Ali I. Eldesoky

A Low-Storage Precise IP Traceback Technique Based on Packet Marking and Logging

IP traceback techniques are used to defend against DDoS attacks, and two of the most preferred techniques are packet marking and packet logging. Marking techniques differentiate between attacks and legitimate packets incur little computation overhead while requiring a large flow of packets to build the entire attack path. Logging techniques trace even a single packet; routers record the packet digests locally, so it is considered as a more powerful technique. However, routers require a high storage space for storing packet digests that represents a vital problem in deploying it practically. In this paper, a new hybrid IP traceback framework is presented. This framework is based on both marking and logging techniques. In the marking algorithm, every router is assigned a 12-bit-length ID number; it helps in deploying pushback method to permit legitimate traffic flow smoothly. In the packet logging technique, a logging ratio is managed by changing a value k specified in the traceback system. According to the mathematical analysis and experimental results, >50% of the storage space of routers could be saved as a result of choosing a proper k value.

AN EFFICIENT OBJECT ORIENTED TEXT ANALYSIS (OOTA) APPROACH TO CONSTRUCT STATIC STRUCTURE WITH DYNAMIC BEHAVIOR

In many fields of science, IT applications and business environments successfully evolved systems to receive vast amount of electronic data and information. Due to increasing electronic data and information, most recent researches have tried to find a solution to resolve the crisis of information overload. These solutions include a combination of techniques of data mining, machine learning, natural language processing and information retrieval, information extraction, and knowledge management. A great challenge is how to exploit those information and knowledge resources and turn them into useful knowledge available to concerned people. The value of knowledge increases when people can share and capitalize on it. Thus, approaches that can help researchers to benefit from existing hidden knowledge are needed. For this, tools that can analyze, extract and explore relevant and useful information with relations are required. So, the main contribution of this paper is to integrate the technology of XML with text analysis for introducing an efficient concept-based structure model, where this model can represent the text in a form that can be easily understood, shared, managed and mined. This paper describes an efficient object oriented text analysis (OOTA) approach by generating an object oriented model that transforms unstructured text to a specific structured form and stored in XML format. The experimental results show that this approach has a good promotion on results.

Distributing Graphic Rendering using Grid Computing with Load Balancing

Graphic rendering is expensive in terms of computation. We investigate distributing it by applying the powerful computing technique called grid computing, and showing how this technology has a great effectiveness and high performance. The paper shows how to develop a java drawing framework for drawing in the distributed environment by dividing the work upon nodes in grid computing and selecting the best nodes for job assignments to have the jobs executed in the least amount of time. Schedulers are limited in individual capability, but when deployed in large numbers can represent a strong force similar to a colony of ants or swarm of bees. The paper also presents a mechanism for load balancing based on swarm intelligence such as Ant colony optimization and Particle swarm Optimization. General Terms Graphic Rendering, Grid Computing, Load Balancing.

Exploiting semantic annotations and Q-learning for constructing an efficient hierarchy/graph texts organization.

Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation and Q-learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach.

An Efficient Approach to Construct Object Model of Static Textual Structure with Dynamic Behavior Based on Q-learning

Developing information technology led to raise the intricacy of information systems intensive, hence techniques with effectiveness and efficiency are required. These techniques are used to support users in using the information for rapid and correct decision-making. Conventional text mining and managing systems mainly use the presence or absence of keywords to discover and analyze useful information from textual documents. However, simple word counting and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper has been primarily concerned with constructing text representation model and exploiting that in mining and managing operations such as gathering, searching, filtering, retrieving, extracting, clustering, classifying, and summarizing. This representation model is based on semantic notions to represent text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. This model reflects the existing relations among concepts and facilitates accurate relatedness measurements that result in better mining performance. The experimental evaluations were carried out on real datasets from various domains, showing the importance of the proposed model.

A Bidder Behavior Learning Intelligent System for Trust Measurement

With the spread of e-commerce fields over the world especially an online auctions segment, the problems of internet crimes increased in the last years. The online auctions face a serious problem of trust among the participants where users have no information about the others, and have no relations among them except the commercial transactions; this allows fraud to occur by malicious users. We proposed a framework depend on “Bidder Behavior” consist of three algorithms the first “ Bidder Increase Price Behavior”(BIPB), the second “Search In Stored Data Base”(SSDB) and the third “Person Recursive Search” (PRS) to improve the performance of trust measurement

An Efficient Methodology for Exploring Valuable Knowledge with New Evaluation for Knowledge Quality

The recent trend has been using hybrid approach rather than using a single intelligent technique to solve the problems. Meanwhile in an information technology era, knowledge is always changing, hence the flexibility in approach and in thinking is a must. Since, the on-going conversation about semantic knowledge (much information with many relations) is much more important than coming up with the right answer (information), for that performing some inferences is needed for exploring creative diverse information. Where the knowledge is an appropriate collection of actionable information. So by combining much information with many relations, more novel and useful information are found as knowledge. Since the demand for Business Intelligence (BI) applications continues to grow even at a time when the demand for most Information Technology (IT) products is soft. Business intelligence systems provide actionable information delivered at the right time, at the right location, and in the right form to assist decision makers. This paper introduces an efficient methodology for storing the explored creative information/knowledge with many relations as a graph structure with a new evaluation of the creative information/knowledge quality. That through the implementation of Oriented Directed Acyclic Info Graph (ODAIG) Algorithm to generate the actionable information map. Moreover, Creative Information Quality (CIQ) Algorithm to evaluate the creative information quality. Different experiment done shows that, this methodology has a good promotion on results.

A novel ideation causal map with a new evaluation for Ideas Quality

In an information technology era, knowledge is always changing, hence the flexibility in approach and in thinking is a must. Since, the on-going conversation about semantic knowledge (many ideas with many relations) is much more important than coming up with the right answer (idea), so performing some inferences is needed for an exploration of creative diverse ideas. By combining many ideas with many relations, more novel and useful ideas are found. Representing such combination requires a special structure (called idea map). This paper introduces an efficient ideas map as a graph structure for diverse ideas and their relationships, called Oriented Directed Acyclic Graph (ODAG) algorithm. Moreover, formulating a new evaluation of the creative ideas, called Creative Ideas Quality (CIQ) algorithm by allowing agents to link diverse ideas dynamically. These diverse ideas are generated from an intelligent inference mechanism, which based on the principles of idea associations; similarity, contrast and contiguity.