Comparing to well protected data frames, Wi-Fi management frames (MFs) are extremely vulnerable to various attacks. Since MFs are transmitted without encryption, attackers can forge them easily. Such attacks can be detected in cooperative environment such as Wireless Intrusion Detection System (WIDS). However, in non-cooperative environment it is difficult for a single station to identify these spoofing attacks using Received Signal Strength (RSS)-based detection, due to the strong correlation of RSS to both the transmission power (Txpower) and the location of the sender. By exploiting some unique characteristics (i.e., rapid spatial decorrelation, independence of Txpower, and much richer dimensions) of the Channel State Information (CSI), a standard feature in 802.11n Specification, we design a prototype, called CSITE, to authenticate the Wi-Fi management frames by a single station without external support. Our design CSITE, built upon off-the-shelf hardware, achieves precise spoofing detection without collaboration and in-advance finger-print. Several novel techniques are designed to address the challenges caused by user mobility and channel dynamics. To verify the performances of our solution, we implement a prototype of our design and conduct extensive evaluations in various scenarios. Our test results show that our design significantly outperforms the RSS-based method in terms of accuracy, robustness, and efficiency: we observe about 8 times improvement by CSITE over RSS-based method on the falsely accepted attacking frames.
The rapid growth of location-based applications has spurred extensive research on localization. Nonetheless, indoor localization remains an elusive problem mostly because the accurate techniques come at the expense of cumbersome war-driving or additional infrastructure. Towards a solution that is easier to adopt, we propose SpinLoc that is free from these requirements. Instead, SpinLoc levies a little bit of the localization burden on the humans, expecting them to rotate around once to estimate their locations. Our main observation is that wireless signals attenuate differently, based on how the human body is blocking the signal. We find that this attenuation can reveal the directions of the APs in indoor environments, ultimately leading to localization. This paper studies the feasibility of SpinLoc in real-world indoor environments using off-the-shelf WiFi hardware. Our preliminary evaluation demonstrates accuracies comparable toschemes that rely on expensive war-driving. Location distinction is the ability to determine when a device has changed its position. We explore the opportunity to use sophisticated PHY-layer measurements in wireless networking systems for location distinction. We first compare two existing location distinction methods - one based on channel gains of multi-tonal probes, and another on channel impulse response. Next, we combine the benefits of these two methods to develop a new link measurement that we call the complex temporal signature. We use a 2.4 GHz link measurement data set, obtained from CRAWDAD [10], to evaluate the three location distinction methods. We find that the complex temporal signature method performs significantly better compared to the existing methods. We also perform new measurements to understand and model the temporal behavior of link signatures over time. We integrate our model in our location distinction mechanism and significantly reduce the probability of false alarms due to temporal variations of link signatures. This paper explores the viability of precise indoor localization using physical layer information in WiFi systems. We find evidence that channel responses from multiple OFDM subcarriers can be a promising location signature. While these signatures certainly vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We attempt to harness these opportunities through a functional system called PinLoc, implemented on off-the-shelf Intel 5300 cards. We evaluate the system in a busy engineering building, a crowded student center, a cafeteria, and at the Duke University museum, and demonstrate localization accuracies in the granularity of 1m x 1m boxes, called “spots”. Results from 100 spots show that PinLoc is able to localize users to the correct spot with 89 mean accuracy, while incurring less than 6 false positives. We believe this is an important step forward, compared to the best indoor localization schemes of today, such as Horus. Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach. The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radio-tagged objects, and for wireless network security in detection of replication attacks. In this paper, we propose using a measured temporal link signature to uniquely identify the link between a transmitter and a receiver. When the transmitter changes location, or if an attacker at a different location assumes the identity of the transmitter, the proposed link distinction algorithm reliably detects the change in the physical channel. This detection can be performed at a single receiver or collaboratively by multiple receivers. We record over 9,000 link signatures at different locations and over time to demonstrate that our method significantly increases the detection rate and reduces the false alarm rate, in comparison to existing methods.