This paper introduces the concept of participatory sensing, which tasks everyday mobile devices, such as cellular phones, to form interactive, participatory sensor networks that enable public and professional users to gather, analyze and share local knowledge. An initial architecture to enhance data credibility, quality, privacy and ‘shareability ’ in such networks is described, as well as a campaign application model that encompasses participation at personal, social and urban scales. Example applications are outlined in four areas: urban planning, public health, cultural identity and creative expression, and natural resource management. Keywords Participatory sensing, urban sensing, network-attested context, community-based participatory research, mobile handsets, urban planning, natural resource management, public health, cultural identity. 1.

Automotive traffic monitoring using probe vehicles with Global Positioning System receivers promises significant improvements in cost, coverage, and accuracy. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we propose a system based on virtual trip lines and an associated cloaking technique. Virtual trip lines are geographic markers that indicate where vehicles should provide location updates. These markers can be placed to avoid particularly privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus they facilitate the design of a distributed architecture, where no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GPS

Location-hidden services, as offered by anonymity systems such as Tor, allow servers to be operated under a pseudonym. As Tor is an overlay network, servers hosting hidden services are accessible both directly and over the anonymous channel. Traffic patterns through one channel have observable effects on the other, thus allowing a service’s pseudonymous identity and IP address to be linked. One proposed solution to this vulnerability is for Tor nodes to provide fixed quality of service to each connection, regardless of other traffic, thus reducing capacity but resisting such interference attacks. However, even if each connection does not influence the others, total throughput would still affect the load on the CPU, and thus its heat output. Unfortunately for anonymity, the result of temperature on clock skew can be remotely detected through observing timestamps. This attack works because existing abstract models of anonymitynetwork nodes do not take into account the inevitable imperfections of the hardware they run on. Furthermore, we suggest the same technique could be exploited as a classical covert channel and can even provide geolocation.

This paper introduces a novel algorithm, UDmap, to identify dynamically assigned IP addresses and analyze their dynamics pattern. UDmap is fully automatic, and relies only on applicationlevel server logs. We applied UDmap to a month-long Hotmail user-login trace and identified a significant number of dynamic IP addresses – more than 102 million. This suggests that the fraction of IP addresses that are dynamic is by no means negligible. Using this information in combination with a three-month Hotmail email server log, we were able to establish that 95.6 % of mail servers setup on the dynamic IP addresses in our trace sent out solely spam emails. Moreover, these mail servers sent out a large amount of spam – amounting to 42.2 % of all spam emails received by Hotmail. These results highlight the importance of being able to accurately identify dynamic IP addresses for spam filtering. We expect similar benefits to arise for phishing site identification and botnet detection. To our knowledge, this is the first successful attempt to automatically identify and understand IP address dynamics.

Motivated by the proliferation of wireless-enabled devices and the suspect nature of device driver code, we develop a passive fingerprinting technique that identifies the wireless device driver running on an IEEE 802.11 compliant device. This technique is valuable to an attacker wishing to conduct reconnaissance against a potential target so that he may launch a driver-specific exploit. In particular, we develop a unique fingerprinting technique that accurately and efficiently identifies the wireless driver without modification to or cooperation from a wireless device. We perform an evaluation of this fingerprinting technique that shows it both quickly and accurately fingerprints wireless device drivers in real world wireless network conditions. Finally, we discuss ways to prevent fingerprinting that will aid in improving the security of wireless communication for devices that employ 802.11 networking. 1

We present novel and practical techniques to accurately detect IP prefix hijacking attacks in real time to facilitate mitigation. Attacks may hijack victim’s address space to disrupt network services or perpetrate malicious activities such as spamming and DoS attacks without disclosing identity. We propose novel ways to significantly improve the detection accuracy by combining analysis of passively collected BGP routing updates with data plane fingerprints of suspicious prefixes. The key insight is to use data plane information in the form of edge network fingerprinting to disambiguate suspect IP hijacking incidences based on routing anomaly detection. Conflicts in data plane fingerprints provide much more definitive evidence of successful IP prefix hijacking. Utilizing multiple real-time BGP feeds, we demonstrate the ability of our system to distinguish between legitimate routing changes and actual attacks. Strong correlation with addresses that originate spam emails from a spam honeypot confirms the accuracy of our techniques.

We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC identification accuracy than prior best known schemes. We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel. Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme. This research was performed under an appointment to the

The ubiquity of 802.11 devices and networks enables anyone to track our every move with alarming ease. Each 802.11 device transmits a globally unique and persistent MAC address and thus is trivially identifiable. In response, recent research has proposed replacing such identifiers with pseudonyms (i.e., temporary, unlinkable names). In this paper, we demonstrate that pseudonyms are insufficient to prevent tracking of 802.11 devices because implicit identifiers, or identifying characteristics of 802.11 traffic, can identify many users with high accuracy. For example, even without unique names and addresses, we estimate that an adversary can identify 64 % of users with 90 % accuracy when they spend a day at a busy hot spot. We present an automated procedure based on four previously unrecognized implicit identifiers that can identify users in three real 802.11 traces even when pseudonyms and encryption are employed. We find that the majority of users can be identified using our techniques, but our ability to identify users is not uniform; some users are not easily identifiable. Nonetheless, we show that even a single implicit identifier is sufficient to distinguish many users. Therefore, we argue that design considerations beyond eliminating explicit identifiers (i.e., unique names and addresses), must be addressed in order to prevent user tracking in wireless networks. Categories and Subject Descriptors:

We demonstrate the feasibility of fingerprinting the radio of wireless sensor nodes (Chipcon 1000 radio, 433MHz). We show that, with this type of devices, a receiver can create device radio fingerprints and subsequently identify origins of messages exchanged between the devices, even if message contents and device identifiers are hidden. We further analyze the implications of device fingerprinting on the security of sensor networking protocols, specifically, we propose two new mechanisms for the detection of wormholes in sensor networks.

Abstract — We reverse engineer copyright enforcement in the popular BitTorrent file sharing network and find that a common approach for identifying infringing users is not conclusive. We describe simple techniques for implicating arbitrary network endpoints in illegal content sharing and demonstrate the effectiveness of these techniques experimentally, attracting real DMCA complaints for nonsense devices, e.g., IP printers and a wireless access point. We then step back and evaluate the challenges and possible future directions for pervasive monitoring in P2P file sharing networks. 1