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.

SDN-inspired, real-time botnet detection and flow-blocking at ISP and enterprise-level

Abstract: Infected machines pose threats to not only their users, but also their network owners (ISPs and enterprises). To neutralize the effect of these infected machines, common solutions span two ends of an architectural spectrum; either fully distributed solutions that are host-based, or completely centralized appliances at the network core. We present NetworkRadar, inspired by an SDN-enabled ISP framework, that operates in between these extremes and contains the benefits of both these approaches. We perform data-plane intensive event monitoring at aggregation points close to customers, and maintain a centralized control plane for correlating and high-granularity blocking of malicious bot activity. Here we present the architecture of our solution and evaluate a prototype deployment over an isolated slice of an ISP network, showing its viability due to a negligible (<1%) impact on customer throughput and its control plane scaling linearly to the customer base.

Time Synchronization in Photon-Limited Deep Space Optical Communications

Abstract: Random jitter or offset between the transmitter/receiver clocks is an important parameter that has to be accurately estimated for optimal detection of pulse position modulation (PPM) symbols for high-data-rate optical communications. This parameter, in general, is modeled as an unknown random quantity that depends on the clock drift between the transmitter/receiver clocks and the random motion between the transmitter and receiver stations. In this paper, we have modeled the time jitter for two scenarios—phase modulation jitter and frequency modulation jitter. The phase modulation jitter is modeled as a Gaussian random variable which is estimated with the help of a maximum a posteriori probability (MAP) estimator. The frequency modulation jitter is characterized as a random walk, and this leads to the modeling of the jitter as a state space variable in the context of a dynamical system. Since the observations are the photon counts in each slot of a PPM symbol (for both MAP estimation and tracking), the resulting dynamical model is highly nonlinear, and particle filters are employed for tracking the frequency modulation jitter. We evaluate the performance of both the maximum a posteriori estimators and the particle filters in terms of the relative mean-square error and probability of error. We conclude that with MAP estimation and particle filters that estimate/track the time offset, we achieve a significant performance gain in terms of probability of error as compared to systems that do not have a time synchronization system in place.

Dividend Policy and Earnings Management: An Empirical Study of Pakistani Listed Companies

Abstract: Dividend policy is one of the widely addressed topics in financial management. It is an important duty of a financial manager to formulate the company's dividend policy that is in the best interest of the company. Many a time financial managers are involved in earnings management practices with the intention of adjusting dividends. The present study has been carried out to scrutinize the effect of earnings management on dividend policy. The researchers have taken the data of 86 listed companies for the year 2004 to 2009. The researchers have measured the dividend policy by using dividend payout ratio while Modified Cross Sectional Jones Model (1995) has been employed to measure the earnings management. The results of the common effect model show that there is not any significant relationship among earnings management and dividend policy. Moreover, smaller companies are paying more dividends as compared to larger companies. This study reveals that involvement of managers is not for dividend policy. There might be some other motives behind the earnings management.

Mining fault tolerant frequent patterns using pattern growth approach

Abstract: Mining fault tolerant (FT) frequent patterns from transactional datasets are very complex than mining all frequent patterns (itemsets), in terms of both search space exploration and support counting of candidate FT-patterns. Previous studies on mining FT frequent patterns adopt Apriori-like candidate set generation- and-test approach, in which a number of dataset scans are needed to declare a candidate FT-pattern frequent. First for checking its FT-pattern support, and then for checking its individual items support present in its FT- pattern which depends on the cardinality of pattern. Inspired from the pattern growth technique for mining frequent itemsets, in this paper we present a novel algorithm for mining FT frequent patterns using pattern growth approach. Our algorithm stores the original transactional dataset in a highly condensed, much smaller data structure called FT-FP-tree, and the FT-pattern support and item support of all the FT- patterns are counting directly from the FT-FP-tree, without scanning the original dataset multiple times. While costly candidate set generations are avoided by generating conditional patterns from FT-FP-tree. Our extensive experiments on benchmark datasets suggest that, mining FT frequent patterns using our algorithm is highly efficient as compared to Apriori-like approach.