National University of Computer and Emerging Sciences

About: National University of Computer and Emerging Sciences is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Computer science & The Internet. The organization has 1506 authors who have published 2438 publications receiving 26786 citations.

Solution of n-Queen problem using ACO

Abstract: In this paper, a solution is proposed for n-Queen problem based on ACO (Ant Colony Optimization) The n-Queen problem become intractable for large values of ‘n’ and thus placed in NP (Non-Deterministic Polynomial) class problem The n-Queen problem is basically a generalized form of 8-Queen problem In 8-Queen problem, the goal is to place 8 queens such that no queen can kill the other using standard chess queen moves So, in this paper, the proposed solution will be applied to 8-Queen problem The solution can very easily be extended to the generalized form of the problem for large values of ‘n’ The paper contains the detail discussion of problem background, problem complexity, Ant Colony Optimization (Swarm Intelligence) and a fair amount of experimental graphs

Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification Using SIFT and RootSIFT Descriptors

Abstract: Handwriting biometrics is the science of identifying the behavioral aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines scale-invariant feature transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMMs). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While an SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates an SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic, and one hybrid language), and the results have shown the superiority of the proposed system over the state-of-the-art techniques.

HADEC: Hadoop-based live DDoS detection framework

Abstract: Distributed denial of service (DDoS) flooding attacks are one of the main methods to destroy the availability of critical online services today. These DDoS attacks cannot be prevented ahead of time, and once in place, they overwhelm the victim with huge volume of traffic and render it incapable of performing normal communication or crashes it completely. Any delays in detecting the flooding attacks completely halts the network services. With the rapid increase of DDoS volume and frequency, the new generation of DDoS detection mechanisms are needed to deal with huge attack volume in reasonable and affordable response time. In this paper, we propose HADEC, a Hadoop-based live DDoS detection framework to tackle efficient analysis of flooding attacks by harnessing MapReduce and HDFS. We implemented a counter-based DDoS detection algorithm for four major flooding attacks (TCP-SYN, HTTP GET, UDP, and ICMP) in MapReduce, consisting of map and reduce functions. We deployed a testbed to evaluate the performance of HADEC framework for live DDoS detection on low-end commodity hardware. Based on the experiment, we showed that HADEC is capable of processing and detecting DDoS attacks in near to real time.

Application of evolutionary algorithms in detection of SIP based flooding attacks

Abstract: The Session Initiation Protocol (SIP) is the de facto standard for user's session control in the next generation Voice over Internet Protocol (VoIP) networks based on the IP Multimedia Subsystem (IMS) framework. In this paper, we first analyze the role of SIP based floods in the Denial of Service (DoS) attacks on the IMS. Afterwards, we present an online intrusion detection framework for detection of such attacks. We analyze the role of different evolutionary and non-evolutionary classifiers on the classification accuracy of the proposed framework. We have evaluated the performance of our intrusion detection framework on a traffic in which SIP floods of varying intensities are injected. The results of our study show that the evolutionary classifiers like sUpervised Classifier System (UCS) and Genetic clASSIfier sySTem (GAssist) can even detect low intensity SIP floods in realtime. Finally, we formulate a set of specific guidelines that can help VoIP service providers in customizing our intrusion detection framework by selecting an appropriate classifier-depending on their requirements in different service scenarios.

Error Analysis of the Written English Essays of Pakistani Undergraduate Students: A Case Study

Abstract: The study aims to examine the errors in a corpus of 50 English essays written by 50 participants (undergraduate Pakistani students). These participants are non native speakers of English language and hail from Intermediate background with weak English writing skills. The instrument used for the study is students‟ written essays in English language. I followed Rod Ellis‟s (1994) procedural analysis of errors; collection of sample of learner language, identification of errors, description of errors, explanation of errors, and evaluation of errors in analyzing 50 English essays. The occurrences of two types of errors; Interlanguage errors and mother tongue (MT) interference errors have been compared and the results show that the percentage of the occurrences of Interlanguage errors is higher than those of errors resulting from the interference of mother tongue (MT). The study has provided an insight into language learning problems which occur when L2 learners internalize the rules of target language (TL) in its production at a particular point resulting into errors in an unknown and a more natural way. These errors serve as a useful guide for English teachers to design an effective curriculum for teaching and learning of English as a second language.