Data analytics for modeling and visualizing attack behaviors: A case study on SSH brute force attacks

In this research, we explore a data analytics based approach for modeling and visualizing attack behaviors. To this end, we employ Self-Organizing Map and Association Rule Mining algorithms to analyze and interpret the behaviors of SSH brute force attacks and SSH normal traffic as a case study. The experimental results based on four different data sets show that the patterns extracted and interpreted from the SSH brute force attack data sets are similar to each other but significantly different from those extracted from the SSH normal traffic data sets. The analysis of the attack traffic provides insight into behavior modeling for brute force SSH attacks. Furthermore, this sheds light into how data analytics could help in modeling and visualizing attack behaviors in general in terms of data acquisition and feature extraction

Disaster Relief Medicaid Evaluation Project

[Excerpt] This study is a retrospective evaluation of the enrollment processes and service delivery associated with DRM. It examines this unexpected experiment and assesses the outcomes. This report begins with an overview of the Medicaid/Family Health Plus program in September 2001, and is followed by a description of the challenges of, and responses to, the World Trade Center disaster. It then looks at how well the DRM process worked, how accessible needed services were for recipients, how costs compared to costs associated with those previously enrolled in the traditional Medicaid program, and how the different eligibility/verification procedures affected program integrity. Finally, in the section Background Information: Detailed History of Disaster Relief Medicaid, it presents a narrative timeline, detailing the decision steps by which DRM was implemented

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial