network of compromised devices that are controlled by malicious “botmaster” in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters generate network traffic while communicating with their bots, analyzing network traffic to detect Botnet traffic can be a promising feature of Intrusion Detection System. Although such system has been applying various machine learning techniques, comparison of machine algorithms including their ensembles on botnet detection has not been figured out. In this study, not only the three most popular classification machine learning algorithms—Naive Bayes, Decision tree, and Neural network are evaluated, but also the ensemble methods known to strengthen classifier are tested to see if they indeed provide enhanced predictions on Botnet detection. This evaluation is conducted with the CTU-13 public dataset, measuring the training time of each classifier and its F measure and MCC score.展开更多
Dear Editor,Visual localization relies on local features and searches a prestored GPS-tagged image database to retrieve the reference image with the highest similarity in feature spaces to predict the current location...Dear Editor,Visual localization relies on local features and searches a prestored GPS-tagged image database to retrieve the reference image with the highest similarity in feature spaces to predict the current location[1]-[3].For the conventional methods[4]-[6],local features are generally explored by multiple-stage feature extraction whichfirst detects and then describes key-point features[4],[7].展开更多
文摘A Botnet is a network of compromised devices that are controlled by malicious “botmaster” in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters generate network traffic while communicating with their bots, analyzing network traffic to detect Botnet traffic can be a promising feature of Intrusion Detection System. Although such system has been applying various machine learning techniques, comparison of machine algorithms including their ensembles on botnet detection has not been figured out. In this study, not only the three most popular classification machine learning algorithms—Naive Bayes, Decision tree, and Neural network are evaluated, but also the ensemble methods known to strengthen classifier are tested to see if they indeed provide enhanced predictions on Botnet detection. This evaluation is conducted with the CTU-13 public dataset, measuring the training time of each classifier and its F measure and MCC score.
文摘Dear Editor,Visual localization relies on local features and searches a prestored GPS-tagged image database to retrieve the reference image with the highest similarity in feature spaces to predict the current location[1]-[3].For the conventional methods[4]-[6],local features are generally explored by multiple-stage feature extraction whichfirst detects and then describes key-point features[4],[7].
