D. Venugopal

The increase in functionality and service offerings of mobile devices and mobile networks has prompted the need to offer better security for these devices. Expand

In this paper, we propose a new model for event extraction that combines the power of MLNs and SVMs, dwarfing their limitations. Expand

In this paper, we propose principled weight learning algorithms for Markov logic networks that can easily scale to much larger datasets and application domains than existing algorithms. Expand

We propose an approach for constructing the clusters and show how it can be used to trade accuracy with computational complexity in a principled manner. Expand

Lifted inference algorithms take advantage of symmetries in first-order probabilistic logic representations such as Markov logic networks and are naturally more scalable than propositional inference algorithms which ground the MLN. Expand

We propose a novel hierarchical supervised-learning approach to increase the likelihood of detecting anomalies by analyzing several user features and then characterizing their collective behavior in a unified manner. Expand

The main computational bottleneck in various sampling based and local-search based inference algorithms for Markov logic networks (e.g., Gibbs sampling, MC-SAT, MaxWalksat, etc.) is computing the number of groundings of a first-order formula that are true given a truth assignment to all of its ground atoms. Expand

In this paper, we describe a system for detecting malware within the network traffic using malware signatures. Expand

In this paper, we investigate combining blocking and collapsing - two widely used strategies for improving the accuracy of Gibbs sampling - in the context of probabilistic graphical models. Expand

We present a comprehensive analysis of some existing machine learning classifiers regarding identifying intrusions in network traffic and summarize their effectiveness using a detailed experimental evaluation. Expand