An Iterative Explicit Method for Parabolic Problems with Cylindrical Symmetry-Increased Accuracy on Non-Uniform Grid

In this paper, the alternating group explicit (AGt-j iterative method is applied to cylindrical problems involving regular domains on a non-uniform grid. The procedure uses the fractional splitting strategy which is applied alternately at each half (intermediate) time step on a tridiagonal system of difference equations. The method is shown to be more accurate than the corresponding AGE scheme solved earlier by the authors using an uniform grid system but with a reduced stability range

Improving intrusion detection using genetic algorithm

Intrusion Detection System (IDS) is one of the key security components in today’s networking environment. A great deal of attention has been recently paid to anomaly detection to accomplish intrusion detection. However, a major problem with this approach is maximizing detection rate and accuracy, as well as minimizing false alarm i.e., inability to correctly discover particular types of attacks. To overcome this problem, a genetic algorithm approach is proposed. Genetic Algorithm (GA) is most frequently employed as a robust technology based on machine learning for designing IDS. GAs are search algorithms which are based on the principles of natural selection and genetics. GA functions on a number of possible solutions using the principle of survival of the fittest with the aim to generate better approximations to solve a particular problem GA is facing. The validity of this approach is verified using Knowledge Discovery and Data Mining Cup 1999 (KDD Cup ’99) dataset. The experimental results demonstrate that the proposed approach outperforms the existing techniques, with the detection rate of attack and false alarm rates of 95.7265 and 4.2735, respectively

Improve cloud computing security using RSA encryption with Fermat's little theorem

Cloud computing (CC) is new technology for hosting and delivering services over the Internet. It moves computing and data away from desktop and portable PCs into large data centers.CC is a Internet based computing, the entire data reside over a set of networked resources, this data can be accessed through virtual machines like i phone, PC etc.CC help to reduce hardware, maintenance and installation cost. But security and privacy is the two major issues in this field and it prevent users for trusting CC. Cloud computing share distributed resources in the open environment via the network, so it makes security problems .To keep user data highly confidentially against un-trusted servers and from malicious attacks is very important. Encryption is the one of the most secured way using prevent unauthorized access. Hence we provide a new method for Cloud Computing Security by applying RSA algorithm and Fermat's theorem together. Its help to build a new trusted cloud computing environment. By using Fermat's theorem can be speed up the RSA Encryption

A Parallel AGE Method for Parabolic Problems with Special Geometries

The alternating group explicit (AGE), an explicit iterative method for parabolic problems involving regular domains of cylindrical symmetry is implemented in parallel on a MIMD Sequent S27 system. The AGE method is suitable for parallel computers as it possesses separate and independent tasks, i.e (2 x 2) blocks which can be executed at the same time without interfering with each other. This paper reports the development and implementation of the parallel AGE algorithm. The results from parallel implementation are compared with those of the sequential implementation

Key transformation approach for Rijndael security.

The aim of the study is to improve the security of Rijndael key scheduling by increasing the bit contusion and diffusion of the Rijndael subkey, Rijndael is a block cipher designed by Joan Daemen and Vincent Rijmen. It is a combination of security, performance, efficiency, implementability and flexibility that makes it the best selection for Advanced Encryption Standard (AES). However, the 128 bit Rijndael key schedule does not satisfy the frequency (bit confusion) test for majority of subkeys and does not satisfy the avalanche (bit diffusion) test for any subkeys. These contribute to some attacks in the key schedule. Thus, a new transformation method which is called Shiftrow is proposed into the 128-bit Rijndael Key Schedule based upon information principles (bit confusion and diffusion properties). The new method has shown positive results in terms of the bit confusion and diffusion of subkey and it has increased bit confusion and diffusion compared to the subkey of the original Rijndael key schedule

Developing Translation Rules for Converting Relational to Object Oriented Database Conceptual Schema

