7233163
Attack Detection in Wireless Localization
Accurately positioning nodes in wireless and sensor networks is important because the location of sensors is a critical input to many higher-level networking tasks. However, the localization infrastructure can be subjected to non-cryptographic attacks, such as signal attenuation and amplification, that cannot be addressed by traditional security services. We propose several attack detection schemes for wireless localization systems. We first formulate a theoretical foundation for the attack detection problem using statistical significance testing. Next, we define test metrics for two broad localization approaches: multilateration and signal strength. We then derived both mathematical models and analytic solutions for attack detection for any system that utilizes those approaches. We also studied additional test statistics that are specific to a diverse set of algorithms. Our trace-driven experimental results provide strong evidence of the effectiveness of our attack detection schemes with high detection rates and low false positive rates across both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in two real office buildings. Surprisingly, we found that of the several methods we describe, all provide qualitatively similar detection rates which indicate that the different localization systems all contain similar attack detection capability.
18093029
The Robustness of Localization Algorithms to Signal Strength Attacks: A Comparative Study
In this paper, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We propose several performance metrics that quantify the estimator's precision and error, including Holder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of several point-based and area-based algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. We observed both strong experimental and theoretic evidence that all the algorithms have similar average responses to signal strength attacks.
17642380
Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
This paper presents a new idea of modeling one-dimensional dynamic systems by higher-order ordinary differential equation (HODE) models in stead of by the ARMA models used in the traditional time series analysis. Accordingly, based on the idea of hybrid evolutionary modeling, we modify the HEMA algorithm in (Cao et al., 1998a; Cao et al., 1998b) to approach the modeling problem of HODEs for dynamic systems. Two applications of time series are used to demonstrate its effectiveness and advantages.
1374
Flexible camera calibration using a new analytical radial undistortion formula with application to mobile robot localization
Most algorithms in 3D computer vision rely on the pinhole camera model because of its simplicity, whereas virtually all imaging devices introduce a certain amount of nonlinear distortion, where the radial distortion is the most severe part. Common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This paper presents a new radial distortion model with an easy analytical undistortion formula, which also belongs to the polynomial approximation category. Experimental results are presented to show that with this radial distortion model, satisfactory accuracy is achieved. An application of the new radial distortion model is non-iterative yellow line alignment with a calibrated camera on ODIS, a robot built in our CSOIS.
16325811
Collaborative detection and filtering of shrew DDoS attacks using spectral analysis
This paper presents a new spectral template-matching approach to countering shrew distributed denial-of-service (DDoS) attacks. These attacks are stealthy, periodic, pulsing, and low-rate in attack volume, very different from the flooding type of attacks. They are launched with high narrow spikes in very low frequency, periodically. Thus, shrew attacks may endanger the victim systems for a long time without being detected. In other words, such attacks may reduce the quality of services unnoticeably. Our defense method calls for collaborative detection and filtering (CDF) of shrew DDoS attacks. We detect shrew attack flows hidden in legitimate TCP/UDP streams by spectral analysis against pre-stored template of average attack spectral characteristics. This novel scheme is suitable for either software or hardware implementation. 
 
The CDF scheme is implemented with the NS-2 network simulator using real-life Internet background traffic mixed with attack datasets used by established research groups. Our simulated results show high detection accuracy by merging alerts from cooperative routers. Both theoretical modeling and simulation experimental results are reported here. The experiments achieved up to 95% successful detection of network anomalies along with a low 10% false positive alarms. The scheme cuts off malicious flows containing shrew attacks using a newly developed packet-filtering scheme. Our filtering scheme retained 99% of legitimate TCP flows, compared with only 20% TCP flows retained by using the Drop Tail algorithm. The paper also considers DSE FPGA, and network processor implementation issues and discusses limitations and further research challenges.
887532
A Suite of Schemes for User-Level Network Diagnosis without Infrastructure
It is highly desirable and important for end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications. However, existing tools are inaccurate to infer the link-level loss rates and have large diagnosis granularity (in terms of the number of hops). To address these problems, we propose a suite of user-level diagnosis approaches in two categories: (1) only need to be deployed at the source and (2) deployed at both source and destination. For the former, we propose two fragmentation aided diagnosis approaches (FAD), Algebraic FAD and Opportunistic FAD, which uses IP fragmentation to enable accurate link-level loss rate inference. For the latter category, we propose Striped Probe Analysis (SPA) which significantly improves the diagnosis granularity over those of the source-only approaches. Internet experiments are applied to evaluate each individual schemes (including an improved version of the state-of-the-art tool, Tulip [1]) and various hybrid approaches. The results indicate that our approaches dramatically outperform existing work (especially for diagnosis granularity) and provide not only the best performance but also smooth tradeoff among deployment requirement, diagnosis accuracy and granularity.
37744500
Improving Nearest Neighbor Classification with Simulated Gravitational Collapse
The performance of the Nearest Neighbor classifier drops significantly with the increase of the overlapping of the distribution of different classes. To overcome this drawback, we propose to simulate the physical process of gravitational collapse to trim the boundaries of the distribution of each class to reduce overlapping. The proposed simulated gravitational collapse(SGC) algorithm is tested on 7 real-world data sets. Experimental results show that the nearest prototype classifier based on SGC outperforms conventional NN and k-NN classifiers.