Studying Ransomware Attacks Using Web Search Logs

Cyber attacks are increasingly becoming prevalent and causing significant damage to individuals, businesses and even countries. In particular, ransomware attacks have grown significantly over the last decade. We do the first study on mining insights about ransomware attacks by analyzing query logs from Bing web search engine. We first extract ransomware related queries and then build a machine learning model to identify queries where users are seeking support for ransomware attacks. We show that user search behavior and characteristics are correlated with ransomware attacks. We also analyse trends in the temporal and geographical space and validate our findings against publicly available information. Lastly, we do a case study on 'Nemty', a popular ransomware, to show that it is possible to derive accurate insights about cyber attacks by query log analysis.Comment: To appear in the proceedings of SIGIR 202

Fast, Scalable, and Accurate Algorithms for Time-Series Analysis

Time is a critical element for the understanding of natural processes (e.g., earthquakes and weather) or human-made artifacts (e.g., stock market and speech signals). The analysis of time series, the result of sequentially collecting observations of such processes and artifacts, is becoming increasingly prevalent across scientific and industrial applications. The extraction of non-trivial features (e.g., patterns, correlations, and trends) in time series is a critical step for devising effective time-series mining methods for real-world problems and the subject of active research for decades. In this dissertation, we address this fundamental problem by studying and presenting computational methods for efficient unsupervised learning of robust feature representations from time series. Our objective is to (i) simplify and unify the design of scalable and accurate time-series mining algorithms; and (ii) provide a set of readily available tools for effective time-series analysis. We focus on applications operating solely over time-series collections and on applications where the analysis of time series complements the analysis of other types of data, such as text and graphs. For applications operating solely over time-series collections, we propose a generic computational framework, GRAIL, to learn low-dimensional representations that natively preserve the invariances offered by a given time-series comparison method. GRAIL represents a departure from classic approaches in the time-series literature where representation methods are agnostic to the similarity function used in subsequent learning processes. GRAIL relies on the attractive idea that once we construct the data-to-data similarity matrix most time-series mining tasks can be trivially solved. To overcome scalability issues associated with approaches relying on such matrices, GRAIL exploits time-series clustering to construct a small set of landmark time series and learns representations to reduce the data-to-data matrix to a data-to-landmark points matrix. To demonstrate the effectiveness of GRAIL, we first present domain-independent, highly accurate, and scalable time-series clustering methods to facilitate exploration and summarization of time-series collections. Then, we show that GRAIL representations, when combined with suitable methods, significantly outperform, in terms of efficiency and accuracy, state-of-the-art methods in major time-series mining tasks, such as querying, clustering, classification, sampling, and visualization. Overall, GRAIL rises as a new primitive for highly accurate, yet scalable, time-series analysis. For applications where the analysis of time series complements the analysis of other types of data, such as text and graphs, we propose generic, simple, and lightweight methodologies to learn features from time-varying measurements. Such applications often organize operations over different types of data in a pipeline such that one operation provides input---in the form of feature vectors---to subsequent operations. To reason about the temporal patterns and trends in the underlying features, we need to (i) track the evolution of features over different time periods; and (ii) transform these time-varying features into actionable knowledge (e.g., forecasting an outcome). To address this challenging problem, we propose principled approaches to model time-varying features and study two large-scale, real-world, applications. Specifically, we first study the problem of predicting the impact of scientific concepts through temporal analysis of characteristics extracted from the metadata and full text of scientific articles. Then, we explore the promise of harnessing temporal patterns in behavioral signals extracted from web search engine logs for early detection of devastating diseases. In both applications, combinations of features with time-series relevant features yielded the greatest impact than any other indicator considered in our analysis. We believe that our simple methodology, along with the interesting domain-specific findings that our work revealed, will motivate new studies across different scientific and industrial settings

Detecting Snake Fungal Disease (Ophidiomyces ophiodiicola) in the Lower Rio Grande Valley

