To incorporate the user‟s convenient for utilization of biometric feature for the cryptographic security. In order to reduce the effort of remember a memorable password. To generate the key from the biometric features for security is so as to make the key unpredictable to an intruder lacking the knowledge about the user‟s biometrics. Multimodal key generation so as to obtain a cryptographic optimal security. In this paper, we propose an efficient technique based on Multimodal Biometric (FACE and IRIS) for generation of secure key generation. The proposed technique is composed of three main modules namely Feature Extraction, Multimodal biometric template generation, Cryptographic key Generation. Initially, the features like Texture properties are extracted from the face and iris respectively. Consequently, the extracted features are fused together at the feature level to obtain the multi-modal Biometric Template. As a final 256-bit secure cryptographic key is generated from the multi-Biometric template.

Advances in Natural and Applied Sciences

Content based Image retrieval (CBIR) means search the contents of the image instead of information and capture images from database as per the user requirement. Content refers to as color, shapes, textures or any other information.The image retrieval is interesting and fastest developing methodology in all fields. It iseffective and wellorganized approach for retrieving the image from large scale database. CBIR is a technique to take input as query object and gives output from an image database. To build up content based image retrieval system, to improve various processes implicated in retrieval like feature extraction, Image retrieval and similarity matching techniques. In this paper surveys has been conducted on some features such as color, texture and shape retrieval of images from the database and also study to compared content based image retrieval features like Color, texture and shape for efficient and accurate image retrieval. After going through exhaustive analysis of these CBIR techniques there isvarious parameters to review the paper, some of them it is found that each technique have its own strengths and limitations.So this paper gives summarization of the different features of images with their functionality for content based image retrieval systems. General Terms Image Retrieval, Image database

/pdf/analytical-study-of-cbir-techniques-tljul5a1ya.pdf

Analytical Study of CBIR Techniques

With the wide spread utilization of Biometric identification systems, the authenticity of biometric data poses as an important issue. Reliability and accuracy in hiding the biometric data is a dominant concern to the security in a biometric based system. Multimodal Biometric System using multiple sources of data has been widely recognized but the models for multimodal biometric system have only recently received attention. In this paper, a novel approach for creating a multimodal biometric system has been proposed. The multimodal biometric system is implemented using the different fusion schemes such as Discrete Wavelet Transform Fusion, Stationary Wavelet Transform Fusion, Laplacian Gradient Fusion and Pyramid Gradient Fusion to improve the performance of the system. These fusion schemes are termed as multiscale transform. In feature extraction level, the information extracted from different modalities is stored in vectors on the basis of their modality. These modalities are then blended to produce a joint template. The obtained templates are analyzed to find the best template.

The field of image retrieval technologies have enabled the creation of large image datasets. It is necessary to develop appropriate information systems to efficiently manage these collections. An image retrieval system is a computer system for searching, browsing and retrieving images from a large database of images.An approach known as Content-Based Image Retrieval (CBIR) systems used to retrieve images similar to a user-defined specification or pattern (image, sketch etc). Their goal is to support image retrieval based on content properties (colour, texture and shape), usually encoded into feature vectors. CBIR approach is an automatic retrieval process, instead of the traditional keyword-based approach, which usually is very time-consuming. The CBIR technology has been used in several applications such as, digital libraries, crime prevention, medicine, historical research, fingerprint identification, biodiversity information systems.

/pdf/content-based-image-retrieval-using-spatial-features-5gg1hfdap2.pdf

Content Based Image Retrieval Using Spatial Features

The most popular technique for identifying and blocking malicious network requests is the intrusion detection system, or IDS for short. They are positioned carefully to keep an eye on network traffic going to and coming from every device. Most networking devices can employ an IDS with the use of virtual machines and sophisticated switches. While having good accuracy, the classic SIDS (Signature-Based Intrusion Detection System) cannot identify many modern incursions, such as zero-day attacks, as it relies on a pattern matching technique. Instead, the majority of recently launched attacks can be detected using machine learning, statistical, and knowledge-based methods. An anomaly is defined as any significant difference between the observed behavior and the model.The training phase and the testing phase make up the two stages of the development of these models. During the training phase, a model of typical behavior is learned using the average traffic profile. The system's ability to generalize to as-yet-undiscovered intrusions is then determined during the testing step using a fresh data set. In order to identify network traffic anomalies, we have used an unsupervised machine-learning approach called Isolation Forest in this paper. Using the anomaly score, the algorithm finds the outliers. The KDD data set, a well-known benchmark in the study of Intrusion Detection methods, has been used for training and testing. Keywords: anomaly detection; machine learning; network security

Machine Learning for the Identification of Network Anomalies

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Content Based Image Retrieval Using Spatial Features

Machine Learning for the Identification of Network Anomalies