On monitoring and predicting mobile network traffic abnormality

Abstract

Traffic analysis and traffic abnormality detection are emerged as an efficient way of detecting network attacks in recent years. The existing approaches can be improved by introducing a new model and a new analysis method of network user's traffic behaviors. The description dimensions to network user's traffic behaviors in the current approaches are high, resulting in high processing complexity, high delay in differentiating an individual user's abnormal traffic behavior from massive network data, and low detection rate. To improve the detection rate and efficiency, we develop a new method of establishing user's traffic behavior analysis system based on a new model of network traffic monitoring.First, we establish a more complete feature set based on the characteristics of network traffic to describe massive network user's behaviors. Then, we define a feature selection rule based on the relative deviation distance to select the optimized feature set. We use the selected feature set to locate the abnormality moment and the users who produce the abnormal traffic behavior. Finally, a traffic behavior analysis method based on prediction is developed to improve efficiency of the system. This new method is applied to evaluate the mobile users on mobile cloud. The experimental results show that the proposed method has a higher detection rate and lower delay in the analysis of abnormal user's traffic behavior than that of the existing approaches.

A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

“…In cellular networks, the mobile IP traffic classification can be performed at different levels either using the port, the packet payload [10,11], or the statistical flow distribution [12]. For instance, [13] collected IP traffic extracted from mobile networks in fixed time windows. Statistical based features from normal and abnormal traffic are computed, and a classifier is trained for the analysis of the massive network users' traffic behaviors.…”

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

“…This characteristic makes them suitable for feature Some of them are computed under the assumption that the properties values are normally distributed, which might not be true for some cases. [110], [95], [96], [97], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126], [127], [128], [129], [96], [130], [131], [132], [133], [134], [106], [135], [136], [137], [138], [105], [139], [140], [107], [141], [142], [143] Graph based features Internet interactions are modeled as graphs and valuable features can be extracted from these representations They are ideal for understanding communication patterns…”

C‐NSA: a hybrid approach based on artificial immune algorithms for anomaly detection in web traffic

“…There are some studies in the literature for the detection of abnormal traffic on the network [10–12 ]. Several methodologies and data classification techniques are used to detect abnormal traffic in network data [13, 14 ].…”