Passive Smart Phone Indentification and Tracking with Application Set Fingerprints

Current smart phone users can be identified and tracked by fingerprinttechniques. Fingerprint identification techniques can be used for legitimate purposessuch as network management and traffic control to avoid excessive congestion. Thispaper proposes a user identification and tracking technique specific to smart phoneusers for supervised network management. This paper proposes the application setfingerprint, which is a simple set of User-Agent request-header fields in HTTPsessions. The application set fingerprint has three advantages, fully-passive fingerprintgeneration, potential of user trackability, and fingerprinting considering the users'privacy. The results show that the application set fingerprint is practically effectiveand network operators can use it for tracking smart phone users with the purpose ofefficient network management

Group Mobility Detection and User Connectivity Models for Evaluation of Mobile Network Functions

Group mobility in mobile networks is responsible for dynamic changes in user accesses to base stations, which eventually lead to degradation of network quality of service (QoS). In particular, the rapid movement of a dense group of users intensively accessing the network, such as passengers on a train passing through a densely populated area, significantly affects the perceived network QoS. For better design and operation of mobile network facilities and functions in response to this issue, monitoring group mobility and modeling the access patterns in group mobility scenarios are essential. In this paper, we focus on fast and dense group mobility and mobile network signaling data (control-plane data), which contains information related to mobility and connectivity. Firstly, we develop a lightweight method of group mobility detection to extract train passengers from all users\u27 signaling data without relying on precise location information about users, e.g., based on GPS. Secondly, based on the same signaling data and the results obtained by the detection method, we build connected/idle duration models for train users and non-train users. Finally, we leverage these models in mobile network simulations to assess the effectiveness of a dynamic base station switching/orientation scheme to mitigate QoS degradation with low power consumption in a group mobility scenario. The obtained models reveal that train users consume 3.5 times more resources than non-train users, which proves that group mobility has a significant effect on mobile networks. The simulation results show that the dynamic scheme of base station improves users\u27 perceived throughput, latency and jitter with small amount of additional power consumption in case of a moderate number of train users, but its ineffectiveness with larger number of train users is also shown. This would suggest that group mobility detection and the obtained connection/idle duration models based solely on control-plane data analytics are usable and useful for the development of mobility-aware functions in base stations

Locating Congested Segments over the Internet Based on Multiple End-to-End Path Measurements

Since congestion is very likely to happen in the Internet, locating congested areas (path segments) along a congested path is vital to appropriate actions by Internet Service Providers to mitigate or prevent network performance degradation. We propose a practical method to locate congested segments by actively measuring one-way end-to-end packet losses on appropriate paths from multiple origins to multiple destinations, using a network tomographic approach. Then we conduct a long-term experiment measuring packet losses on multiple paths over the Japanese commercial Internet. The experimental results indicate that the proposed method is able to precisely locate congested segments. Some findings on congestion over the Japan Internet are also given based on the experimen

Stream Mining for Network Management

Network management is an important issue in maintaining the Internet as an important social infrastructure. Finding excessive consumption of network bandwidth caused by P2P mass flows is especially important. Finding Internet viruses is also an important security issue. Although stream mining techniques seem to be promising techniques to find P2P and Internet viruses, vast network flows prevent the simple application of such techniques. A mining technique which works well with extremely limited memory is required. Also it should have a real-time analysis capability. In this paper, we propose a cache based mining method to realize such a technique. By analyzing the characteristics of the proposed method with real Internet backbone flow data, we show the advantages of the proposed method, i.e. less memory consumption while realizing realtime analysis capability. We also show the fact that we can use the proposed method to find mass flow information from Internet backbone flow data

A Practical Evaluation Method of Network Traffic Load for Capacity Planning

Part 6: Short PapersInternational audienceCommunications network operators are supposed to provide high quality network service at low cost. Operators always monitor the amount of traffic and decide equipment investment when the amount exceeds a certain threshold considering trade-offs between link capacity and its utilization. To find the proper threshold efficiently, this paper proposes a practical threshold definition method which consists of fine grained data collection and computer simulation. We evaluate the proposed method using commercial traffic data-set. The results show the proper timing for the equipment investment