Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

Persistent user behavior monitoring, which deals with finding users that occur persistently over a measurement period, is one hot topic in traffic measurement. It is significant for many applications, such as anomaly detection. Former works concentrate on monitoring frequent user behavior, such as users occurring frequently either over one measurement period or on one monitor. They have paid little attention to detect persistent user behavior over a long measurement period on multiple monitors. However, persistent users do not necessarily appear frequently in a short measurement period, but appear persistently in a long measurement period. Due to limited resource on monitors, it is not practical to collect a tremendous amount of network traffic in a long measurement period on one single monitor. Moreover, since network attackers deliberately send packets flowing through the entire managed network, it is difficult to detect abnormal behavior on one single monitor. To solve the above challenges, a novel method for detecting persistent user behavior called DPU is proposed, and it contains both online distributed traffic collection in a long measurement period on multiple monitors and offline centralized user behavior detection on the central server. The key idea of DPU is that we design the compact distributed synopsis data structure to collect the relevant information with users occurring in a long measurement period on each monitor, and we can reconstruct user IDs using simple calculations and bit settings to find users with persistent behavior on the basis of estimated occurrence frequency of users on the central server when user IDs are unknown in advance. The experiments are conducted on real traffic to evaluate the performance of detecting persistent user behavior, and the experimental results illustrate that our method can improve about 30% estimation accuracy, 40% detection precision, and accelerate about 3 times in comparison with the related method.

Towards Smart City Governance. Case Study: Improving the Interpretation of Quantitative Traffic Measurement Data through Citizen Participation

Citizens play a core role in sustainable cities as users of the services delivered by cities and as active participants in initiatives aimed at making cities more sustainable. This paper considers the role of citizens as information providers and discusses the conditions under which citizens can participate in the development of sustainable cities. The objective of this study is to document the sustainability of an urban transit system and evaluate its compliance, with citizen participation as a major contributor. The methodology used is intensive field visits, interviews, and a mixed analysis of Sant Andreu de Palomar District in Barcelona city. The circulating vehicles are quantitatively monitored, qualitative problems are detected, and the typology of vehicles and other aspects identified and detailed in the study are indicated. All this information is contrasted with that of the technological sensors in the sectors. The results indicate that vehicles in the current pattern of urban density planned under incorrect sensor operation influence sustainable behavior through agglomerative clustering. This paper provides recommendations for future urban sustainability assessment research, including the employment of mixed-methods research, among other strategies. This article is intended to assist policymakers and traffic engineers in evaluating the sustainability of urban transportation infrastructure projects considering citizens as sensors.

Hierarchical Virtual Bitmaps for Spread Estimation in Traffic Measurement

This paper introduces a hierarchical traffic model for spread measurement of network traffic flows. The hierarchical model, which aggregates lower level flows into higher-level flows in a hierarchical structure, will allow us to measure network traffic at different granularities at once to support diverse traffic analysis from a grand view to fine-grained details. The spread of a flow is the number of distinct elements (under measurement) in the flow, where the flow label (that identifies packets belonging to the flow) and the elements (which are defined based on application need) can be found in packet headers or payload. Traditional flow spread estimators are designed without hierarchical traffic modeling in mind, and incur high overhead when they are applied to each level of the traffic hierarchy. In this paper, we propose a new Hierarchical Virtual bitmap Estimator (HVE) that performs simultaneous multi-level traffic measurement, at the same cost of a traditional estimator, without degrading measurement accuracy. We implement the proposed solution and perform experiments based on real traffic traces. The experimental results demonstrate that HVE improves measurement throughput by 43% to 155%, thanks to the reduction of perpacket processing overhead. For small to medium flows, its measurement accuracy is largely similar to traditional estimators that work at one level at a time. For large aggregate and base flows, its accuracy is better, with up to 97% smaller error in our experiments.

Network and Application Performance Measurement Challenges on Android Devices

Modern mobile systems are optimized for energy-efficient computation and communications, and these optimizations affect the way they use the network, and thus the performance of the applications. Therefore, understanding network and application performance are essential for debugging, improving user experience, and performance comparison. In recent years, several tools have emerged that analyze network performance of mobile applications in situ with the help of the VPN service. However, there is a limited understanding of how these measurement tools and system optimizations affect the network and application performance. This paper first demonstrates that mobile systems employ energy-aware system hardware tuning, affecting network latency and throughput. We next show that the VPN-based tools, such as Lumen, PrivacyGuard, and Video Optimizer, aid in ambiguous network performance measurements and degrade the application performance. Our findings suggest that sound Internet traffic measurement on Android devices requires a good understanding of the device, networks, measurement tools, and applications.

Estimation of Traffic Matrix from Links Load using Genetic Algorithm

Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.