Developing an AI-Powered Chatbot to Support the Administration of Middle and High School Cybersecurity Camps

Throughout the Internet, many chatbots have been deployed by various organizations to answer questions asked by customers. In recent years, we have been running cybersecurity summer camps for youth. Due to COVID-19, our in-person camp has been changed to virtual camps. As a result, we decided to develop a chatbot to reduce the number of emails, phone calls, as well as the human burden for answering the same or similar questions again and again based on questions we received from previous camps. This paper introduces our practical experience to implement an AI-powered chatbot for middle and high school cybersecurity camps using the Google Dialogflow platform

Teaching Hands-On Cyber Defense Labs to Middle School and High School Students: Our Experience from GenCyber Camps

With the high demand of the nation for next generation cybersecurity experts, it is important to design and provide hands-on labs for students at the K-12 level in order to increase their interest in cybersecurity and enhance their confidence in learning cybersecurity skills at the young age. This poster reports some preliminary analysis results from the 2016 GenCyber summer camp held at Old Dominion University (ODU), which is part of a nationwide grant program funded by the National Security Agency (NSA) and the National Science Foundation (NSF). This poster also demonstrates the design of three hands-on labs which have been devised to be age-appropriate for middle and high school students

DeepPOSE: Detecting GPS Spoofing Attack Via Deep Recurrent Neural Network

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this end, we propose DeepPOSE, a deep learning model, to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms. Our design uses a convolutional and recurrent neural network to reduce the noise, to recover a vehicle\u27s real-time trajectory from multiple sensor inputs. We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map, to smartly eliminate the accumulation of errors on the trajectory estimation. The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack. Compared with the existing method, the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks

A Channel State Information Based Virtual MAC Spoofing Detector

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information (CSI) between the communication peers to provide additional authentication protection at the physical layer. A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location, which was not well addressed by the existing approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located

View Synthesis With Scene Recognition for Cross-View Image Localization

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions

Fatigue safety monitoring and assessment of short and medium span concrete girder bridges

Concrete bridge is widely used in highway infrastructure in China, especially in short and medium span bridges. Concrete bridges are prone to fatigue failure under the coupled actions of repeated vehicles loads, environment and material degradation. In recent years, the traffic volume and vehicle weights of highway bridges have been continuously increasing, so concrete bridge fatigue problem becomes more serious. This paper introduces advanced fatigue safety monitoring techniques and fatigue performance assessment methods for short and medium span concrete girder bridges. Weigh-in-motion (WIM) system was used to record the real traffic volume, and then the acquired load spectrum was applied on typical concrete bridges through Matlab to analyze the fatigue performance of different bridge types. From the analysis results, several typical short and medium span concrete girder bridges are selected to conduct long-term service monitoring. The cross section types include hollow slab girder, T-girder and short box girder, and the structure types contain simple supported bridge and continuous girder bridge. WIM technique, dynamic strain monitoring technique and acoustic emission technique are used to monitor the key details. Fatigue performance is assessed and analyzed based on monitoring data, considering traffic increase, overload and corrosion factors

DeepMag+ : Sniffing Mobile Apps in Magnetic Field Through Deep Learning

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only 15% and at the same time has a negligible effect on benign Apps. ©2019 Published by Elsevier B.V

Biology Inspired Approach for Communal Behavior in Sensor Networks

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control