Department of Computer Science and Information Systems
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Item Deep Neural Networks for Securing IoT Enabled Vehicular Ad-Hoc Networks(IEEE, 2021) Alladi, Tejasvi; Chamola, VinayVehicular ad-hoc network (VANET) security has been an active area of research over the past decade. However, with the increasing adoption of the Internet of Things (IoT) in VANETs, the number of connected vehicles is set to grow exponentially over the next few years, which translates to a higher number of communication interfaces and a greater possibility of cybersecurity attacks. Along with these cybersecurity attacks, the instances of compromised vehicles sending faulty information about their positions and speeds also increase exponentially. Thus, there is a need to augment the existing security schemes with anomaly detection schemes which can differentiate normal vehicle data from malicious and faulty data. Since, the number of anomaly types can be many, deep neural networks would work best in this scenario. In this paper, we propose a deep neural network-based vehicle anomaly detection scheme. We use a sequence reconstruction approach to differentiate normal vehicle data from anomalous data. Numerical results show that we can correctly detect data corresponding to several anomaly types.Item Artificial Intelligence (AI)-Empowered Intrusion Detection Architecture for the Internet of Vehicles(IEEE, 2021-06) Alladi, Tejasvi; Chamola, VinayRecent advances in the Internet of Things (IoT) and the adoption of IoT in vehicular networks have led to a new and promising paradigm called the Internet of Vehicles (IoV). However, the mode of communication in IoV being wireless in nature poses serious cybersecurity challenges. With many vehicles being connected in the IoV network, the vehicular data is set to explode. Traditional intrusion detection techniques may not be suitable in these scenarios with an extremely large amount of vehicular data being generated at an unprecedented rate and with various types of cybersecurity attacks being launched. Thus, there is a need for the development of advanced intrusion detection techniques capable of handling possible cyberattacks in these networks. Toward this end, we present an artificial intelligence (AI)-based intrusion detection architecture comprising Deep Learning Engines (DLEs) for identification and classification of the vehicular traffic in the IoV networks into potential cyberattack types. Also, taking into consideration the mobility of the vehicles and the realtime requirements of the IoV networks, these DLEs will be deployed on Multi-access Edge Computing (MEC) servers instead of running on the remote cloud. Extensive experimental results using popular evaluation metrics and average prediction time on a MEC testbed demonstrate the effectiveness of the proposed scheme.Item Securing the Internet of Vehicles: A Deep Learning-Based Classification Framework(IEEE, 2021-06) Alladi, Tejasvi; Chamola, VinayAlong with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.Item ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions(IEEE, 2020) Narang, Pratik; Chamola, VinayAdverse weather conditions such as fog, haze, snow, mist and glare create visibility problems for applications of autonomous vehicles. To ensure safe and smooth operations in frequent bad weather scenarios, image dehazing is crucial to any vehicular motion and navigation task on road or air. Moreover, the commonly deployed mobile systems are resource constrained in nature. Therefore, it is important to ensure memory, compute and run-time efficiency of dehazing algorithms. In this manuscript we propose ReViewNet, a fast, lightweight and robust dehazing system suitable for autonomous vehicles. The network uses components like spatial feature pooling, quadruple color-cue, multi-look architecture and multi-weighted loss to effectively dehaze images captured by cameras of autonomous vehicles. The effectiveness of the proposed model is analyzed by exhaustive quantitative evaluation on five benchmark datasets demonstrating its supremacy over other existing state-of-the-art methods. Further, a component-wise ablation and loss weight ratio analysis demonstrates the contribution of each and every component of the network. We also show the qualitative analysis with special use cases and visual responses on distinctive vehicular vision instances, establishing the effectiveness of the proposed method in numerous hazy weather conditions for autonomous vehicular applications.Item AI-Enabled Object Detection in UAVs: Challenges, Design Choices, and Research Directions(IEEE, 2021-08) Narang, Pratik; Chamola, VinayUnmanned aerial vehicles (UAVs) are emerging as a powerful tool for various industrial and smart city applications. UAVs coupled with various sensors can perform many cognitive tasks such as object detection, surveillance, traffic management, and urban planning. Deep learning has emerged as a popular technique to speed up the processing of high-dimensional data like images and videos, which has led to several applications in surveillance and autonomous driving. However, the area of aerial object detection has been understudied. This work proposes a deep learning approach for detection of objects in aerial scenes captured by UAVs. Our work first categorizes the current methods for aerial object detection using deep learning techniques and discusses how the task is different from general object detection scenarios. We delineate the specific challenges involved and experimentally demonstrate the key design decisions that significantly affect the accuracy and robustness of models. We further propose an optimized architecture that utilizes these optimal design choices along with the recent Res-NeSt backbone to achieve superior performance in aerial object detection. Lastly, we propose several research directions to inspire further advancement in aerial object detection.Item A low power consumption mobile based IoT framework for real-time classification and segmentation for apple disease(Elsevier, 2022-10) Raman, Sundaresan; Chamola, VinayUntreated diseases in plants not only lead to monetary losses but can have adverse implications when consumed. Disease diagnosis requires early detection and analysis of the disease. Apple horticulture has been a significant agriculture industry around the world and is affected by three most prominent domains of disease in apple namely: Blotch, Scab and Rot. In this paper, we provide a real-time mechanism for simultaneous classification and segmentation of the disease which significantly improves the speed of prediction. We have introduced atrous skip connections with UNet (with ResNet as backbone) furthering the performance. Experimental results on our proposed framework, achieves an accuracy of 94.29% to classify the disease and a dice score of 90.01% for segmentation of the diseased part. We also have developed a mobile application to demonstrate the objectives and to facilitate a user-friendly interface for using the proposed framework.