A Hybrid Software Defined Network Platform for Undergraduate Research and Education

Software Defined Networks (SDNs) are leading the evolution toward network programmability and open architectures. While many corporations, nonprofits, and individuals have developed training on SDNs, the industry has a significant gap with the robustness of entrenched traditional network educational models, such as Cisco’s Networking Academy. The Department of Defense (DoD) will likely adopt some form of SDN into its global transport network at various tiers and authority boundaries. It is imperative for 21st century leaders to understand how and why the manner in which DoD provides Information Technology (IT) services to its customers is changing with such rapidity. Therefore, we developed three basic SDN course lessons as a base of knowledge and support and integrated a hybrid physical SDN research platform into existing laboratory infrastructure for faculty research and capstone projects for senior cadets. This was accomplished by leveraging existing SDN-related tutorials and resources and integrating them within a virtualized SDN simulation environment. The three lessons were developed for integration into our core networking course that describes fundamental networking concepts in the context of an SDN - with a centralized control plane, while ensuring lesson learning objectives were achievable by non-technical majors yet sufficiently comprehensive across the fundamental operations of an SDN. The hybrid research platform consists of a number of Virtual Machines (VMs) running Mininet1 - an SDN simulation environment - and hosted on a VMware vSphere cluster with direct connectivity to twelve physical openflow-capable switches. This will allow students in the networking course to plan, design, implement, and test a basic SDN topology in either a virtual, physical, or hybrid environment. In addition, it will provide topological and experimental flexibility to student and faculty researchers and senior capstone project teams alike

Effects of YouTube Video as Pre-Lecture Preparation

Classroom lectures convey course concepts more effectively when students have prepared in advance. Traditionally, students prepare for lectures by reading the course textbook. Textbooks are the default study material for most educational courses; however, some technical subjects are better conveyed in video format. Therefore, in this study, we encouraged students to supplement their learning resources with web-based video tutorials that provide detailed demonstrations with the respect to technical network configuration and management. YouTube is a video sharing website that can provide free educational tutorials and instructions on technical subject matter, where students can observe practical human-machine interaction to prepare for lectures and increase overall course performance on exams, assignments, and laboratory projects. Our goal was to compare the overall performance as well as the level of active class participation between two groups of the same computer networking course. We found that the group that used YouTube videos for pre-lecture preparation scored approximately 3% higher on exams but 5% lower on homework assignments than the control group (textbook only). There was no statistical significance between the two groups with respect to overall course grades. Study habits and degree of class participation of each student correlated more strongly with overall course performance than whether the student viewed the videos

Integrating Data Science into a General Education Information Technology Course: An Approach to Developing Data Savvy Undergraduates

The National Academies recommend academic institutions foster a basic understanding of data science in all undergraduates. However, data science education is not currently a graduation requirement at most colleges and universities. As a result, many graduates lack even basic knowledge of data science. To address the shortfall, academic institutions should incorporate introductory data science into general education courses. A general education IT course provides a unique opportunity to integrate data science education. Modules covering databases, spreadsheets, and presentation software, already present in many survey IT courses, teach concepts and skills needed for data science. As a result, a survey IT course can provide comprehensive introductory data science education by adding a data science module focused on modeling and evaluation, two key steps in the data science process. The module should use data science software for application, avoiding the complexities of programming and advanced math, while enabling an emphasis on conceptual understanding. We implemented a course built around these ideas and found that the course helps develop data savvy in students

Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People

This paper presents a near real-time, multi-stage classifier which identifies people and handguns in images, and then further assesses the threat-level that a person poses based on their body posture. The first stage consists of a convolutional neural network (CNN) that determines whether a person and a handgun are present in an image. If so, a second stage CNN is then used to estimate the pose of the person detected to have a handgun. Lastly, a feed-forward neural network (NN) makes the final threat assessment based on the joint positions of the person’s skeletal pose estimate from the previous stage. On average, this entire pipeline requires less than 1 second of processing time on a desktop computer. The model was trained using approximately 2,000 images and achieved a pistol and person detection rate of 22% and 55%, respectively. The final stage NN correctly identified the severity of the threat with 84% accuracy. The images used to train each stage of our multi-classifier model are available online. With an expanded dataset the accuracy of detecting people and pistols can likely be improved in the future

