Boosted Hidden Markov Models for Malware Detection

Digital security is an important issue today, and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has found widespread application in the field of pattern matching and malware detection is hidden Markov models (HMMs). Since HMM training is a hill climb technique, we can often significantly improve a model by training multiple times with different initial values. In this research, we compare boosted HMMs (using AdaBoost) to HMMs trained with multiple random restarts, in the context of malware detection. These techniques are applied to a variety of challenging malware datasets and we analyze the results in terms of effectiveness and efficiency

Hidden Markov Models with Random Restarts vs Boosting for Malware Detection

Effective and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has been used widely in the field of pattern matching in general-and malware detection in particular-is hidden Markov models (HMMs). HMM training is based on a hill climb, and hence we can often improve a model by training multiple times with different initial values. In this research, we compare boosted HMMs (using AdaBoost) to HMMs trained with multiple random restarts, in the context of malware detection. These techniques are applied to a variety of challenging malware datasets. We find that random restarts perform surprisingly well in comparison to boosting. Only in the most difficult "cold start" cases (where training data is severely limited) does boosting appear to offer sufficient improvement to justify its higher computational cost in the scoring phase

Trajectory Aware Macro-cell Planning for Mobile Users

We design and evaluate algorithms for efficient user-mobility driven macro-cell planning in cellular networks. As cellular networks embrace heterogeneous technologies (including long range 3G/4G and short range WiFi, Femto-cells, etc.), most traffic generated by static users gets absorbed by the short-range technologies, thereby increasingly leaving mobile user traffic to macro-cells. To this end, we consider a novel approach that factors in the trajectories of mobile users as well as the impact of city geographies and their associated road networks for macro-cell planning. Given a budget k of base-stations that can be upgraded, our approach selects a deployment that impacts the most number of user trajectories. The generic formulation incorporates the notion of quality of service of a user trajectory as a parameter to allow different application-specific requirements, and operator choices.We show that the proposed trajectory utility maximization problem is NP-hard, and design multiple heuristics. We evaluate our algorithms with real and synthetic data sets emulating different city geographies to demonstrate their efficacy. For instance, with an upgrade budget k of 20%, our algorithms perform 3-8 times better in improving the user quality of service on trajectories in different city geographies when compared to greedy location-based base-station upgrades.Comment: Published in INFOCOM 201

Boragaon: Amid Guwahati's Waste is a Neglected Ecosystem of People and Animals

Waste is often considered a great nuisance – but it can also be a source of livelihoods and sustenance in its own right. A big thorn in Guwahati’s side is its municipal waste, and the public dialogue over its Boragaon landfill is currently caught between urban development and ecological and public health. Yet there exists a third group with its own interests that both civil society and policymakers need to acknowledge and plan for

Disorder-enhanced phase coherence in trapped bosons on optical lattices

The consequences of disorder on interacting bosons trapped in optical lattices are investigated by quantum Monte Carlo simulations. At small to moderate strengths of potential disorder a unique effect is observed: if there is a Mott plateau at the center of the trap in the clean limit, phase coherence {\it increases} as a result of disorder. The localization effects due to correlation and disorder compete against each other, resulting in a partial delocalization of the particles in the Mott region, which in turn leads to increased phase coherence. In the absence of a Mott plateau, this effect is absent. A detailed analysis of the uniform system without a trap shows that the disordered states participate in a Bose glass phase.Comment: 4 pages, 4 figure

TPX: Biomedical literature search made easy

TPX is a web-based PubMed search enhancement tool that enables faster article searching using an alysis and exploration features . These features include identification of relevant biomedical concepts from search results with linkouts to source databases, concept based article categorization, concept assisted search and filtering, query refinement. A distinguishing feature here is the ability to add user-defined concept names and/or concept types for named entity recognition. The tool allows contextual exploration of knowledge sources by providing concept association maps derived from the MEDLINE repository. It also has a full-text search mode that can be configured on request to access local text repositories, incorporating entity co-occurrence search at sentence/paragraph levels. Local text files can also be analyzed on-the-fly

Superconductivity in Compositionally-Complex Cuprates with the YBa2_2Cu3_3O7−x_{7-x} Structure

High-temperature superconductivity is reported in a series of compositionally-complex cuprates with varying degrees of size and spin disorder. Three compositions of Y-site alloyed YBa$_2$Cu$_3$O$_{7-x}$, i.e., (5Y)BCO, were prepared using solid-state methods with different sets of rare earth ions on the Y-site. Synchrotron X-ray diffraction and energy-dispersive X-ray spectroscopy confirm these samples have high phase-purity and homogeneous mixing of the Y-site elements. The superconducting phase transition was probed using electrical resistivity and AC magnetometry measurements, which reveal the transition temperature, T$_C$, is greater than 91 K for all series when near optimal oxygen doping. Importantly, these T$_C$ values are only $\approx$1$\%$ suppressed relative to pure YBCO (T$_C$ = 93 K). This result highlights the robustness of pairing in the YBCO structure to specific types of disorder. In addition, the chemical flexibility of compositionally-complex cuprates allows spin and lattice disorder to be decoupled to a degree not previously possible in high-temperature superconductors. This feature makes compositionally-complex cuprates a uniquely well-suited materials platform for studying proposed pairing interactions in cuprates.Comment: 6 pages, 3 figures, 1 tabl