Emerging diseases such as Snake Fungal Disease (SFD) caused by the fungus Ophidiomyces ophiodiicola (Oo) have caused population declines in various snake species in the United States which play a crucial role in the ecosystem as a natural pest control. This fungus targets the scales as a medium to thrive on which can lead to facial disfiguration and respiratory infections. We examined snakes to see if SFD was present in the LRGV and if other fungal species pose a threat to the various snake species population. The data for this study consisted of 14 live snakes captured in the wild and released after being swabbed, 2 deceased snakes and 4 sheds. The swabs were then cultured and isolated and a total of 29 isolates were sent to MIDI Labs for 28S rRNA PCR assays. The DNA sequence report from MIDI Labs did not identify Oo as being present in any of the samples but other fungal species were present in 15 of the total isolates. Seeing that harsh cold snaps and high moisture levels are rare in the LRGV, this lowers the likelihood that snakes use communal dens to maintain thermoregulation; the typical infection route for Oo to find hosts and thrive. The newly discovered fungi may have implications for the agriculture industry and public health as snakes could serve as a possible vector

The Investigation of Snake-phobia Management by the Inhabitants of the City of Kumba, Southwest Region, Cameroon

Humans have had a long standing history of interaction hostility with snakes, and most snakes have been killed by this interaction in Cameroon and many other parts of the world. Basic education on snake conservation importance has to be provided to the communities to avoid unnecessary killing of snakes. The objective of this survey is to investigate the management of snake-phobia by the inhabitants of the city of Kumba. The research data collection witnessed the administration of two hundred and fifty questionnaires in the study area to a randomly selected population sample. The results recorded a significant association between Gender and snake phobia (X2 = 17.725 df=1, P<0.05. Inaddition, there is a positive correlation between Profession and the knowledge people on non venomous snakes (R2 = 0.446, P<0.05).  Moreover, the survey revealed a significant link between the Age Category and Snake-phobia (X2 = 16.134 df=2, P<0.05).  Furthermore, there exist a significant association between the snake phobia and human reaction at snake sight (X2 = 16.521 df=3, P<0.05).  A respondent score of 70.15% is recorded on snake phobia. A respondent score of 56.72% is recorded on Black cobra, as a snake most commonly seen in the study area. Snakes are not human enemies, rather are important for human existence, ecologically and biomedically, hence their killing must be avoided during interactions. There need to be a public educational programme on the education of snakes species and behaviour to reduce and eliminate human snake-phobia. It is also very important to know that the existence of snakes should never be mystified, so many species commonly seen around like green tree snake (Dendrelaphis punctulata) are non venomous. Keywords: Snake-phobia, Non-venomous snakes, Green-tree snake, Profession, Humans

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Rapid Detection of Pityophthorus juglandis (Blackman) (Coleoptera, Curculionidae) with the Loop-Mediated Isothermal Amplification (LAMP) Method

The walnut twig beetle Pityophthorus juglandis is a phloem-boring bark beetle responsible, in association with the ascomycete Geosmithia morbida, for the Thousand Cankers Disease (TCD) of walnut trees. The recent finding of TCD in Europe prompted the development of effective diagnostic protocols for the early detection of members of this insect/fungus complex. Here we report the development of a highly efficient, low-cost, and rapid method for detecting the beetle, or even just its biological traces, from environmental samples: the loop-mediated isothermal amplification (LAMP) assay. The method, designed on the 28S ribosomal RNA gene, showed high specificity and sensitivity, with no cross reactivity to other bark beetles and wood-boring insects. The test was successful even with very small amounts of the target insect’s nucleic acid, with limit values of 0.64 pg/µL and 3.2 pg/µL for WTB adults and frass, respectively. A comparison of the method (both in real time and visual) with conventional PCR did not display significant differences in terms of LoD. This LAMP protocol will enable quick, low-cost, and early detection of P. juglandis in areas with new infestations and for phytosanitary inspections at vulnerable sites (e.g., seaports, airports, loading stations, storage facilities, and wood processing companies)