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Process of Fingerprint Authentication using Cancelable Biohashed Template

Mamatha K. R., Radhika K. R.

Computer Science

2023

T. 24 (4)

537--564

Template protection using cancelable biometrics prevents data loss and hack-ing stored templates, by providing considerable privacy and security. Hashingand salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacksnamely brute-force attack, dictionary attack, rainbow table attacks. Saltingclaims that random data can be added to input of hash function to ensureunique output. Hashing are speed bumps in an attacker’s road to breach user’sdata. Research proposes a contemporary two factor authenticator called Bio-hashing. Biohashing procedure is implemented by recapitualted inner productover a pseudo random number generator key, as well as fingerprint featuresthat are a network of minutiae. Cancelable template authentication used infingerprint-based sales counter accelerates payment process. Fingerhash is codeproduced after applying biohashing on fingerprint. Fingerhash is a binary stringprocured by choosing individual bit of sign depending on a preset threshold.Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentica-tion accuracy is found to be nearly 97%. Results compared with state-of-artapproaches finds promising.

L-SCANN: Logarithmic Subcentroid and Nearest Neighbor

Tohari A., Kharisma M.

Journal of Telecommunications and Information Technology

2016

nr 4

71--80

Securing a computer network has become a need in this digital era. One way to ensure the security is by deploying an intrusion detection system (IDS), which some of them employs machine learning methods, such as k-nearest neighbor. Despite its strength for detecting intrusion, there are some factors, which should be improved. In IDS, some research has been done in terms of feature generation or feature selection. However, its performance may not be good enough. In this paper, a method to increase the quality of the generated features while maintaining its high accuracy and low computational time is proposed. This is done by reducing the search space in training data. In this case, the authors use distance between the evaluated point and the centroid of the other clusters, as well as the logarithmic distance between the evaluated point and the subcentroid of the respective cluster. Besides the performance, the effect of homogeneity in extracting centroid and subcentroid on the accuracy of the detection model is also evaluated. Based on conducted experiment, authors find that the proposed method is able to decrease processing time and increase the performance. In more details, by using NSL-KDD 20% dataset, there is an increase of 4%, 2%, and 6% from those of TANN in terms of accuracy, sensitivity and specificity, respectively. Similarly, by using Kyoto 2006 dataset, proposed method rises 1%, 3%, and 2% than those of TANN.