A Machine Learning Framework for Building Passive Surveillance Photogrammetry Models

Determining the geographic location of an object using two-dimensional (2D) images recorded at high-oblique angles is a nontrivial problem. Existing methods to solve this problem rely on parameters that are either difficult to measure or are based on assumptions. This paper investigates the accuracy of building photogrammetric models using machine learning. Our novel approach involves the collection of training examples before using supervised learning to build a nonlinear, multitarget prediction model. We collected training examples using an unmanned ground vehicle (UGV) that moved throughout the fields of view of multiple cameras. The UGV was tracked and bounded using existing computer vision techniques. With each image frame, the center pixel position (x, y image coordinates) of the vehicle and its bounding box area (in pixels) were mapped to its current GPS coordinates. Multiple machine learning models were created using various combinations of cameras to determine the key features for building accurate photogrammetric models. Data was collected under realistic conditions for ground-based surveillance systems, which may require cameras to be placed at low elevations and high-oblique angles. We found the prediction accuracy of our models to be between 0.58 and 3.54 meters depending upon a number of factors, including the locations, heights, and orientations of the cameras used

Autonomous Navigation via a Deep Q Network with One-Hot Image Encoding

Common autonomous driving techniques employ various combinations of convolutional and deep neural networks to safely and efficiently navigate unique road and traffic conditions. This paper investigates the feasibility of employing a reinforcement learning (RL) model for autonomous navigation using a low dimensional input. While many navigation applications generate each individual state as a function of a frame\u27s raw pixel information, we use a deep Q network (DQN) with reduced input dimensionality to train a mobile robot to continuously remain within a lane around an elliptical track. We accomplish this by using a one-hot encoding scheme that assigns a binary variable to each element in a square array. This value is a function of whether the input frame detects the presence of a lane boundary. Our ultimate goal was to determine the minimum number of training samples required to consistently train the robot to complete one cycle around the track, from multiple starting positions and directions, without crossing a lane boundary. We found that by intelligently balancing exploration and exploitation of its environment, as well as the rewards for staying in the lane, the robot was able to achieve its goal with a small number of samples

Comparison of Skeleton Models and Classification Accuracy forPosture-Based Threat Assessment Using Deep-Learning

This paper compares the effectiveness of two different skeletal pose models for a near real-time, multi-stage classifier. A cascaded neural-network (NN) classifier was previously developed to identify the level of threat posed by an armed person based on detected weapons and body posture. On an updated database of images containing armed individuals and groups, AlphaPose was used to calculate both MPII and COCO skeletons while OpenPose was used to calculate the COCO only. For comparison, we evaluated the importance of individual skeletal joints by systematically removing specific joints from the feature vector and retraining a reduced order network. On the database of images, the AlphaPose-COCO network was best able to correctly classify the threat presented by individuals, 83.7% on average, while AlphaPose-MPII registered 82.2% and 77.6% for OpenPose-COCO. As expected, the most important single joint in both skeleton models is the location of the pistol. As a guide for others deciding which skeleton to use for further studies, we conclude that neither skeleton significantly outperforms the other

Establishing and Maintaining Multivariate Trust in a Hierarchical SDN

Traditional network architectures suffer from an inability to depart from the marriage between the control and data planes housed in the same physical device. Software Defined Networking (SDN) provides such a departure: an architecture that can rapidly integrate diverse and dynamic network functions. Current network architectures trust traffic typically based on IP address and the physical location of the host. This paper defines and outlines a multivariate trust model in an SDN environment that provides a method to implement the policies of a complex organization. An entity\u27s trust level, based on hardware trusted platform modules, operating system status, user identification, and traffic patterns, is used to determine whether its particular traffic flow is allowed to traverse the network. Ultimately, we allocate a dynamic network slicing solution to such flows, enabling the efficient allocation of bandwidth across a layered SDN. We are deploying this trust model on a three-tiered network model designed to simulate the hierarchical nature of the US Army and the Department of Defense Information Network (DODIN) through common traffic scenarios