A Al Harthi

Bio: A Al Harthi is an academic researcher. The author has contributed to research in topics: Traffic classification & Traffic generation model. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Designing an accurate and efficient classification approach for network traffic monitoring

Abstract: In recent years, knowing what information is passing through the networks is rapidly becoming more and more complex due to the ever-growing list of applications shaping today's Internet traffic. Consequently, traffic monitoring and analysis have become cr

Edge Computing Intelligence Using Robust Feature Selection for Network Traffic Classification in Internet-of-Things

Abstract: Internet-of-Things (IoT) devices are massively interconnected, which generates a massive amount of network traffic. The concept of edge computing brings a new paradigm to monitor and manage network traffic at the network’s edge. Network traffic classification is a critical task to monitor and identify Internet traffic. Recent traffic classification works suggested using statistical flow features to classify network traffic accurately using machine learning techniques. The selected classification features must be stable and can work across different spatial and temporal heterogeneity. This paper proposes a feature selection mechanism called Ensemble Weight Approach (EWA) for selecting significant features for Internet traffic classification based on multi-criterion ranking and selection mechanisms. Extensive simulations have been conducted using publicly-available traces from the University of Cambridge. The simulation results demonstrate that EWA is capable of identifying stable features subset for Internet traffic identification. EWA-selected features improve the mean accuracy up to 1.3% and reduce RMSE using fewer features than other feature selection methods. The smaller number of features directly contributes to shorter classification time. Furthermore, the selected features can train stable traffic classification generative models irrespective of the dataset’s spatial and temporal differences, with consistent accuracy up to 97%. The overall performance indicates that EWA-selected statistical flow features can improve the overall traffic classification.

Comparative Analysis of Machine Learning techniques for Network Traffic Classification

Abstract: Network traffic classification is the operation of giving appropriate identification to the every traffic flowing through a network. Several methods have been applied in the past to achieve network traffic classification including port-based, payload-based, behavior based and so on. These methods have been found to one limitation or the other. Nowadays, attention is now on Machine Learning(ML) methods that rely on the statistical properties of the traffic flows generated. However, ML methods do not perform well when confronted with large-scale traffic data having large number of features and instances. Feature selection is employed to remove non-relevant and redundant features before passing the data to ML classifiers. In this study, network traffic classification using ML methods is demonstrated from two perspectives: one that involves feature selection and one that does not. A number of performance metrics are considered including runtime, accuracy, recall, precision and F- score. The experimental results indicate that the classification without features has an average accuracy and runtime of 94.14% and 0.52 seconds respectively. On the other hand, the method with feature selection has accuracy of 95.61% and average of 0.25 seconds for the runtime. The improvement obtained reflects the importance of applying only relevant and non-redundant features to the ML methods. Thus it recommended that feature selection be included in the network classification process to guarantee an optimal accuracy result.