DNA Cryptography

In today's world, sensitive information like secret message, financial transaction, medical report, personal information is transferred over public communication channel. Since the advancement of communication begins, data security becomes a massive problem. The increasing rate of eavesdropping over communication channel leads the introduction of cryptography algorithm for data transmission. Different traditional cryptographic technique is adopted worldwide for protected data transmission. The recent advancement on this field is DNA based cryptography. This chapter describes the application of DNA as computational tool after the exposure of its capability was discovered by Leonard M. Adleman in 1994. Its random nature also helps the cryptography algorithm to become unbreakable. Conventional cryptography methods are sometimes susceptible to attack by the intruder. Therefore the idea of using codon based DNA as a computational tool is used in this cryptography method as an alternative method that fetches new hope in communication technology.

Introduction to Molecular Computation

The present chapter deals with the topic Molecular Computation. The chapter first defines the basic terminologies associated with the processes. The chapter discusses the basic molecular biology and DNA and membranes. Emphases are given on the structural arrangements of DNA and the molecular architecture of biological membranes. The chapter also focuses on the molecular logic behind the applications of DNA and bimolecular membranes in computations. There are discussions on the current researches that are going on in the field of DNA and membrane computations. There are comparative analyses of the existing computational techniques with molecular computations. There are very few reports that deal with the underlying basics of molecular computation techniques. Thus the chapter may be a first hand guide for researchers interested in the field. The chapter is written for the benefits of both the biologists as well as computer scientists.

Sustainability of Public Key Cryptosystem in Quantum Computing Paradigm

With the emergence of technological revolution to host services over Internet, secure communication over World Wide Web becomes critical. Cryptographic protocols are being in practice to secure the data transmission over network. Researchers use complex mathematical problem, number theory, prime numbers etc. to develop such cryptographic protocols. RSA and Diffie Hellman public key crypto systems have proven to be secure due to the difficulty of factoring the product of two large primes or computing discrete logarithms respectively. With the advent of quantum computers a new paradigm shift on public key cryptography may be on horizon. Since superposition of the qubits and entanglement behavior exhibited by quantum computers could hold the potential to render most modern encryption useless. The aim of this chapter is to analyze the implications of quantum computing power on current public key cryptosystems and to show how these cryptosystems can be restructured to sustain in the new computing paradigm.

Source Location Privacy Using Ant Colony Optimization in Wireless Sensor Networks

One of the important application domain of sensor network is monitoring a region and/or tracking a target. In such type of applications, the location of the source node tracking that target is very important. At the same time, if the location of the node currently tracking the target is captured by an adversary then that target may fall into a difficult situation. In this chapter, a solution to source location privacy with the help of Ant Colony Optimization is proposed. The idea of pheromone level is normally used to find out the shortest path between the source node and the base station that to minimize the energy consumption of the networks. The pheromone level of the ants is used here to guide the ants to follow different paths to hide the source location from adversaries who uses traffic analysis to capture the source node.

Optimizing Solution for Storage Space Allocation Problem in Container Terminal Using Genetic Algorithm

Seaport container terminals are essential nodes in sea cargo transportation networks. In container terminal, one of the most important performance measures in container terminals is the service time. Storage space allocation operations contribute to minimizing the vessel service time. Storage space allocation problem at container terminals is a combinatorial optimization NP-hard problem. This chapter proposes a methodology based on Genetic Algorithm (GA) to optimize the solution for storage space allocation problem. A new mathematical model that reflects reality and takes into account the workload balance among different types of storage blocks to avoid bottlenecks in container yard operations is proposed. Also the travelling distance between vessels berthing positions and storage blocks at container yard is considered in this research. The proposed methodology is applied on a real case study data of container terminal in Egypt. The computational results show the effectiveness of the proposed methodology.

Genetic-Algorithm-Based Optimization of Fragile Watermarking in Discrete Hartley Transform Domain

In this chapter, a fragile watermarking scheme based on One-Dimensional Discrete Hartley Transform (1D-DHT) has been proposed to verify the authenticity of color images. One-Dimensional Discrete Hartley Transform (1D-DHT) converts each 1 x 2 sub-matrix of pixel components into transform domain. Watermark (along with a message digest MD) bits are embedded into the transformed components in varying proportion. To minimize the quality distortion, genetic algorithm (GA) based optimization is applied which yields the optimized component corresponding to each embedded component. Applying One-Dimensional Inverse Discrete Hartley Transform (1D-IDHT) on 1 x 2 sub-matrices of embedded components re-generates the pixel components in spatial domain. The reverse approach is followed by the recipient to retrieve back the watermark (along with the message digest MD) which in turn is compared against the re-computed Message Digest (MD') for authentication. Simulation results demonstrate that the proposed technique offers variable payload and less distortion as compared to existing schemes.

Realizing the Need for Intelligent Optimization Tool

A large number of traditional optimization tools are available in the literature, as each of these techniques is suitable to solve a particular problem. Realizing this fact, non-traditional optimization tools have been proposed, which are supposed to be robust enough to solve a variety of problems. Moreover, these tools should be able to reach the optimal solutions quickly and as accurately as possible. The family of non-traditional optimization tools has become bigger, nowadays, which contradicts the very purpose of developing non-traditional optimization tool. In this write-up, the reasons behind this fact have been discussed in detail, and the need for an intelligent optimization tool has been felt, which is supposed to be problem-independent.

Overview of Cellular Computing-Basic Principles and Applications

Computers, due to their raw speed and massive computing power, have been highly used by biologists to expedite life science research whereas several computational algorithms like artificial neural network, genetic algorithm and many similar ones have been inspired by the behaviors of several biological or cellular entities. However till date both these disciplines i.e. life sciences and computer sciences have mostly progressed separately while recent studies are increasingly highlighting the impact of each discipline on the other. The chapter describes several features of biological systems which could be used for further optimizations of computer programs or could be engineered to harness necessary computational capabilities in lieu of traditional silico chip systems. We also highlight underlying challenges and avenues of implementations of cellular computing.

Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model

There are several deep learning approaches that can be applied for analyzing situations in real world problems and inventing their solution in a scientific technique. Supervised data mining methods that predicts instance values, using previously obtained results from already collected data are pretty popular due to their intelligence in machine learning area. Stream data is continuous form of data which can be handled by using incremental learning approach. Stream data learning may face several challenges in real world like concept drift or class imbalance. Concept drift occurs in non-stationary environment where data distribution generation function is dynamic in nature and has no fixed formula to predict the future data distribution nature. Neural network techniques are intelligent enough to improve performance of algorithmic systems that work in such problem domains. This chapter briefly describes how MLP technique is integrated in system so that the system becomes a complete framework for handling unbalanced data with concept drift in the incremental learning strategies.

Variable Length PSO-Based Image Clustering for Image Denoising

This chapter proposed a variable length Particle Swarm Optimization based image clustering technique for restoration of noises from digital images. Here in this two step noise restoration technique the noise free pixels are kept unchanged. The denoising technique uses 3 × 3 test window on the center pixel of the noisy image. Prior to detection and filtering, variable length PSO based image clustering has been done. The output of clustering determines the performance of the subsequent stages of the algorithm. For denoising weighted median filtering technique is proposed. Variable length particles are considered and randomly encoded for the initial population. The length of particles is changed by adding and/or deleting cluster centers present in the particles. Three evaluation criteria are used in the fitness function of the proposed algorithm. The performance of the proposed algorithm is compared with some similar algorithms existing in the literature on several standard digital images.