Department of Computer Science and Information Systems
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Item DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs(IEEE, 2021-09) Alladi, Tejasvi; Chamola, VinayWe are seeing a growth in the number of connected vehicles in Vehicular Ad-hoc Networks (VANETs) to achieve the goal of Intelligent Transportation System (ITS). This is leading to a connected vehicular network scenario with vehicles continuously broadcasting data to other vehicles on the road and the roadside network infrastructure. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. Existing works provide solutions addressing specific anomalies in the network only. However, since there can be a multitude of anomalies possible in the network, there is a need for better anomaly detection frameworks that can address this unprecedented scenario. In this paper, we propose an anomaly detection framework for VANETs based on deep neural networks (DNNs) using a sequence reconstruction and thresholding algorithm. In this framework, the DNN architectures are deployed on the roadside units (RSUs) which receive the broadcast vehicular data and run anomaly detection tasks to classify a particular message sequence as anomalous or genuine. Multiple DNN architectures are implemented in this experiment and their performance is compared using key evaluation metrics. Performance comparison of the proposed framework is also drawn against the prior work in this area. Our best performing deep learning-based scheme detects anomalous sequences with an accuracy of 98%, a great improvement over the set benchmark.Item Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles(IEEE, 2021-04) Alladi, Tejasvi; Chamola, Vinay; Singh, DheerendraInternet of Vehicles (IoVs) are fast becoming the norm in our society, but such a trend also comes with its own set of challenges (e.g., new security and privacy risks due to the expanded attack vectors). In this work, we propose an edge-computing-based secure, efficient, and intelligent multitier heterogeneous IoVs network. We first discuss the functionality and objectives of such an architecture. Then, we demonstrate how unsupervised deep learning techniques can facilitate the identification of suspicious vehicle behavior and ensure the security of such an architecture. The findings from our evaluations demonstrate the learning spatiotemporal information and parameter efficiency of the proposed stacked long short-term memory (LSTM) model over single LSTMs.Item NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks(IEEE, 2022-11) Alladi, Tejasvi; Chamola, VinayModern vehicular electronics is a complex system of multiple Electronic Control Units (ECUs) communicating to provide efficient vehicle functioning. These ECUs communicate using the well-known Controller Area Network (CAN) protocol. The increasing amount of research in the Intelligent Transportation System (ITS) domain has demonstrated that this protocol is vulnerable to various types of security attacks, compromising the safety of passengers and pedestrians on the roads. Hence, there is a need to develop novel anomaly detection systems to address this problem. This work presents a novel deep learning-based Intrusion Detection System incorporating thresholding and error reconstruction approaches. We train and explore multiple neural network architectures and compare their performance. The proposed anomaly detection system is tested on four kinds of attacks - Denial of Service (DoS), Fuzzy, RPM Spoofing and Gear Spoofing using evaluation metrics such as Precision, Recall and F1-Score. We also present reconstruction-error distribution plots to give a qualitative intuition about the proposed system’s ability to distinguish between genuine and anomalous sequences.