Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to revolutionize commercial industries and the public domain outside of the military. UAVs would be able to speed up rescue and recovery operations from natural disasters and can be used for autonomous delivery systems (e.g., Amazon Prime Air). An increase in the number of active UAV systems in dense urban areas is attributed to an influx of UAV hobbyists and commercial multi-UAV systems. As airspace for UAV flight becomes more limited, it is important to monitor and manage many UAV systems using modern collision avoidance techniques. In this paper, we propose a cloud-based web application that provides real-time flight monitoring and management for UAVs. For each connected UAV, detailed UAV sensor readings from the accelerometer, GPS sensor, ultrasonic sensor and visual position cameras are provided along with status reports from the smaller internal components of UAVs (i.e., motor and battery). The dynamic map overlay visualizes active flight paths and current UAV locations, allowing the user to monitor all aircrafts easily. Our system detects and prevents potential collisions by automatically adjusting UAV flight paths and then alerting users to the change. We develop our proposed system and demonstrate its feasibility and performances through simulation.

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

Moving target defense (MTD) is useful for thwarting network reconnaissance and preventing unauthorized access. While previous research in MTD focuses on protecting the endnodes, we leverage software-defined networking (SDN) to implement MTD on the data-plane switches, which significantly decreases the controller communication overhead and enables quicker defense response to reduce the attack impact. Our work not only randomizes the IP addresses for MTD but also uses the IP addresses for synchronization across the nodes in the networking path by generating hash-chain-based synchronization signatures. Our scheme is practical as it builds on and encodes the existing IP addresses for randomization to construct a modular solution independent to the routing/flow rule implementation and does not incur additional networking overhead except for the seed distribution (which can occur offline). Our scheme is also effective (the attacker’s required cost to achieve timely network reconnaissance increases by more than an order of magnitude than the previous state of the art having the controller actuate the MTD randomization) and scalable (the relative overhead cost of our scheme becomes smaller as the network grows). We analyze our scheme and implement and experiment it on an Open vSwitch-based testbed and on CloudLab to validate these properties.

Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. The problem being solved is one of the characteristics that lead to fraud rather than detecting fraud. Neural network algorithm is used to evaluate the proposed feature set and compare it against the state-of-the-art feature sets in detecting fraud. The feature set considers the user’s social interaction on the Yelp platform to determine if the user is committing fraud. The neural network algorithm helps in comparing the feature set with other feature sets used to detect fraud. Any attempt to find the characteristics that lead to fraud has a prerequisite to be good enough to detect fraud as well. The F1 score obtained using neural networks is on par with all the well-known methods for detecting fraud, a value of 0.95. The effectiveness of the feature set is in rivaling the other approaches to fraud detection. A user’s social interaction on a digital platform such as Yelp is equally important in evaluating the user as social interaction is in real life. The characteristics that lead to fraud can be intuitively captured. The characteristics such as number of friends, number of followers and the number of times the user has provided a review which was helpful to multiple people provide the neural network with a base to form a relationship between opinion fraud and social interaction characteristics.

Software-defined wide-area network (SD-WAN) is an emerging and advanced networking platform extending software-defined networking (SDN) across multiple networking domains. Because SD-WAN manages the data plane in the networking domains separated by the public Internet, SDWAN provides a distinct environment and challenges from SDN, including greater risks for the security threats injecting control plane communications from attackers residing outside of the SDN domain. We design and build blockchain-coordinating controllers (BCC) to secure control communications of the SD-WAN controller network formed by the distributed controllers spread across multiple domains. BCC provides resiliency against the security threats in the control plane where an attacker compromises controller communications to manipulate the coordination and the operations of the other controllers. More specifically, BCC provides secure control communications even when up to n controllers’ networking credentials are compromised. BCC is also designed for modularity so that it applies generally across the controller implementations. We prototype BCC using Ethereum and smart contract on CloudLab to validate its effectiveness and efficiency. We experiment on geographically separate nodes on CloudLab and show that BCC achieves the distributed consensus at sub-second level for certificate/key distribution and for network-wide control communication synchronization.

Deep learning-based intrusion detection systems have advanced due to their technological innovations such as high accuracy, automation, and scalability, to develop an effective network intrusion detection system (NIDS). However, most of the previous research has focused on model generation through intensive analysis of feature engineering instead of considering real environments. They have limitations to applying the previous methods for a real network environment to detect real-time network attacks. In this paper, we propose a new flexible and robust NIDS based on Recurrent Neural Network (RNN) with a multi-classifier to general a detection model in real-time. The proposed system adaptively and intelligently adjusts the generated model with given system parameters that can be used as security parameters to defend against the attacker’s obfuscation techniques in real-time. In the experimental results, the proposed system demonstrated that the proposed system detects network attacks with a high accuracy and high-speed model upgrade in real-time while showing robustness under an attack.