Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models

Web-based phishing attacks nowadays exploit popular cloud web hosting services and apps such as Google Sites and Typeform for hosting their attacks. Since these attacks originate from reputable domains and IP addresses of the cloud services, traditional phishing detection methods such as IP reputation monitoring and blacklisting are not very effective. Here we investigate the effectiveness of deep learning models in detecting this class of cloud-based phishing attacks. Specifically, we evaluate deep learning models for three phishing detection methods--LSTM model for URL analysis, YOLOv2 model for logo analysis, and triplet network model for visual similarity analysis. We train the models using well-known datasets and test their performance on phishing attacks in the wild. Our results qualitatively explain why the models succeed or fail. Furthermore, our results highlight how combining results from the individual models can improve the effectiveness of detecting cloud-based phishing attacks

SOIL EROSION MAPPING OF WATERSHED IN MIRZAPUR DISTRICT USING RUSLE MODEL IN GIS ENVIRONMENT

Soil erosion is one of the serious issues threatening the environment. It is a growing problem especially in areas of agricultural activity where soil erosion not only leads to de-creased agricultural productivity but also reduces water availability. This leads to drastic degradation of the agricultural lands. So there is a need to take up conservation and management measures which can be applied to check further soil erosion. Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. Remote sensing and GIS techniques have become valuable tools for the digitization of the input data and genereation of maps. In the present study, RUSLE model has been adopted to estimate the soil erosion in the Khajuri watershed of Uttar Pradesh, India. This model involves calculation of parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor  (LS), cropping management factor (C), and support practice factor (P). Layer wise thematic maps of each of these factors were generated using GIS platform using various data sources and data preparation methods. The results of the study indicate that the annual average soil loss within the watershed is about  t/ha/yr (metric ton per hectare per year)

'Reforms by stealth': Indian economic policy, big business and the promise of the Shastri years, 1964-1966

South Asia252205-23