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.

cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing

Abstract: Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.

Psogp: a genetic programming based adaptable evolutionary hybrid particle sw arm optimization

Abstract: In this study we describe a method for extending particle swarm optimiza- tion. We have presented a novel approach for avoiding premature convergence to local minima by the introduction of diversity in the swarm. The swarm is made more diverse and is encouraged to explore by employing a mechanism which allows each particle to use a different equation to update its velocity. This equation is also continuously evolved through the use of genetic programming to ensure adaptability. We compare two vari- ations of our algorithm, one utilizing random initialization while in the second one we utilize partial non-random initalization which forces some particles to use the standard PSO velocity update equation. Results from experimentation suggest that the modied PSO with complete random initialization shows promise and has potential for improve- ment. It is particularly very good atnding the exact optimum.

Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

Abstract: Healthcare system facilitates the treatment of patients with the support of wearable, smart, and handheld devices, as well as many other devices These devices are producing a huge bulk of data that need to be moulded for extracting meaningful insights from them for the useful use of researchers and practitioners Various approaches, methods, and tools are in use for doing so and to extract meaningful information in the field of healthcare This information is being used as evidence to further analyze the data for the early care of patient and to devise treatment Early care and treatment can facilitate healthcare and the treatment of the patient and can have immense potentiality of dropping the care cost and quality refining of care and can decrease waste and chances of error To facilitate healthcare in general and cardiology in specific, the proposed study presents an overview of the available literature associated with big data, its insights, and analytics The presented report will help practitioners and researchers to devise new solutions for early care in healthcare and in cardiology

S-box design based on optimize LFT parameter selection: a practical approach in recommendation system domain

Abstract: The strength of cryptography encryption is strongly depended upon the substitution box (S-box), hence highly non-linear S-box is appreciated in cryptography. In this work, we proposed a scheme to select the optimized values for control parameters (a, b, c, d) of linear fractional transform (LFT) to achieve an S-box with non-linearity of 112. For dynamic parameter values, we used location of chaotic sequences. The proposed s-box tested on different criteria such as non-linearity method (NL), probability methods (linear approximation (LP), differential approximation (DP), strictly avalanche criteria (SAC) and bit independence criteria (BIC). Our result shows that proposed s-box achieves better or equal to cryptographic strength as compared with state-of-the-art techniques. Moreover, we tested the proposed s-box based encryption method in recommendation system scenario with the idea of text-to-image conversion. The statistical analysis of this image encryption shows the bright prospects of the proposed method.