An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTELTEA networks Abstract   With the development of wireless communications, Mobile Networks have become an important part of our daily life and fueled the growth of many attractive technologies such as 5G, Internet of Things (IoT) and even Smart City. As a main bearer of current Mobile Networks, LTELTEA carries massive and important business data but is facing more and more serious attack situations, which makes the Security Measurement over it become necessary and important. However, current methods are usually designed from specific malicious detections, which cannot provide the user with a synthetic view of security evaluation. Meanwhile, as the massive amount and poor quality of networking data are considered, the efficiency and accuracy of the current security measurement methods are usually not good. In this paper, we focus on the evaluation basis (the collecting data) of security measurement over LTELTEA networks, and propose an Adaptive Security Data Collection and Composition Recognition (ASDCCR) method for it. We design heuristic algorithms and processing framework in ASDCCR to make the data collection adaptive and synthetic attack recognition become possible. We also verified the proposed method in simulated LTE environment of NS3 to verify the usability and accuracy of the proposed methods.

A streaming approach to reveal crowded events from cellular data Abstract   Anomaly detection has been a very popular research topics over the last few years and applies to many scenarios from different disciplines. This research focuses on crowded phenomena, and addresses the detection of popular events by looking for xe2x80x9canomalousxe2x80x9d patterns in cellular traffic data. In particular, the paper elaborates upon previous proposals and presents two streaming algorithms based on the wavelet decomposition of traffic data. The new algorithms consume traffic samples as soon as they are available, elaborate the data in real time and possibly raise alarms upon threshold crossing. The effectiveness of the approach is assessed by using the public dataset containing the real cellular data acquired over the network of the most popular Italian traffic operator. The experiments prove that the streaming algorithms generally achieve performance comparable to that of their offline counterparts, and that the small degradation that may occasionally be observed is however well counterbalanced by the obvious advantage of detecting anomalies in realxe2x80x93time with no need to wait for the elaboration of overly long traffic timeseries.