Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

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Ambient Intelligence for Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion and Anomaly Detection Strategies
(IEEE, 2023-03) Alladi, Tejasvi; Chamola, Vinay
The Internet of Things (IoT) is increasingly being deployed in smart city applications such as vehicular networks. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. These anomalies could range from faulty vehicular data being broadcast by the vehicles to more catastrophic attacks such as disruptive attacks and Denial of Service (DoS) attacks to name a few. This calls for a need to develop robust security schemes such as intrusion detection and anomaly detection schemes. With a humongous growth in the amount of vehicular traffic data expected, artificial intelligence (AI)-based detection strategies need to be developed to address this burgeoning demand. In this article, we propose three AI-based intrusion detection strategies for vehicular network applications, leading to an effective Ambient Intelligence based vehicular network paradigm. The detection tasks are run on local edge servers deployed at the network edge. By showing the prediction results on an experimental testbed emulating the edge servers, we show the feasibility of deploying the proposed strategies in the vehicular network scenario.
A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems
(Elsevier, 2022-07) Alladi, Tejasvi; Chamola, Vinay
With the rise of the Internet of Vehicles (IoV) and the number of connected vehicles increasing on the roads, Cooperative Intelligent Transportation Systems (C-ITSs) have become an important area of research. As the number of Vehicle to Vehicle (V2V) and Vehicle to Interface (V2I) communication links increases, the amount of data received and processed in the network also increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario, the need for deep learning-based techniques is all the more heightened. This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). The proposed Deep Learning Classification Engines (DCLE) comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers. Vehicular data received by the Road Side Units (RSUs) is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
Detecting UAV Presence Using Convolution Feature Vectors in Light Gradient Boosting Machine
(IEEE, 2022-12) Alladi, Tejasvi; Chamola, Vinay
The growing number of Unmanned Aerial Vehicle (UAV) applications brings with it, a rising number of privacy concerns. The high availability of commercial drones is also increasing the need for strict regulations. As far away as we are from establishing such protocols to ensure that the most basic human right to privacy is not exploited, we are further away from enforcing them. Thus, there is a need for a generalised drone detection system to detect different drones operating in a broad range of Radio Frequencies (RF). Previous attempts to tackle this problem have been made using audio, video, radar, WiFi and RF signals. While all these methods have their own benefits and drawbacks, RF has various characteristics which make them suitable for practical applications on a large scale. In this paper, we propose a novel technique called the ConvLGBM model which combines the feature extraction capability of a Convolution Neural Network (CNN) with the high classification accuracy of the Light Gradient Boosting Machine (LightGBM). We develop and evaluate the classifications done by an optimal CNN and the LightGBM model and then compare both models with the ConvLGBM.