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Leveraging USB to Establish Host Identity Using Commodity Devices
, 2013
"... Abstract—Determining a computer’s identity is a challenge of critical importance to users wishing to ensure that they are interacting with the correct system; it is also extremely valuable to forensics investigators. However, even hosts that contain trusted computing hardware to establish identity c ..."
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Abstract—Determining a computer’s identity is a challenge of critical importance to users wishing to ensure that they are interacting with the correct system; it is also extremely valuable to forensics investigators. However, even hosts that contain trusted computing hardware to establish identity can be defeated by relay and impersonation attacks. In this paper, we consider how to leverage the virtually ubiquitous USB interface to uniquely identify computers based on the characteristics of their hardware, firmware, and software stacks. We collect USB data on a corpus of over 250 machines with a variety of hardware and software configurations, and through machine learning classification tech-niques we demonstrate that, given a period of observation on the order of tenths of a second, we can differentiate hosts based on a variety of attributes such as operating system, manufacturer, and model with upwards of 90 % accuracy. Over longer periods of observation on the order of minutes, we demonstrate the ability to distinguish between hosts that are seemingly identical; using Random Forest classification and statistical analysis, we generate fingerprints that can be used to uniquely and consistently identify 70 % of a field of 30 machines that share identical OS and hardware specifications. Additionally, we show that we can detect the presence of a hypervisor on a computer with 100% accuracy and that our results are resistant to concept drift, a spoofing attack in which malicious hosts provide fraudulent USB messages, and relaying of commands from other machines. Our techniques are thus generally employable in an easy-to-use and low-cost fashion. I.
A Nonparametric Bayesian Approach for Opportunistic Data Transfer in Cellular Networks
- in Proceedings of the 7th International Conference of Wireless Algorithms, Systems, and Applications (WASA), ser. Lecture Notes in Computer Science
"... Abstract. The number of mobile Internet users is growing rapidly, as well as the capability of mobile Internet devices. As a result, the enormous amount of traffic generated everyday on mobile Internet is pushing cellular services to their limits. We see great potential in the idea of scheduling the ..."
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Abstract. The number of mobile Internet users is growing rapidly, as well as the capability of mobile Internet devices. As a result, the enormous amount of traffic generated everyday on mobile Internet is pushing cellular services to their limits. We see great potential in the idea of scheduling the transmission of delay tolerant data towards times when the network condition is better. However, such scheduling requires good network condition prediction, which has not been effectively tackled in previous research. In this paper, we propose a Dynamic Hidden Markov Model (DHMM) to model the time dependent and location dependent network conditions observed by individual users. The model is dynamic since transition matrix and states are updated when new observations are available. On the other hand, it has all the properties of a Hidden Markov Model. DHMM can predict precisely the next state given the current state, hence can provide a good prediction of network condition. DHMM has two layers, the top layer is Received Signal Strength (RSS) and the bottom layer consists of states, defined as a mixture of location, time and the signal strength itself. Since the state is defined as a mixture, it is hidden and the number of states is also not known a priori. Thus, the Nonparametric Bayesian Classification is applied to determine the hidden states. We show through simulations that when combined with a Markov decision process, the opportunistic scheduling can reduce transmission costs up to 50.34 % compared with a naive approach. 1
Fingerprinting Smart Devices Through Embedded Acoustic Components
"... The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart de-vices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote iden-tification without user awareness. We propose a novel fingerprinting approach ..."
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The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart de-vices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote iden-tification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an indi-vidual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We ex-ploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic fea-tures and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even pos-sible 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 suc-cess rate and examines the effect of background noise and other variables on fingerprinting accuracy. 1
Do You Hear What I Hear? Fingerprinting Smart Devices Through Embedded Acoustic Components
"... The widespread use of smart devices gives rise to privacy concerns. Fingerprinting smart devices can jeopardize privacy by allowing re-mote identification without user awareness. We study the feasibil-ity of using microphones and speakers embedded in smartphones to uniquely fingerprint individual de ..."
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The widespread use of smart devices gives rise to privacy concerns. Fingerprinting smart devices can jeopardize privacy by allowing re-mote identification without user awareness. We study the feasibil-ity of using microphones and speakers embedded in smartphones to uniquely fingerprint individual devices. During fabrication, sub-tle imperfections arise in device microphones and speakers, which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smartphones through playback and recording of audio samples. We explore different acoustic features and analyze their ability to successfully fingerprint smartphones. Our experiments show that not only is it possible to fingerprint de-vices manufactured by different vendors but also devices that have the same maker and model; on average we were able to accurately attribute 98 % of all recorded audio clips from 50 different Android smartphones. Our study also identifies the prominent acoustic fea-tures capable of fingerprinting smart devices with a high success rate, and examines the effect of background noise and other vari-ables on fingerprinting accuracy.
