Fingerprinting IEEE 802.15.4 Devices with Commodity Radios

We present a reliable method of PHY-layer fingerprinting of IEEE 802.15.4-conformant nodes with commodity digital radio chips widely used in building inexpensive IEEE 802.15.4-conformant devices. Typically, PHY-layer fingerprinting requires software-defined radios that cost orders of magnitude more than the chips they can fingerprint; our method does not require a software-defined radio and uses the same inexpensive chips. For mission-critical systems relying on 802.15.4 devices, defense-in-depth is thus necessary. Device fingerprinting has long been an important defensive tool; reducing its cost raises its utility for defenders. We investigate new methods of fingerprinting 802.15.4 devices by exploring techniques to differentiate between multiple 802.15.4-conformant radio-hardware manufactures and firmware distributions, and point out the implications of these results for WIDS, both with respect to WIDS evasion techniques and countering such evasion

XSS-FP: Browser Fingerprinting using HTML Parser Quirks

There are many scenarios in which inferring the type of a client browser is desirable, for instance to fight against session stealing. This is known as browser fingerprinting. This paper presents and evaluates a novel fingerprinting technique to determine the exact nature (browser type and version, eg Firefox 15) of a web-browser, exploiting HTML parser quirks exercised through XSS. Our experiments show that the exact version of a web browser can be determined with 71% of accuracy, and that only 6 tests are sufficient to quickly determine the exact family a web browser belongs to

IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead

Fingerprinting Software Defined Networks and Controllers

SDN transforms a network from a calcified collection of hardware into a logically centralized and programmable method of interconnectivity. Changing the networking paradigm shifts a networks security posture. Changes visible to a host connected to the network include small latency differences between a traditional network environment and an SDN environment. This thesis aims to reliably distinguish SDN environments from traditional environments by observing latency behavior. Additionally, this thesis determines whether latency information contributes to the unique fingerprint of SDN controllers. Identifying the controller software gives an adversary information contributing to a network attack. An SDN and traditional network environment consisting of two hosts, one switch, and one controller are created. Within both environments, packet RTT values are compared between SDN and traditional environments to determine if both sets differ. Latency analysis is used to observe features of an SDN controller. Collected features contribute to a table of information used to uniquely fingerprint an SDN controller. Results show that packet RTTs within a traditional network environment significantly (p-value less than 1:0 10(-15)) differ from SDN environments. The predicted controller inactivity timeout within the simulated environment differs from the true timeout by a mean value of 0.44956 seconds. The emulated environment shows that the observed inactivity timeout depends on the network switch implementation of the controllers set value, leading to incorrect observed timeouts. Within the SDN environment, the host is not able to directly communicate with the SDN controller, leading to an inability to collect the number of features needed to uniquely identify the SDN controller

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy