Neural Network Rule Extraction for Real Time Traffic Behavior Identification The rapid identification of network traffic classification has become an important network management tasks. Although the current research methods can classify the network traffic and achieve high accuracy. Because of the high complexity of computing, memory consumption and other reasons, they can only provide 100 megabytes of processing power and cannot handle a large number of concurrent traffic situation. Aiming at this problem, this paper proposes a method to identify the traffic classification on the network, and selects the early identified traffic characteristics as the attributes of the training model to alleviate the resource and time consumption of the online traffic classification. The method first sends the traffic data to the trained neural network and extracts the fuzzy traffic data. And then the fuzzy traffic data optimizes by genetic algorithm and sends to the decision tree algorithm to generate decision tree. Finally, the decision tree is transformed into rules, such as ifthen rules. The resulting rules use FPGA as the application context to achieve online network traffic classification. This method is not only an innovation in the field of neural network extraction, but also a novel method of neural network rule extraction to solve the online network traffic classification.

An Effective Approach to Classify Abnormal Network Traffic Activities using Wavelet Transform Understanding network activities has become the most significant task in network security due to the rapid growth of the Internet and mobile devices usages. To protect our computing infrastructures and personal data from network intruders or attacks, identifying abnormal activities is critical. Extracting features from network traffic data is considered as an essential task to be performed because it affects the overall performances to identify the activities accurately. Although researchers proposed several approaches, they mainly focused on identifying the best possible technique to detect abnormal network activities. Only a few studies considered utilizing feature extraction techniques. In this paper, we introduced a new approach, with which an integrative information feature set is determined to identify abnormal network activities using wavelet transformation. Instead of extracting features by attributes, the approach uses all attributes information to extract features and to design a reliable learning model to detect abnormal activities by reducing false positives. Two machine learning techniques, Logistic Regression (LR) and Naive Bayes, are utilized to show the effectiveness of the approach. A visualization method is also used to emphasize our approach. As a result, we found that our proposed approach produces a better performance result with less computational time in detecting abnormal network activities.