Shahrzad Zargari

Cloud Forensics: Concepts, Issues, and Challenges

Cloud Computing is becoming so popular among organizations, promising simplicity and delivering utilities based on virtualization technologies. Convenience, availability, elasticity, large storage capacity, speed, scalability, and on-demand network access are some of the attractions of the cloud computing. The adoption of cloud computing solution is increasing rapidly which makes it inevitable for digital forensics not to follow since major potential security risks are surrounded this new technology. This study provides an overview of cloud forensics including the issues and the existing challenges in order to give better future prospects and also offers some steps to be taken to overcome these challenges.

Feature Selection in the Corrected KDD-dataset

Automation in anomaly detection, which deals with detecting of unknown attacks in the network traffic, has been the focus of research by using data mining techniques in recent years. This study attempts to explore significant features (curse of high dimensionality) in intrusion detection in order to be applied in data mining techniques. Therefore, the existing irrelevant and redundant features are deleted from the dataset resulting faster training and testing process, less resource consumption as well as maintaining high detection rates. The findings were tested on the NSL-KDD datasets (anomaly intrusion datasets) in order to confirm the outcomes.

Fake News Identification on Twitter with Hybrid CNN and RNN Models

The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain.

A statistical downscaling framework for environmental mapping

In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold—complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June–August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted components via an algorithm with fully adaptable parameters. We demonstrate how the algorithm can be extended to weather-dependent and other applications with inherent data randomness and model variability via its built-in interdisciplinary computational power that connects data sources with end-users.