Guanling Chen

Detecting 802.11 MAC Layer Spoofing Using Received Signal Strength

MAC addresses can be easily spoofed in 802.11 wireless LANs. An adversary can exploit this vulnerability to launch a large number of attacks. For example, an attacker may masquerade as a legitimate access point to disrupt network services or to advertise false services, tricking nearby wireless stations. On the other hand, the received signal strength (RSS) is a measurement that is hard to forge arbitrarily and it is highly correlated to the transmitters location. Assuming the attacker and the victim are separated by a reasonable distance, RSS can be used to differentiate them to detect MAC spoofing, as recently proposed by several researchers. By analyzing the RSS pattern of typical 802.11 transmitters in a 3-floor building covered by 20 air monitors, we observed that the RSS readings followed a mixture of multiple Gaussian distributions. We discovered that this phenomenon was mainly due to antenna diversity, a widely-adopted technique to improve the stability and robustness of wireless connectivity. This observation renders existing approaches ineffective because they assume a single RSS source. We propose an approach based on Gaussian mixture models, building RSS profiles for spoofing detection. Experiments on the same testbed show that our method is robust against antenna diversity and significantly outperforms existing approaches. At a 3% false positive rate, we detect 73.4%, 89.6% and 97.8% of attacks using the three proposed algorithms, based on local statistics of a single AM, combining local results from AMs, and global multi-AM detection, respectively.

Analysis of a Location-Based Social Network

Location-based Social Networks (LSNs) allow users to see where their friends are, to search location-tagged contentwithin their social graph, and to meet others nearby. The recent availability of open mobile platforms, such as Apple iPhones and Google Android phones, makes LSNs much more accessible to mobile users.To study how users share their location in real world, wecollected traces from a commercial LSN service operated by astartup company. In this paper, we present results of data analysis over user profiles, update activities, mobility characteristics, social graphs, and attribute correlations. To the best of our knowledge, this study is the first large-scale quantitative analysis of a real-world commercial LSN service.

Design and implementation of a large-scale context fusion network

We motivate a context fusion network (CFN), an infrastructure model that allows context-aware applications to select distributed data sources and compose them with customized data-fusion operators into a directed acyclic information fusion graph. Such a graph represents how an application computes high-level understandings of its execution context from low-level sensory data. Multiple graphs by different applications interconnect with each other to form a global graph. A key advantage of a CFN is reusability, both at code-level and instance-level, facilitated by operator composition. We designed and implemented a distributed CFN system, Solar, which maps the logical operator graph representation onto a set of overlay hosts. In particular, Solar meets the challenges inherent to heterogeneous and volatile ubicomp environments. By abstracting most complexities into the infrastructure, Solar facilitates both the development and deployment of context-aware applications. We present the operator composition model, basic services of the Solar overlay network, and programming support for the developers. We also discuss some applications built with Solar and the lessons we learned from our experience.

Data-centric middleware for context-aware pervasive computing

The complexity of developing and deploying context-aware pervasive-computing applications calls for distributed software infrastructures that assist applications to collect, aggregate, and disseminate contextual data. In this paper, we motivate a data-centric design for such an infrastructure to support context-aware applications. Our middleware system, Solar, treats contextual data sources as stream publishers. The core of Solar is a scalable and self-organizing peer-to-peer overlay to support data-driven services. We describe how different services can be systematically integrated on top of the Solar overlay and evaluate the resource discovery and data-dissemination services. We also discuss our experience and lessons learned when using Solar to support several implemented scenarios. We conclude that a data-centric infrastructure is necessary to facilitate both the development and deployment of context-aware pervasive-computing applications.

CrowdRecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint

This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale real-world dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.

Analyzing Privacy Designs of Mobile Social Networking Applications

The combined advances of open mobile platforms and online social networking applications (SNAs) are driving pervasive computing to the real-world users, as the mobile SNAs are expected to revolutionize wireless application industry. While sharing location through mobile SNAs is useful for information access and user interactions, privacy issues must be addressed at the design levels of mobile SNAs. In this paper, we survey mobile SNAs available today and we analyze their privacy designs using feedback and control framework on information capture, construction, accessibility, and purposes. Our analysis results suggest that todays mobile SNAs need better privacy protection on construction and accessibility, to handle increasingly popular mash-ups between different SNA sites. We also identify two unexpected privacy breaches and suggest three potential location misuse scenarios using mobile SNAs.

Simulating non-scanning worms on peer-to-peer networks

Millions of Internet users are using large-scale peer-to-peer (P2P) networks to share content files today. Many other mission-critical applications, such as Internet telephony and Domain Name System (DNS), have also found P2P networks appealing due to their scalability and reliability properties. These P2P networks, however, could be leveraged by automatic-propagating Internet worms to quickly infect a large vulnerable population and inflict tremendous damages to information infrastructure and end systems.While much work has been done to study random-scanning worms, such as CodeRed and Slammer, we have less understanding of non-scanning worms that are potentially stealthy. In this paper, we identify three strategies a non-scanning worm could use to propagate through P2P systems. To understand their behaviors, we provide a workload-driven simulation framework to characterize these worms and identify the parameters influencing their propagations. The non-scanning nature allows P2P worms to evade many of todays detection methods aimed at random-scanning worms. We propose and evaluate an online detection algorithm against these P2P worms using statistical detection of change-points in streaming sensor data.

Predicting mobile application usage using contextual information

As the mobile applications become increasing popular, people are installing more and more Apps on their smart phones. In this paper, we answer the question whether it is feasible to predict which App the user will open. The ability for such prediction can help pre-loading the right Apps to the memory for faster execution or help floating the desired Apps to the home screen for quicker launch. We explored a variety of contextual information, such as last used App, time, location, and the user profile, to predict the users App usage using the MDC dataset. We present three findings from our studies. First, the contextual information can be used to learn the pattern of users App usage and to predict App usage effectively. Second, for the MDC dataset, the correlation between sequentially used Apps has a strong contribution to the prediction accuracy. Lastly, the linear model is more effective than the Bayesian model to combine all contextual information and for such predictions.

CrowdTasker: Maximizing coverage quality in Piggyback Crowdsensing under budget constraint

This paper proposes a novel task allocation framework, CrowdTasker, for mobile crowdsensing. CrowdTasker operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model, and aims to maximize the coverage quality of the sensing task while satisfying the incentive budget constraint. In order to achieve this goal, CrowdTasker first predicts the call and mobility of mobile users based on their historical records. With a flexible incentive model and the prediction results, CrowdTasker then selects a set of users in each sensing cycle for PCS task participation, so that the resulting solution achieves near-maximal coverage quality without exceeding incentive budget. We evaluated CrowdTasker extensively using a large-scale real-world dataset and the results show that CrowdTasker significantly outperformed three baseline approaches by achieving 3%-60% higher coverage quality.

Map: a scalable monitoring system for dependable 802.11 wireless networks

Many enterprises deploy 802.11 wireless networks for mission-critical operations; these networks must be protected for dependable access. This article introduces the MAP project, which includes a scalable 802.11 measurement system that can provide continuous monitoring of wireless traffic to quickly identify threats and attacks. We discuss the MAP system architecture, design decisions, and evaluation results from a real testbed.