Polymorphism and danger susceptibility of system call DASTONs

We have proposed a metaphor “DAnger Susceptible daTa codON� (DASTON) in data subject to processing by Danger Theory (DT) based Artificial Immune System (DAIS). The DASTONs are data chunks or data point sets that actively take part to produce “danger�; here we abstract “danger� as required outcome. To have closer look to the metaphor, this paper furthers biological abstractions for DASTON. Susceptibility of DASTON is important parameter for generating dangerous outcome. In biology, susceptibility of a host to pathogenic activities (potentially dangerous activities) is related to polymorphism. Interestingly, results of experiments conducted for system call DASTONs are in close accordance to biological theory of polymorphism and susceptibility. This shows that computational data (system calls in this case) exhibit biological properties when processed with DT point of view

A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

Real valued negative selection for anomaly detection in wireless ad hoc networks

Wireless ad hoc network is one of the network technologies that have gained lots of attention from computer scientists for the future telecommunication applications. However it has inherits the major vulnerabilities from its ancestor (i.e., the fixed wired networks) but cannot inherit all the conventional intrusion detection capabilities due to its features and characteristics. Wireless ad hoc network has the potential to become the de facto standard for future wireless networking because of its open medium and dynamic features. Non-infrastructure network such as wireless ad hoc networks are expected to become an important part of 4G architecture in the future. In this paper, we study the use of an Artificial Immune System (AIS) as anomaly detector in a wireless ad hoc network. The main goal of our research is to build a system that can learn and detect new and unknown attacks. To achieve our goal, we studied how the real-valued negative selection algorithm can be applied in wireless ad hoc network network and finally we proposed the enhancements to real-valued negative selection algorithm for anomaly detection in wireless ad hoc network

Spectral properties from Matsubara Green's function approach - application to molecules

We present results for many-body perturbation theory for the one-body Green's function at finite temperatures using the Matsubara formalism. Our method relies on the accurate representation of the single-particle states in standard Gaussian basis sets, allowing to efficiently compute, among other observables, quasiparticle energies and Dyson orbitals of atoms and molecules. In particular, we challenge the second-order treatment of the Coulomb interaction by benchmarking its accuracy for a well-established test set of small molecules, which includes also systems where the usual Hartree-Fock treatment encounters difficulties. We discuss different schemes how to extract quasiparticle properties and assess their range of applicability. With an accurate solution and compact representation, our method is an ideal starting point to study electron dynamics in time-resolved experiments by the propagation of the Kadanoff-Baym equations.Comment: 12 pages, 8 figure

Solving time gap problems through the optimization of detecting stepping stone algorithm

This paper describes an analysis of detecting stepping stone algorithm to defeat the time gap problem. It is found that current algorithm of detecting stepping stone is not optimized. Several weaknesses are identified and suggestions are proposed to overcome this problem. The suggestions are applied in the improved algorithm. Since the detecting stepping stone is listed as one of the response technique, it is suggested that the improved algorithm should be used as a remedial to the time gap problem

An improved discrete cosine transformation block based scheme for copy-move image forgery detection

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF

Komputer dari logik ke seni bina

Buku ini sesuai digunakan untuk kursus asas seni bina komputer. Ia meliputi topik seni bina komputer bermula daripada teknologi komputer kepada perisian sistem dan komunikasi. Ia mengambil pendekatan bawah-atas kepada subjek, bermula pada peringkat terendah, logik, dan membina seni bina perkakasan dan perisian komputer daripada asas ini. Buku ini mengandungi 14 bab. Bab 1 memperkenalkan beberapa konsep yang penting dalam memahami seni bina komputer. Bab 2 hingga 4 adalah berkaitan dengan konsep yang diperlukan untuk memahami logik dan reka bentuk logik. Bab 5 hingga Bab 11 membincangkan dengan terperinci elemen-elemen dan reka bentuk sistem komputer serta pengendalian data di dalam komputer. Bab 12 merupakan pengenalan kepada perisian sistem. Bab 13 pula merupakan pengenalan kepada komunikasi data dan pengkomputan teragih. Bab terakhir menerangkan beberapa pendekatan baru untuk mereka bentuk sistem komputer

Efficient image block matching algorithm with two layer feature extraction

Image block matching is the main step of duplicated region detection for exploring copy-paste image forgery. High computational time in this step is one of the most important problems to find similar regions. In this paper we propose an efficient image block matching algorithm based on two layer feature extraction in order to improve time complexity. Furthermore, we determine performance of proposed algorithm based on time complexity function. The experimental results and mathematical analysis demonstrate that two layer matching can be more time-efficient than previous common methods such as lexicographically sorting

Defining Generic Attributes for IDS Classification

Detection accuracy of Intrusion Detection System (IDS) depends on classifying network traffic based on data features. Using all features for classification consumes more computation time and computer resources. Some of these features may be redundant and irrelevant therefore, they affect the detection of traffic anomalies and the overall performance of the IDS. The literature proposed different algorithms and techniques to define the most relevant sets of features of KDD cup 1999 that can achieve high detection accuracy and maintain the same performance as the total data features. However, all these algorithms and techniques did not produce optimal solutions even when they utilized same datasets. In this paper, a new approach is proposed to analyze the researches that have been conducted on KDD cup 1999 for features selection to define the possibility of determining effective generic features of the common dataset KDD cup 1999 for constructing an efficient classification model. The approach does not rely on algorithms, which shortens the computational cost and reduces the computer resources. The essence of the approach is based on selecting the most frequent features of each class and all classes in all researches, then a threshold is used to define the most significant generic features. The results revealed two sets of features containing 7 and 8 features. The classification accuracy by using eight features is almost the same as using all dataset features

Text content analysis for illicit web pages by using neural networks

Illicit web contents such as pornography, violence, and gambling have greatly polluted the mind of web users especially children and teenagers. Due to the ineffectiveness of some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking against today's dynamic web contents, content based analysis techniques with effective model are highly desired. In this paper, we have proposed a textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages. We have examined the entropy scheme with two other common term weighting schemes that are TFIDF and Glasgow. Those techniques have been tested with artificial neural network using small class dataset. In this study, we found that our proposed model has achieved better performance in terms accuracy, convergence speed, and stability compared to the other techniques