A multidatabase is a confederation of pre-existing distributed, heterogeneous, and autonomous database system. Obviously, the integration process is essential in the effort of forming a distributed, heterogeneous database system. This process generally consists of two main phases, which are conceptual schema translation phase followed by the integration phase. This paper presents a translation approach to convert relational database schema to object-oriented database schema. The translation approach consists of a set of translation rules, which is based on inclusion dependencies, key attributes and types of attributes. A database schema translation tool prototype, called RETOO (RElational-ToObject- Qriented) is then developed based on the proposed translation approach. RETOO receives a relational database schema as input data and generates an object-oriented database schema as the output. The translation approach is not only able to maintain the semantics of the relational database schema, but also enhance the semantics of the translated object-oriented schema via objectoriented data modeling concepts

Tripling formulae of elliptic curve over binary field in Lopez-Dahab model

In elliptic curve cryptosystem (ECC), scalar multiplication is the major and most costly operation. Scalar multiplication involves with point operations such as point addition, point doubling, and point tripling. Scalar multiplication can be improved by using efficient point operations. This research focuses on point tripling operation for elliptic curves over the binary field in Lopez-Dahab (LD) model. Currently, there is no existing tripling formula for this model. Traditionally, tripling is computed using one doubling followed by one addition (i.e. 3P=2P+P) with cost of 18M+8S, where M is field multiplication and S is field squaring. In this paper, we proposed tripling formulae with cost of 12M+7S. We proved the formulae and proposed its algorithm. The tripling saved 6M+1S which contribute to cost reduction in multiplication and squaring by 33% and 12.5% respectively when compared with the traditional method. For National Institute of Standards and Technology (NIST) curve (i.e. where a = 1), the cost of the tripling is further reduced to 10M+7S which saved 8M+1S from the traditional one. Further cost reduction in multiplication and squaring by 44% and 12.5% respectively

Malware classification framework for dynamic analysis using Information Theory

Objectives: 1. To propose a framework for Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique. 2. To extract behavioral patterns from execution reports of malware in terms of its features and generates a data repository. 3. To select the most promising features using information theory based concepts. Methods/Statistical Analysis: Today, malware is a major concern of computer security experts. Variety and in- creasing number of malware affects millions of systems in the form of viruses, worms, Trojans etc. Many techniques have been proposed to analyze the malware to its class accurately. Some of analysis techniques analyzed malware based upon its structure, code flow, etc. without executing it (called static analysis), whereas other techniques (termed as dynamic analysis) focused to monitor the behavior of malware by executing it and comparing it with known malware behavior. Dynamic analysis has proved to be effective in malware detection as behavior is more difficult to mask while executing than its underlying code (static analysis). In this study, we propose a framework for Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique. The proposed framework extracts behavioral patterns from execution reports of malware in terms of its features and generates a data repository. Further, it selects the most promising features using information theory based concepts. Findings: The proposed framework detects the family of unknown malware samples after training of a classifier from malware data repository. We validated the applicability of the proposed framework by comparing with the other dynamic malware analysis technique on a real malware dataset from Virus Total. Application: The proposed framework is a Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique

A K-Means and Naive Bayes learning approach for better intrusion detection

Intrusion Detection Systems (IDS) have become an important building block of any sound defense network infrastructure. Malicious attacks have brought more adverse impacts on the networks than before, increasing the need for an effective approach to detect and identify such attacks more effectively. In this study two learning approaches, K-Means Clustering and Naïve Bayes classifier (KMNB) are used to perform intrusion detection. K-Means is used to identify groups of samples that behave similarly and dissimilarly such as malicious and non-malicious activity in the first stage while Naive Bayes is used in the second stage to classify all data into correct class category. Experiments were performed with KDD Cup '99 data sets. The experimental results show that KMNB significantly improved and increased the accuracy, detection rate and false alarm of single Naïve Bayes classifier up to 99.6, 99.8 and 0.5%

Investigating America online instant messaging application: data remnants on windows 8.1 client machine

Instant messaging applications (apps) are one potential source of evidence in a criminal investigation or a civil litigation. To ensure the most effective collection of evidence, it is vital for forensic practitioners to possess up-to-date knowledge about artifacts of forensic interest from various instant messaging apps. Hence, in this chapter we study America Online Instant Messenger (version 7.14.5.8) with the aims of contributing to an in-depth understanding of the types of terrestrial artifacts that are likely to remain after the use of instant messaging services and apps on Windows 8.1 devices. Potential artifacts identified during the research include data relating to the installation or uninstallation, log-in and log-off information, contact lists, conversations, and transferred files