External vendors develop a significant percentage of Windows kernel drivers, and Microsoft relies on these vendors to handle all aspects of driver security. Unfortunately, device vendors are not immune to software bugs, which in some cases can be exploited to gain elevated privileges. Testing the security of kernel drivers remains challenging: the lack of source code, the requirement of the presence of a physical device, and the need for a functional kernel execution environment are all factors that can prevent thorough security analysis. As a result, there are no binary analysis tools that can scale and accurately find bugs at the Windows kernel level.

In many software applications, logs serve as the only interface between the application and the developer. However, navigating through the logs of long-running applications is often challenging. Logs from previously successful application runs can be leveraged to automatically identify errors and provide users with only the logs that are relevant to the debugging process. We describe a privacy-preserving framework that can be employed by Platform as a Service (PaaS) providers to utilize the user logs generated on the platform while protecting the potentially sensitive logged data. Further, in order to accurately and scalably parse log lines, we present a distributed log parsing algorithm that leverages Locality Sensitive Hashing (LSH). We outperform the state-of-the-art on multiple datasets. We further demonstrate the scalability of Delog on publicly available Thunderbird log dataset with close to 27,000 unique patterns and 211 million lines.

Logs serve as a critical tool for debugging and monitoring applications. However, gaining insights from unstructured logs is difficult. Hence, many log management and analysis applications first parse logs into structured templates. In this paper, we train a data-driven log parser on our new Apache Spark dataset, the largest application log dataset yet. We implement a distributed online algorithm to accommodate for the large volume of data. We also devise a new metric for evaluation of parsers when labeled data is unavailable. We show that our method generalizes over diverse datasets without any parameter tuning or domain-specific inputs from the user. When evaluated on publicly available HDFS dataset our method performs 13x faster than the previous state-of-the-art.

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.

Insect pests are a major cause of crop loss globally. Pest management will be effective and efficient if we can predict the occurrence of peak activities of a given pest. Research efforts are going on to understand the pest dynamics by applying analytical and other techniques on pest surveillance data sets. We make an effort to understand pest population dynamics using neural networks by analyzing pest surveillance data set of Helicoverpa armigera or Pod borer on chickpea (Cicer arietinum L.) crop. The results show that neural network method successfully predicts the pest attack incidences for one week in advance.