Safia Abbas

A comparative study between modern encryption algorithms based on cloud computing environment

The concept of “cloud computing” is not new, it is undisputable that they have proven a major commercial success over recent times. Cloud computing is the delivery of computing services by shared resources, software and information over the internet (public) or intranet (private). The improvement of cloud technology is reinforced by the improvement of the security concerns, accordingly various encryption algorithms have been developed. This paper provides a comparative study that represents the differences between modern encryption algorithms in cloud computing. The study encompasses the key size, the performance and the size of the output encrypted file based two different categorizes of algorithms (symmetric and asymmetric).

Argument mining based on a structured database and its usage in an intelligent tutoring environment

Argumentation theory is a new research area that concerns mainly with reaching a mutually acceptable conclusion using logical reasoning. Argumentation can be defined as a proof of dynamic nature and is considered as an ill-defined domain that typically lacks clear distinctions between “right” and “wrong” answers. Instead, there are often competing reasonable answers. Recently, a number of argument mapping tools have been developed to diagram, articulate, and comprehend different arguments. Despite the fact, these methods are of complementary nature, and the efforts for integrating these tools are missing. The purpose of this paper is threefold: (1) revealing a novel approach for argument representation using a structured relational argument database “RADB”, which has been designed, developed, and implemented in order to represent different argument analyses and diagrams, (2) presenting a classifier agent that utilizes the RADB repository by using different mining techniques in order to retrieve the most relevant arguments to the subject of search, and (3) proposing an agent-based educational environment (ALES) that utilizes the RABD together with the classifier agent to teach argument analysis.

Advanced encryption standard development based quantum key distribution

This paper presents a development of the advanced encryption standard (AES) algorithm, which is considered as the most important symmetric encryption algorithm. The development focuses on the generation of dynamic quantum S-Boxes (DQS-Boxes) based quantum cipher key, instead of the ordinary used static S-Boxes. Moreover, the paper introduces the integration between the developed AES based DQS-Boxes, and the specific selected secret key generated from the QKD using two different modes (online and off-line).

Statistical analysis for random bits generation on quantum key distribution

Recently, Quantum cryptography researchers utilize the quantum keys, in order to provide a more trusted environment for both key distribution and management processes. The quantum keys are generated based on quantum mechanics phenomena. However, all events for the quantum key generation rely on exchanging photons between parties over limited distances. So, in this paper, random tests algorithms, such as NIST and DIEHARD, are implemented to test and evaluate the randomness rates for quantum keys generation. After then, the initialized vector, which is the seed of the symmetric encryption algorithms, is established based on specific analysis to be a key for the algorithms. The paper utilizes the (BB84) quantum key distribution (QKD) protocol based on two different innovated modes, the raw and privacy modes.

A New Trend of Pseudo Random Number Generation using QKD

Random Numbers determine the security level of cryptographic applications as they are used to generate padding schemes in the encryption/decryption process as well as used to generate cryptographic keys. This paper utilizes the QKD to generate a random quantum bit rely on BB84 protocol, using the NIST and DIEHARD randomness test algorithms to test and evaluate the randomness rates for key generation. The results show that the bits generated using QKD are truly random, which in turn, overcomes the distance limitation (associated with QKD) issue, its well-known challenges with the sending/ receiving data process between different communication parties

Automatic Arabic Ontology Construction Framework: An Insectivore's Animal Case Study.

Arabic is the official language of millions of people in the Middle East and northern African countries. Arabic ontology is the foundation of the formulation of Arabic Semantic Web. Surprisingly, little has been done in the field of computerized language and lexical resources. This paper introduces a framework that generates an Arabic Ontology from a semi-structured data (XML documents associated with graph schema), in which, XML schema is created and utilized in the graph schema development (XSG). Finally, the paper provides a case study, insectivore’s case, where the developed Arabic ontology is applied. The results consist of 143 words, 10 Concepts, 10 elements and 20 relations. The generated ontology is evaluated using data-driven evaluation method and tree based mining. 65% of the source XML documents have been included in the insectivore’s case study. This result can be refined more to reach satisfying results.

Securing Cloud Computing Environment using a New Trend of Cryptography

Cloud computing is an internet-based computing, where shared resources, software, and information are provided with consumers on-demand. They guarantee a way to share distributed resources and services that belong to different organizations. In order to build secure cloud environment, data security and cryptography must be assured to share data through distributed environment. So, this paper provides more flexibility and secured communication environment by deploying a new cryptographic service. This service entails both Quantum Key Distribution (QKD) and enhanced version of Advanced Encryption Standard (AES). Moreover, this service solves the key distribution and key management problems in cloud environment which emerged through the two implemented modes, on-line and off-line modes.

Deposit subscribe Prediction using Data Mining Techniques based Real Marketing Dataset

Recently, economic depression, which scoured all over the world, affects business organizations and banking sectors. Such economic pose causes a severe attrition for banks and customer retention becomes impossible. Accordingly, marketing managers are in need to increase marketing campaigns, whereas organizations evade both expenses and business expansion. In order to solve such riddle, data mining techniques is used as an uttermost factor in data analysis, data summarizations, hidden pattern discovery, and data interpretation. In this paper, rough set theory and decision tree mining techniques have been implemented, using a real marketing data obtained from Portuguese marketing campaign related to bank deposit subscription [Moro et al., 2011]. The paper aims to improve the efficiency of the marketing campaigns and helping the decision makers by reducing the number of features, that describes the dataset and spotting on the most significant ones, and predict the deposit customer retention criteria based on potential predictive rules.

Learning cross-domain social knowledge from cognitive scripts

Creating artificial intelligence (AI) agents with computational models based on human cognitive abilities is an ongoing research area. This paper proposes a new evolutionary cognitive model for cross-domain learning, which aims to improve the cognitive learning process by extracting new experienced knowledge from pre-existing socio-cultural cognitive scripts. This knowledge is necessary for the Al agents to develop learning for the current faced social situation (Target). The model depends basically on two phases; the retrieval phase and the learning phase. In the retrieval phase, Pharaoh algorithm is utilized to retrieve the most relevant cognitive script (Base) to the target script considering the context. Whereas, the learning phase employs evolutional processes to enrich the target script. Finally, the enriched script replaces the target script in the evolved script-base in order to be used in the learning and retrieval phases again.