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.

The rise of traffic classification in IoT networks: A survey Abstract   With the proliferation of the Internet of Things (IoT), the integration and communication of various objects have become a prevalent practice. The huge growth of IoT devices and different characteristics in the IoT traffic patterns have brought attention to traffic classification methods to address various raised issues in IoT applications. While network traffic classification has been well discussed in a number of surveys and review papers, it is still immature in IoT due to the differences in traffic characteristics in IoT and NonIoT devices. This survey looks at the emerging trends of network traffic classification in IoT and the utilization of traffic classification in its applications. It also compares the legacy of traffic classification methods and presents an overview of traditional models. This paper extends the discussion with a taxonomy of the current network traffic classification within the IoT context. We then expose commercial and realworld use cases of the IoT traffic classification and finally outline open research issues and challenges in this domain.