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, 2014
"... This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has ..."
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This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has
Proximity-based Security Techniques for Mobile Users in Wireless Networks
"... Abstract—In this paper, we propose a privacy-preserving proximity-based security system for location-based services (LBS) in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. In this system, the proximity-based authentication and session key ..."
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Abstract—In this paper, we propose a privacy-preserving proximity-based security system for location-based services (LBS) in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. In this system, the proximity-based authentication and session key establishment are implemented based on spatial temporal location tags. Incor-porating the unique physical features of the signals sent from multiple ambient radio sources, the location tags cannot be easily forged by attackers. More specifically, each radio client builds a public location tag according to the received signal strength indicators (RSSI), sequence numbers and MAC addresses of the ambient packets. Each client also keeps a secret location tag that consists of the packet arrival time information to generate the session keys. As clients never disclose their secret location tags, this system is robust against eavesdroppers and spoofers outside the proximity range. The system improves the authentication accuracy by introducing a nonparametric Bayesian method called infinite Gaussian mixture model in the proximity test and provides flexible proximity range control by taking into account multiple physical-layer features of various ambient radio sources. Moreover, the session key establishment strategy significantly increases the key generation rate by exploiting the packet arrival time of the ambient signals. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments. I.
IEEE ICC 2013- Ad-hoc and Sensor Networking Symposium Proximity-based Security Using Ambient Radio Signals
"... Abstract-In this paper, we propose a privacy-preserving proximity-based security strategy for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. More specifically, radio clients build their location tags according to ..."
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Abstract-In this paper, we propose a privacy-preserving proximity-based security strategy for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. More specifically, radio clients build their location tags according to the unique physical features of their ambient radio signals, which cannot be forged by attackers outside the proximity range. The proximity-based authentication and session key generation is based on the public location tag, which incorporates the received signal strength indicator (RSSI), sequence number and MAC address of the ambient radio packets. Meanwhile, as the basis for the session key generation, the secret location tag consisting of the arrival time interval of the ambient packets, is never broadcast, making it robust against eavesdroppers and spoofers. The proximity test utilizes the nonparametric Bayesian method called infinite Gaussian mixture model, and provides range control by selecting different features of various ambient radio sources. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments. 1.
Practical User Authentication Leveraging Channel State Information (CSI)
"... User authentication is the critical first step to detect identity-based attacks and prevent subsequent malicious attacks. How-ever, the increasingly dynamic mobile environments make it harder to always apply the cryptographic-based methods for user authentication due to their infrastructural and key ..."
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User authentication is the critical first step to detect identity-based attacks and prevent subsequent malicious attacks. How-ever, the increasingly dynamic mobile environments make it harder to always apply the cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, we ex-plore to use channel state information (CSI), which is avail-able from off-the-shelf WiFi devices, to conduct fine-grained user authentication. We propose an user-authentication frame-work that has the capability to build the user profile re-silient to the presence of the spoofer. Our machine learn-ing based user-authentication techniques can distinguish two users even when they possess similar signal fingerprints and detect the existence of the spoofer. Our experiments in both office building and apartment environments show that our framework can filter out the signal outliers and achieve higher authentication accuracy compared with existing ap-proaches using received signal strength (RSS).
Portability of an RF Fingerprint of a Wireless Transmitter
"... Abstract—In conventional wireless networks, security issues are primarily considered above the physical layer and are usually based on bit-level algorithms to establish the identity of a legitimate wireless device. Physical layer security is a new paradigm in which features extracted from an analog ..."
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Abstract—In conventional wireless networks, security issues are primarily considered above the physical layer and are usually based on bit-level algorithms to establish the identity of a legitimate wireless device. Physical layer security is a new paradigm in which features extracted from an analog signal can be used to establish the unique identity of a transmitter. Our previous research work into RF fingerprinting has shown that every transmitter has a unique RF fingerprint owing to imperfections in the analog components present in the RF front end. Generally, it is believed that the RF fingerprint of a specific transmitter is same across all receivers. That is, a fingerprint created in one receiver can be transported to another receiver to establish the identity of a transmitter. However, to the best of the author’s knowledge, no such example is available in the literature in which an RF fingerprint generated in one receiver is used for identification in other receivers. This paper presents the results of experiments, and analyzing the feasibility of using an universal RF fingerprint of a transmitter for identification across different receivers.
