Showing papers by "Muddassar Farooq published in 2012"

An Information-Preserving Watermarking Scheme for Right Protection of EMR Systems

Abstract: Recently, a significant amount of interest has been developed in motivating physicians to use e-health technology (especially Electronic Medical Records (EMR) systems). An important utility of such EMR systems is: a next generation of Clinical Decision Support Systems (CDSS) will extract knowledge from these electronic medical records to enable physicians to do accurate and effective diagnosis. It is anticipated that in future such medical records will be shared through cloud among different physicians to improve the quality of health care. Therefore, right protection of medical records is important to protect their ownership once they are shared with third parties. Watermarking is a proven well-known technique to achieve this objective. The challenges associated with watermarking of EMR systems are: 1) some fields in EMR are more relevant in the diagnosis process; as a result, small variations in them could change the diagnosis, and 2) a misdiagnosis might not only result in a life threatening scenario but also might lead to significant costs of the treatment for the patients. The major contribution of this paper is an information-preserving watermarking scheme to address the above-mentioned challenges. We model the watermarking process as a constrained optimization problem. We demonstrate, through experiments, that our scheme not only preserves the diagnosis accuracy but is also resilient to well-known attacks for corrupting the watermark. Last but not least, we also compare our scheme with a well-known threshold-based scheme to evaluate relative merits of a classifier. Our pilot studies reveal that-using proposed information-preserving scheme-the overall classification accuracy is never degraded by more than 1 percent. In comparison, the diagnosis accuracy, using the threshold-based technique, is degraded by more than 18 percent in a worst case scenario.

ELF-Miner: using structural knowledge and data mining methods to detect new (Linux) malicious executables

Abstract: Linux malware can pose a significant threat—its (Linux) penetration is exponentially increasing—because little is known or understood about Linux OS vulnerabilities. We believe that now is the right time to devise non-signature based zero-day (previously unknown) malware detection strategies before Linux intruders take us by surprise. Therefore, in this paper, we first do a forensic analysis of Linux executable and linkable format (ELF) files. Our forensic analysis provides insight into different features that have the potential to discriminate malicious executables from benign ones. As a result, we can select a features’ set of 383 features that are extracted from an ELF headers. We quantify the classification potential of features using information gain and then remove redundant features by employing preprocessing filters. Finally, we do an extensive evaluation among classical rule-based machine learning classifiers—RIPPER, PART, C4.5 Rules, and decision tree J48—and bio-inspired classifiers—cAnt Miner, UCS, XCS, and GAssist—to select the best classifier for our system. We have evaluated our approach on an available collection of 709 Linux malware samples from vx heavens and offensive computing. Our experiments show that ELF-Miner provides more than 99% detection accuracy with less than 0.1% false alarm rate.

Laminar flow of couple stress fluids for Vogels model

Abstract: The coupled nonlinear equations for heat transfer flow of variable viscosity couple stress fluids between two parallel plates are derived for four different problems, namely plane Couette flow, plug flow, plane Poiseuille flow and generalized plane Couette flow. These equations are made dimensionless with the help of non-dimensional parameters and solved by using regular perturbation technique. The effect of various emerging parameters embedded in the problem is discussed graphically. Key words: Couple stress fluids, vogel's viscosity model, perturbation technique, heat transfer.

Multi-level distance labelings for generalized gear graph

Abstract: The \emph{radio number} of $G$, $rn(G)$, is the minimum possible span. Let $d(u,v)$ denote the \emph{distance} between two distinct vertices of a connected graph $G$ and $diam(G)$ be the \emph{diameter} of $G$. A \emph{radio labeling} $f$ of $G$ is an assignment of positive integers to the vertices of $G$ satisfying $d(u,v)+|f(u)-f(v)|\geq diam(G)+1$. The largest integer in the range of the labeling is its span. In this paper we show that $rn(J_{t,n})\geq\left\{ \begin{array}{ll} \frac{1}{2}(nt^{2}+2nt+2n+4), & \hbox{when t is even;} \\ \frac{1}{2}(nt^{2}+4nt+3n+4), & \hbox{when t is odd.} \end{array} \right.$\\ oindent Further the exact value for the radio number of $J_{2,n}$ is calculated.