Honeypots are decoys designed to trap, delay, and gather information about attackers. We can use honeypot logs to analyze attackers' behaviors and design new defenses. A virtual honeypot can emulate multiple honeypots on one physical machine and provide great flexibility in repesenting one or more networks of machines. But when attackers recognize a honeypot, it becomes useless. In this paper, we address issues related to detecting and "camouflaging" virtual honeypots, in particular Honeyd, which can emulate any size of network on physical machines. We find that an attacker may remotely fingerprint Honeyd by measuring the latency of the network links emulated by Honeyd. We analyze the threat from this fingerprint attack based on the Neyman-Pearson decision theory and find that this class of attack can achieve a high detection rate and low false alarm rate. In order to counter this fingerprint attack, we make virtual honeypots behave like their surrounding networks and blend in with their surroundings. We design a camouflaged Honeyd by revising a small part of the Honeyd toolkit code and by appropriately patching the operating system. Our experiments demonstrate the effectiveness of our approach to camouflaging Honeyd.

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3% and 91.6%, respectively, which compare favorably with other published research and are one of the best results published to date.

We present a comparison of two key approaches for fingerprint identification. These approaches are based on (a) classification followed by verification, and (b) indexing followed by verification. The fingerprint classification approach is based on a novel feature-learning algorithm. It learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. These features are then used for classification of fingerprints into five classes. The indexing approach is based on novel triplets of minutiae. The verification algorithm, based on least square minimization over each of the possible minutiae triplet pairs, is used for identification in both cases. On the NIST-4 fingerprint database, the comparison shows that, although correct classification rate can be as high as 92.8% for 5-class problems, the indexing approach performs better, based on the size of the search space and identification results.

Fingerprints have long been used for person authentication. However, there is not enough scientific research to explain the probability that two fingerprints, which are impressions of different fingers, may be taken as the same one. In this paper, we propose a formal framework to estimate the fundamental algorithm independent error rate of fingerprint matching. Unlike a previous work, which assumes that there is no overlap between any two minutiae uncertainty areas and only measures minutiae's positions and orientations. In our model, we do not make this assumption and measure the relations, i.e., ridge counts between different minutiae as well as minutiae's positions and orientations. The error rates of fingerprint matching obtained by our approach is significantly lower than that of previously published research. Results are shown using NIST-4 fingerprint database. These results contribute toward making fingerprint matching a science and settling the legal challenges to fingerprints.

We are concerned with accurate and efficient indexing of fingerprint images. We present a model-based approach, which efficiently retrieves correct hypotheses using novel features of triangles formed by the triplets of minutiae as the basic representation unit. The triangle features that we use are its angles, handedness, type, direction, and maximum side. Geometric constraints based on other characteristics of minutiae are used to eliminate false correspondences. Experimental results on live-scan fingerprint images of varying quality and NIST special database 4 (NIST-4) show that our indexing approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion, and clutter. We also perform scientific experiments to compare the performance of our approach with another prominent indexing approach and show that the performance of our approach is better for both the live scan database and the ink based database NIST-4.

Due to the complex distortions involved in two impressions of the same finger, fingerprint identification is still a challenging problem for person authentication. In this paper, we propose a fingerprint identification approach based on the triplets of minutiae. The features that we use to find the potential corresponding triangles include angles, triangle orientation, triangle direction, maximum side, minutiae density and ridge counts. False corresponding triangles are eliminated by applying constraints to the transformation between two potential corresponding triangles. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that, as compared to the linear search, the proposed approach provides a reduction by a factor of 200 for the number of the hypotheses that need to be considered and it can achieve good performance even when a large portion of fingerprints in the database are of poor quality.

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint matching approach based on Genetic Algorithms (GA), which finds the optimal global transformation between two different fingerprints. In order to deal with low quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design the fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.

Most current techniques for minutiae extraction in fingerprint images utilize complex preprocessing and postprocessing. In this paper, we propose a new technique, based on the use of learned templates, which statistically characterize the minutiae. Templates are teamed from examples by optimizing a criterion function using Lagrange's method. To detect the presence of minutiae in test images, templates are applied with appropriate orientations to the binary image only at selected potential minutia locations. Several performance measures, which evaluate the quality and quantity of extracted features and their impact on identification, are used to evaluate the significance of learned templates. The performance of the proposed approach is evaluated on two sets of fingerprint images: one is collected by an optical scanner and the other one is chosen from NIST special fingerprint database 4. The experimental results show that learned templates can improve both the features and the performance of the identification system.

We present an approach for extraction of minutiae features from fingerprint images. The proposed approach is based on the use of logical templates for minutiae extraction in the presence of data distortion. A logical template is an expression that is applied to the binary ridge (valley) image at selected potential locations to detect the presence of minutiae at these locations. It is adapted to local ridge orientation and frequency. We discuss the proposed technique in detail, and present experimental results on low-resolution images of various qualities.