An improved and secure multiserver authentication scheme based on biometrics and smartcard

With the advancement in internet technologies, the number of servers has increased remarkably to provide more services to the end users. These services are provided over the public channels, which are insecure and susceptible to interception, modification, and deletion. To provide security, registered entities are authenticated and then a session key is established between them to communicate securely. The conventional schemes anow a user to access services only after their independent registration with each desired server in a multiserver system. Therefore, a user must possess multiple smartcards and memorize various identities and passwords for obtaining services from multiple servers. This has led to the adoption of multiserver authentication in which a user accesses services of multiple servers after registering himself at only one central authority. Recently, Kumar and Om discussed a scheme for multiserver environment by using smartcard. Since the user-memorized passwords are of low entropy, it is possible for an attacker to guess them. This paper uses biometric information of user to enhance the security of the scheme by Kumar and Ore. Moreover, we conducted rigorous security analyses （informal and formal） in this study to prove the security of the proposed scheme against all known attacks. We also simulated our scheme by using the automated tool, ProVerif, to prove its secrecy and authentication properties. A comparative study of the proposed scheme with the existing related schemes shows its effectiveness.

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

Design of a USIM and ECC based handover authentication scheme for 5G-WLAN heterogeneous networks

A variety of wireless communication technologies have been developed to provide services to a large number of users.The future integrated 5G-WLAN wireless networks will support seamless and secure roaming,and various types of real-time applications and services,which will be the trend of next-generation computing paradigm.In this paper,we discuss the privacy and security problems in 5G-WLAN heterogeneous networks and present a logical 5G-WLAN integrated architecture.We also propose a novel USIM and ECC based design of handover authentication for next-generation 5G-WLAN heterogeneous networks that can provide secure and seamless Internet connectivity.Our scheme has the features of strong security and better performance in terms of computation cost,energy cost,and storage cost as compared with the state-of-the-art schemes.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

Designing Intrusion Detection System for Web Documents Using Neural Network

Cryptographic systems are the most widely used techniques for information security. These systems however have their own pitfalls as they rely on prevention as their sole means of defense. That is why most of the organizations are attracted to the intrusion detection systems. The intrusion detection systems can be broadly categorized into two types, Anomaly and Misuse Detection systems. An anomaly-based system detects com-puter intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous. Misuse detection systems can detect almost all known attack patterns;they however are hardly of any use to de-tect yet unknown attacks. In this paper, we use Neural Networks for detecting intrusive web documents avail-able on Internet. For this purpose Back Propagation Neural (BPN) Network architecture is applied that is one of the most popular network architectures for supervised learning. Analysis is carried out on Internet Security and Acceleration (ISA) server 2000 log for finding out the web documents that should not be accessed by the unau-thorized persons in an organization. There are lots of web documents available online on Internet that may be harmful for an organization. Most of these documents are blocked for use, but still users of the organization try to access these documents and may cause problem in the organization network.

Extracting Significant Patterns for Oral Cancer Detection Using Apriori Algorithm

Presently, no effective tool exists for early diagnosis and treatment of oral cancer. Here, we describe an approach for cancer detection and prevention based on analysis using association rule mining. The data analyzed are pertaining to clinical symptoms, history of addiction, co-morbid condition and survivability of the cancer patients. The extracted rules are useful in taking clinical judgments and making right decisions related to the disease. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay and treatment with sufficient support and confidence suitable for detection of early-stage oral cancer.