Composite Fixed-Length Ordered Features for Palmprint Template Protection with Diminished Performance Loss

Palmprint recognition has become more and more popular due to its advantages over other biometric modalities such as fingerprint, in that it is larger in area, richer in information and able to work at a distance. However, the issue of palmprint privacy and security (especially palmprint template protection) remains under-studied. Among the very few research works, most of them only use the directional and orientation features of the palmprint with transformation processing, yielding unsatisfactory protection and identification performance. Thus, this paper proposes a palmprint template protection-oriented operator that has a fixed length and is ordered in nature, by fusing point features and orientation features. Firstly, double orientations are extracted with more accuracy based on MFRAT. Then key points of SURF are extracted and converted to be fixed-length and ordered features. Finally, composite features that fuse up the double orientations and SURF points are transformed using the irreversible transformation of IOM to generate the revocable palmprint template. Experiments show that the EER after irreversible transformation on the PolyU and CASIA databases are 0.17% and 0.19% respectively, and the absolute precision loss is 0.08% and 0.07%, respectively, which proves the advantage of our method

DIGITAL SIGNATURE IN CYBER SECURITY

For secure exchanges over open organizations, the Digital Signature method is basic. It is having assortments of uses to guarantee the uprightness of information traded or put away and to demonstrate the character of the originator to the beneficiary. Computerized Signature plans are regularly utilized in cryptographic conventions to offer types of assistance like element verification, confirmed key vehicle and validated key arrangement. Multi-biometric frameworks are as a rule perpetually sent in some huge scope biometric applications (e.g., FBI-IAFIS, UIDAI plot in India) since they have many points of interest, for example, second rate mistake rates and more prominent people inclusion contrasted with uni-biometric frameworks. In this paper, we propose a component level combination system to all the while ensure various layouts of a client as a sole secure sketch. Our main commitments include: 1) useful execution of the proposed highlight level combination development utilizing two notable biometric cryptosystems, in particular, fluffy vault and fluffy responsibility, and 2) nitty gritty investigation of the compromise between coordinating exactness and security in the proposed multibiometric cryptosystems dependent on two divergent information bases (one genuine and one virtual multimodal information base), each containing the three most famous biometric modalities, to be specific, unique mark, iris, and face. Test results give subtleties that together the multibiometric cryptosystems proposed here have progressed safe-haven and equal execution contrasted with their uni-biometric partners

Finding a suitable threshold value for an iris-based authentication system

Authentication is the first line of defense of any information technology systems. One of the popular methods used today is biometric, and iris authentication is gaining popularity. However, the threshold value is deemed to be secure and appropriate has not been thoroughly studied. Threshold is a value that defines the acceptable amount of the correct bits of the image before securely passing the authentication process. Therefore, the main aim of this research was to find a secure and suitable threshold value used in iris authentication system, where iris localization was done by using Circle Hough Transform technique. Iris image databases v.4 from the Chinese Academy of Sciences Institute of Automatic (CASIA) were used in this research. The way to find the appropriate threshold was to test for the right balance of the GAR, FRR and FAR values when trying to verify the person’s identity. The results of the test revealed that the appropriate threshold had the value of 72.9246 percent of all the available bits of the iris image. Both had a high GAR and very low FAR and FRR values.  It can be concluded that the obtained threshold value was suitable and secure

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort

A Survey on Biometrics and Cancelable Biometrics Systems

Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results

An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language