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Benjamin W. P. Ramsey

dblp:06/7666 · DBLP profile ↗
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5ranked-venue papers
3as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 3 · 1 first-authorComputer networks · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Network security · 77% Authentication and access control · 23%
Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
physical layer
0.212015
Wireless Intrusion Detection and Device Fingerprinting through Preamble Manipulation · IEEE Trans. Dependable Secur. Comput. 2015
Network security › intrusion detection and prevention
intrusion detection
0.212015
Wireless Intrusion Detection and Device Fingerprinting through Preamble Manipulation · IEEE Trans. Dependable Secur. Comput. 2015
Network security › intrusion detection and prevention › intrusion detection › network intrusion detection
wireless intrusion detection
0.212015
Wireless Intrusion Detection and Device Fingerprinting through Preamble Manipulation · IEEE Trans. Dependable Secur. Comput. 2015
Authentication and access control
multi-factor authentication
0.112015
Wireless Intrusion Detection and Device Fingerprinting through Preamble Manipulation · IEEE Trans. Dependable Secur. Comput. 2015
Authentication and access control › network authentication
wireless authentication
0.112015
Wireless Intrusion Detection and Device Fingerprinting through Preamble Manipulation · IEEE Trans. Dependable Secur. Comput. 2015

Methods — techniques the papers use, named apart from their topics

preamble manipulation · 0.4frame length manipulation · 0.4
YearPublicationVenuePosition
2017 The Z-Wave routing protocol and its security implications
abstract
Z-Wave is a proprietary technology used to integrate sensors and actuators over RF and perform smart home and office automation services. Lacking implementation details, consumers are under-informed on the security aptitude of their installed distributed sensing and actuating systems. While the Physical (PHY) and Medium Access Control (MAC) layers of the protocol have been made public, details regarding the network layer are not available for analysis. Using a real-world Z-Wave network, the frame forwarding and topology management aspects of the Z-Wave routing protocol are reverse engineered. A security analysis is also performed on the network under study to identify source and data integrity vulnerabilities of the routing protocol. It is discovered that the topology and routes may be modified by an outsider through the exploitation of the blind trust inherent to the routing nodes of the network. A Black Hole attack is conducted on a real-world Z-Wave network to demonstrate a well-known routing attack that exploits the exposed vulnerabilities. As a result of the discoveries, several recommendations are made to enhance the security of the routing protocol.
Christopher W. Badenhop, Scott R. Graham, Benjamin W. P. Ramsey, Barry E. Mullins, Logan O. Mailloux
Comput. Secur.3
2017 Misuse-based detection of Z-Wave network attacks
Jonathan Fuller 0001, Benjamin W. P. Ramsey, Mason Rice, John M. Pecarina
Comput. Secur.2
2015 Wireless Intrusion Detection and Device Fingerprinting through Preamble Manipulation
abstract
Wireless networks are particularly vulnerable to spoofing and route poisoning attacks due to the contested transmission medium. Recent works investigate physical layer features such as received signal strength or radio frequency fingerprints to localize and identify malicious devices. In this paper we demonstrate a novel and complementary approach to exploiting physical layer differences among wireless devices that is more energy efficient and invariant with respect to the environment. Specifically, we exploit subtle design differences among transceiver hardware types. Transceivers fulfill the physical-layer aspects of wireless networking protocols, yet specific hardware implementations vary among manufacturers and device types. In this paper we demonstrate that precise manipulation of the physical layer header prevents a subset of transceiver types from receiving the manipulated packet. By soliciting acknowledgments from wireless devices using a small number of packets with manipulated preambles and frame lengths, a response pattern identifies the true transceiver class of the device under test. Herein we demonstrate a transceiver taxonomy of six classes with greater than 99 percent accuracy, irrespective of environment. We successfully demonstrate wireless multi-factor authentication, intrusion detection, and transceiver type fingerprinting through preamble manipulation.
Benjamin W. P. Ramsey, Barry E. Mullins, Michael A. Temple, Michael R. Grimaila
IEEE Trans. Dependable Secur. Comput.1
2012 PHY foundation for multi-factor ZigBee node authentication
abstract
The ZigBee specification builds upon IEEE 802.15.4 low-rate wireless personal area standards by adding security and mesh networking functionality. ZigBee networks may be secured through 128-bit encryption keys and by MAC address access control lists, yet these credentials are vulnerable to interception and spoofing via free software tools available over the Internet. This work proposes a multi-factor PHY-MAC-NWK security framework for ZigBee that augments bit-level security using radio frequency (RF) PHY features. These features, or RF fingerprints, can be used to differentiate between dissimilar or like-model wireless devices. Previous PHY-based works on mesh network device differentiation predominantly exploited the signal turn-on region, measured in nanoseconds. For an arbitrary benchmark of 90% or better classification accuracy, this work shows that reliable PHY-based ZigBee device discrimination can be achieved at SNR ≥ 8 dB. This is done using the entire transmission preamble, which is less technically challenging to detect and is over 1000 times longer than the signal turn-on region. This work also introduces a statistical, pre-classification feature ranking technique for identifying relevant features that dramatically reduces the number of RF fingerprint features without sacrificing classification performance.
Benjamin W. P. Ramsey, Michael A. Temple, Barry E. Mullins
GLOBECOM1
2008 Subjective Audio Quality over Secure IEEE 802.11 Wireless Local Area Networks
abstract
This paper presents the results of experiments to measure subjective G.711 audio quality resulting from wireless transmission over encrypted IEEE 802.11b, IEEE 802.11g, and IEEE 802.11n draft 2.0 wireless local area networks (WLANs). Following ITU-T Recommendation P. 800, forty-two human subjects rate audio recordings taken during 12 network conditions. WLAN encryption is alternated between WPA2 and WPA2 with transport mode 3DES IPsec, and simultaneous call volume is alternated between 6 or 10 simultaneous calls. Results indicate that a previous E-model calculation for 802.11b accurately predicts the subjective Mean Opinion Score (MOS). The upper bound for the decline in MOS due to 3DES IPsec is found to be less than 0.65 (90% CI) for all scenarios examined. Additionally, the subjective data suggest that the combination of G.711 encoding and 802.11 transmission makes toll quality (MOS > 4.0) calls over an encrypted 802.11 WLAN impractical.
Benjamin W. P. Ramsey, Barry E. Mullins, Ryan W. Thomas, Todd R. Andel
IPCCC1