Crypto Steganography using linear algebraic equation

Demand of information security is increasing day by day with the exponential growth of Internet. The content of message is kept secret in cryptography, where as steganography message is embedded into the cover image. In this paper a system is developed in which cryptography and steganography are used as integrated part along with newly developed enhanced security model. In cryptography the process of encryption is carried out using symmetric block ciphers with linear algebraic equation to encrypt a message [1] and the obtained cipher text is hidden in to the cover image which makes the system highly secured. Least Significant Bit (LSB) technique is used for message hiding which replaces the least significant Bits of pixel selected to the hide the information. A large number of commercial steganographic programs use LSB as the method of choice for message hiding in 24-bit,8bit-color images, and gray scale images. It is observed from the simulation study that both methods together enhance security significantly

Semantic Learning and Web Image Mining with Image Recognition and Classification

Image mining is more than just an extension of data mining to image domain. Web Image mining is a technique commonly used to extract knowledge directly from images on WWW. Since main targets of conventional Web mining are numerical and textual data, Web mining for image data is on demand. There are huge image data as well as text data on the Web. However, mining image data from the Web is paid less attention than mining text data, since treating semantics of images are much more difficult. This paper proposes a novel image recognition and image classification technique using a large number of images automatically gathered from the Web as learning images. For classification the system uses imagefeature- based search exploited in content-based image retrieval(CBIR), which do not restrict target images unlike conventional image recognition methods and support vector machine(SVM), which is one of the most efficient & widely used statistical method for generic image classification that fit to the learning tasks. By the experiments it is observed that the proposed system outperforms some existing search system

Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each

Computing Symmetric Block Cipher Using Linear Algebraic Equation.

In this paper, a pair of symmetric block ciphers has been developed for encryption and decryption of text file. The characters in the file are represented by the ASCII codes. A substitution table and a reverse substitution table are formed by using a key. The process of encryption and decryption is carried by using linear algebraic equations. However, the cryptanalysis has been discussed for establishing the strength of the algorithm. Result and analysis exhibits that the current algorithm works well and more secured to break the cipher

Metacognition using classifier system: A step approaching intelligent agents

Meta-cognition allows one to monitor and adaptively control cognitive processes. It guides people to select, evaluate, revise, and abandon cognitive tasks, goals, and strategies. Also, meta-cognition can play an important role in human-like software agents. It includes meta-cognitive knowledge, meta cognitive monitoring, and meta cognitive regulation. The main purpose of this research paper is to understand the principles of natural minds and adopt these principles to simulate artificial minds. We consider the conscious software agent, “CMattie” which has its cognitive science side (cognitive modelling) as well as its computer science side (intelligent software). We describe the incorporation of meta cognition in CMattie using fuzzy classifier system including Genetic algorithm and Probabilistic approaches