International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Network-based Metabolic Characterization of Renal Cell Carcinoma

Abstract: An emerging hallmark of cancer is metabolic reprogramming, which presents opportunities for cancer diagnosis and treatment based on metabolism. We performed a comprehensive metabolic network analysis of major renal cell carcinoma (RCC) subtypes including clear cell, papillary and chromophobe by integrating transcriptomic data with the human genome-scale metabolic model to understand the coordination of metabolic pathways in cancer cells. We identified metabolic alterations of each subtype with respect to tumor-adjacent normal samples and compared them to understand the differences between subtypes. We found that genes of amino acid metabolism and redox homeostasis are significantly altered in RCC subtypes. Chromophobe showed metabolic divergence compared to other subtypes with upregulation of genes involved in glutamine anaplerosis and aspartate biosynthesis. A difference in transcriptional regulation involving HIF1A is observed between subtypes. We identified E2F1 and FOXM1 as other major transcriptional activators of metabolic genes in RCC. Further, the co-expression pattern of metabolic genes in each patient showed the variations in metabolism within RCC subtypes. We also found that co-expression modules of each subtype have tumor stage-specific behavior, which may have clinical implications.

Where the Wild Warnings Are: Root Causes of Chrome HTTPS Certificate Errors

Abstract: HTTPS error warnings are supposed to alert browser users to network attacks. Unfortunately, a wide range of non-attack circumstances trigger hundreds of millions of spurious browser warnings per month. Spurious warnings frustrate users, hinder the widespread adoption of HTTPS, and undermine trust in browser warnings. We investigate the root causes of HTTPS error warnings in the field, with the goal of resolving benign errors. We study a sample of over 300 million errors that Google Chrome users encountered in the course of normal browsing. After manually reviewing more than 2,000 error reports, we developed automated rules to classify the top causes of HTTPS error warnings. We are able to automatically diagnose the root causes of two-thirds of error reports. To our surprise, we find that more than half of errors are caused by client-side or network issues instead of server misconfigurations. Based on these findings, we implemented more actionable warnings and other browser changes to address client-side error causes. We further propose solutions for other classes of root causes.

Impact of PMU in dynamic state estimation of power systems

Abstract: Energy Management Systems (EMS) play the all important role of monitoring and control of the power systems and state estimation forms its backbone. In these days of increased operation of power system near to its limits and the tendency to utilize the grid to its full potential, monitoring and control with high level of accuracy becomes a boon. Dynamic state estimation (DSE) techniques with their unique ability to predict the state vector one time stamp ahead have the potential to foresee potential contingencies and security risks. Any improvement in its ability to predict or estimate would definitely go a long way in reducing the security risks in the modern day power system. One important factor affecting the quality of estimation is the measurement accuracy. Phasor Measurement Units (PMU) have revolutionized the way state estimation is being performed. Their unique ability to measure the voltage and current phasors (magnitude and phase angle) with very high accuracy makes them extremely useful in modern day energy management systems. This paper presents a PMU based DSE algorithm and studies the impact of PMUs on a dynamic state estimation technique. The paper explores the effect of the number of PMUs, their location, and the weightage given to their measurements on the accuracy of dynamic state estimation. With increasing usage of PMUs by the utilities, the PMU based dynamic state estimators will be of extreme importance in the modern day EMS.

A Secure Group-Based Blackhole Node Detection Scheme for Hierarchical Wireless Sensor Networks

Abstract: Rapid development of the wireless communication technology and low cost of sensing devices has accelerated the development of wireless sensor networks (WSNs). These types of networks have a wide range of applications including habitat monitoring, health monitoring, data acquisition in hazardous environmental conditions and military operations. Sensor nodes are resource constrained having limited communication range, battery and processing power. Sensor nodes are prone to failure and can be also physically captured by an adversary. One of the main concerns in WSNs is to provide security, especially in the cases where they are deployed for military applications and monitoring. Further, WSNs are prone to various attacks such as wormhole, sinkhole and blackhole attacks. A blackhole attack is a kind of denial of service attack, which is very difficult to detect and defend and such blackhole attack, if happens, affects the entire performance of the network. In addition, it causes high end-to-end delay and less throughput with less packet delivery ratio. The situation can be worst if multiple blackhole attacker nodes present in the network. As a result, detection and prevention of the blackhole attack becomes crucial in WSNs. In this paper, we aim to propose a new efficient group-based technique for the detection and prevention of multiple blackhole attacker nodes in WSNs. In our approach, the entire WSN is divided into several clusters and each cluster has a powerful high-end sensor node (called a cluster head), which is responsible for the detection of blackhole attacker nodes, if present, in that cluster. The proposed scheme achieves about 90 % detection rate and 3.75 % false positive rate, which are significantly better than the existing related schemes. Furthermore, our scheme is efficient and thus, it is very appropriate for practical applications in WSNs.

Off-policy Bandits with Deficient Support

Abstract: Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e.g. voice assistants, recommendation, search), since it enables the reuse of large amounts of log data. State-of-the-art methods for such off-policy learning, however, are based on inverse propensity score (IPS) weighting. A key theoretical requirement of IPS weighting is that the policy that logged the data has "full support", which typically translates into requiring non-zero probability for any action in any context. Unfortunately, many real-world systems produce support deficient data, especially when the action space is large, and we show how existing methods can fail catastrophically. To overcome this gap between theory and applications, we identify three approaches that provide various guarantees for IPS-based learning despite the inherent limitations of support-deficient data: restricting the action space, reward extrapolation, and restricting the policy space. We systematically analyze the statistical and computational properties of these three approaches, and we empirically evaluate their effectiveness. In addition to providing the first systematic analysis of support-deficiency in contextual-bandit learning, we conclude with recommendations that provide practical guidance.