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.

Malicious AODV: Implementation and Analysis of Routing Attacks in MANETs

Abstract: From the security perspective Mobile Ad hoc Networks (MANETs) are amongst the most challenging research areas and one of the key reasons for this is the ambiguous nature of insider attacks in these networks. In recent years, many attempts have been made to study the intrinsic attributes of these insider attacks but the focus has generally been on the analysis of one or very few particular attacks, or only the survey of various attacks without any performance analysis. Therefore, a major feature that research has lately lacked is a detailed and comprehensive study of the effects of various insider attacks on the overall performance of MANETs. In this paper we investigate, in detail, some of the most severe attacks against MANETs namely the blackhole attack, sinkhole attack, selfish node behavior, RREQ flood, hello flood, and selective forwarding attack. A detailed NS-2 implementation of launching these attacks successfully using Ad hoc On-Demand Distance Vector (AODV) routing protocol has been presented and a comprehensive and comparative analysis of these attacks is performed. We use packet efficiency, routing overhead, and throughput as our performance metrics. Our simulationbased study shows that flooding attacks like RREQ flood and hello flood drastically increase the routing overhead of the protocol. Route modification attacks such as sinkhole and blackhole are deadly and severely affect the packet efficiency and bring down the throughput to unacceptable ranges.

IMAD: in-execution malware analysis and detection

Abstract: The sophistication of computer malware is becoming a serious threat to the information technology infrastructure, which is the backbone of modern e-commerce systems. We, therefore, advocate the need for developing sophisticated, efficient, and accurate malware classification techniques that can detect a malware on the first day of its launch -- commonly known as "zero-day malware detection". To this end, we present a new technique, IMAD, that can not only identify zero-day malware without any apriori knowledge but can also detect a malicious process while it is executing (in-execution detection). The capability of in-execution malware detection empowers an operating system to immediately kill it before it can cause any significant damage. IMAD is a realtime, dynamic, efficient, in-execution zero-day malware detection scheme, which analyzes the system call sequence of a process to classify it as malicious or benign. We use Genetic Algorithm to optimize system parameters of our scheme. The evolutionary algorithm is evaluated on real world synthetic data extracted from a Linux system. The results of our experiments show that IMAD achieves more than 90% accuracy in classifying in-execution processes as benign or malicious. Moreover, our scheme can classify approximately 50% of malicious processes within first 20% of their system calls.

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

Abstract: Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.