Hong-Wei Sun

Efficient vector quantization using genetic algorithm

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.

A fast and elastic fingerprint matching algorithm using minutiae-centered circular regions

Reliably and fast matching fingerprints is still a challenging problem in a fingerprint verification system. This paper proposes a minutiae matching algorithm that uses minutiae-centered circular regions to help ensure the speed of matching and the robustness to non-linear distortion. In our method, a circular region is constructed around each minutia, which can be regarded as a secondary feature. Using the constructed regions, the proposed algorithm can find matched minutiae more rapidly via regional matching. Since each minutias region is formed from only a small area of the fingerprint, our algorithm is more tolerant to non-linear distortion when compared to global matching approaches. On the other hand, the area of the constructed region is much larger than that of local neighborhood in local matching approaches, which means that circular region, including a larger subnet of minutiae, is more reliable and distinct feature. Experiment results show our algorithm s good performance on processing speed and accuracy.

A Quick Algorithmfor Reduction of Attribute in Information Systems

The security of cryptosystems lies in three main factors: the complexity of the encryption algorithm, the length of the key, and safe storage of the key. Safe storage of the key, which is known as key management, is the most vulnerable area in the encryption process. Since biometrics requires the physical presence of the user, we can use biometric data to protect the cipher key. But the main obstacle of the integration is that biometric data are noisy while cryptography requires keys to be exactly right. In this paper, we propose a key management scheme using biometrics, which can generate constant and accurate keys from noisy biometric samples every time. The proposed scheme includes key binding and key retrieval, utilizing a multivariate linear equation and its solution space to generate repeatable keys. Our experiments use fingerprint as an instance, and results show good performance in terms of both accuracy and speed. This scheme can also be applied to other biometric identifiers such as face ,iris, voice, etc., with most substeps being the same except the generation of false feature elements and the process of feature matching.

An efficient anomaly detection algorithm for vector-based intrusion detection systems

This paper proposes a new algorithm that improves the efficiency of the anomaly detection stage of a vector-based intrusion detection scheme. In general, intrusion detection schemes are based on the hypothesis that normal system/user behaviors are consistent and can be characterized by some behavior profiles such that deviations from the profiles are considered abnormal. In complicated computing environments, users may exhibit complicated usage patterns that the user profiles have to be established using sophisticated classification methods such as vector quantization (VQ) technique. However, anomaly detection based on the data set in a high dimension space is inefficient. In this paper we focus on the design of an algorithm that uses principal component analysis (PCA) to improve the anomaly detection efficiency. The main contribution of this research is to demonstrate how the efficiency of the anomaly detection can be raised while the effectiveness of the detection in terms of low false alarm rate and high detection rate can be maintained.

Anomaly Detection in Grid Computing Based on Vector Quantization

An efficient and effective intrusion detection model based the Vector Quantization (VQ) technique is proposed. This model is suitable for security monitoring in the grid computing environment. Experimental results based on this model have shown very promising performance in terms of high detection rate and low false alarm rate.

Improved fingerprint-based remote user authentication scheme using smart cards

Remote user authentication is important to protect web-based services offered over the Internet. The use of smart cards was proposed to avoid the storage of password table in the server. Further protection of user information in the smart cards can be done by imposing a fingerprint verification mechanism. The Lee, Ryu and Yoo (LRY) scheme which provides both smart card based authentication and fingerprint verification is found to be vulnerable to various attacks. This paper proposed an improvement to the LRY scheme which is able to withstand the attacks of the LRY scheme.

An efficient algorithm for fingercode-based biometric identification

With the emerging trend of incorporating biometrics information in e-financial and e-government systems arisen from international efforts in anti-money laundering and counter-terrorism, biometric identification is gaining increasing importance as a component in information security applications Recently, fingercode has been demonstrated to be an effective fingerprint biometric scheme, which can capture both local and global details in a fingerprint In this paper, we formulate fingercode identification as a vector quantization (VQ) problem, and propose an efficient algorithm for fingercode-based biometric identification Given a fingercode of the user, the algorithm aims to efficiently find, among all fingercodes in the database of registered users, the one with minimum Euclidean distance from the users fingercode Our algorithm is based on a new VQ technique which is designed to address the special needs of fingercode identification Experimental results on DB1 of FVC 2004 demonstrate that our algorithm can outperform the full search algorithm, the partial distance search algorithm and the 2-pixel-merging sum pyramid based search algorithm for fingercode-based identification in terms of computation efficiency without sacrificing accuracy and storage.

Robust Remote Authentication for Scalable Web-Based Services

Remote authentication is an important security control mechanism for Web-based services. Some remote authentication schemes based on smart cards were proposed. A new smart card based remote authentication scheme was proposed in this paper to enhance the robustness to meet the scalability requirement of most Web applications.

Efficient Fingercode Classification

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e.g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

Iris-based remote user authentication scheme using smart cards

A remote user authentication scheme based on the algorithm of Awasthi and Lai is proposed. The proposed scheme uses an iris-based biometric cryptographic technique to overcome the difficulty of managing security parameters in the Awasthi and Lai scheme. By combining remote user authentication and iris-based biometric cryptography, the proposed scheme achieves the security strength of the Awasthi and Lai scheme and at the same time addresses the issue of management of secret data of that scheme.