Vehicular Ad-hoc network is new and emerging technology. Researchers are gaining interest in this technology. Due to its open nature it is vulnerable to various attacks. Sybil attack is one of them. Various defense techniques have been given by researchers. In this work we briefly explain those defense techniques, given recently. We categorize these techniques as trusted certificates base, resource testing based and social network based. We give an overview of some defense schemes based on first two categories. We also give a summary of the techniques given in this paper, which is based on some parameters used in those techniques.

Biometric authentication plays a major role in security as these are by nature unique for every human. But the security is compromised when the pattern matching system is not accurate. Authentication system like fingerprint recognition is most commonly used biometric authentication system. In this paper survey is done on fingerprint recognition techniques. And different approaches are studied in terms of accuracy and performance. As fingerprint may also contain noise; so image de-noising techniques are also studied Cross ridge frequency analysis of fingerprint images is performed by means of statistical measures and weighted mean phase is calculated. These different features along with ridge reliability or ridge centre frequency are given as inputs to a fuzzy c-means classifier..

In the digital world, Recently growth of online shopping site for purchasing clothes, electronic items, glossary etc and online transaction for transfer money is increasing day by day . At the same time, criminals have become able to doing fault and earning money through wrong ways .that’s why fraud grows. With the development of Machine Learning in the field of Computer Science and Engineering, its application in the different domain also in fields like Medical, Marketing, Telecommunication, finance, etc. The reason for the popularity of Machine Learning in these domains is due to its high accuracy prediction. That’s why over many years, machine learning has been used in fraud detection. With the advancement of technology in online transactions, fraud is the greatest issue for businesses and has become difficult to recognize than the traditional form of this crime. Historically, the area of Fraud Detection is interrelated to Data Mining & Text Mining. Due to the sudden growth of fraud whose outcome is loss of trillions of rupees worldwide every year, various modern techniques in detecting fraud were proposed that are progressed without interruption and applied to many business fields. Bank frauds worth ₹2.05 trillion happened in the last 11 years, among which there were overall 53,334 fraud issues in the usage of RBI data. The principle purpose behind this write up is to review different methods in identifying frauds corresponding to the unusualness in the transactions. The supervised and unsupervised machine learning algorithms will be used to identify fraud and the best first search optimization will be analyzed to compare both results, i.e., before and after optimization

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