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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.Anomaly detection

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Now showing 1 - 7 of 7
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    QuARCS: Quantum Anomaly Recognition and Caption Scoring Framework for Surveillance Videos
    (IEEE, 2024-08) Chamola, Vinay
    Traditional surveillance video stream monitoring demands manual analysis, often leading to inaccuracies. While recent advancements have enabled automated analysis in surveillance video stream monitoring, challenges persist in achieving high accuracy and efficiency. Thus, an automated system is needed to monitor and report on video streams in real-time or retrospectively within surveillance networks, alleviating human error and inefficiency. Our paper, presents a comprehensive framework that integrates a hybrid quantum-classical anomaly detection system, a caption-generating model, and a novel Text-Driven Urgency Rating Model (T-DURM) trained using a newly created labelled dataset called UCFC-CUR which prioritises crimes based on their urgency. The hybrid classifier outperforms its direct classical counterpart by 7.7%. The aforementioned pipeline possesses the capability to identify anomalous occurrences from surveillance videos, generate a textual representation of the event, and assign a numerical value indicating the level of urgency associated with the specific anomaly. The hybrid anomaly detection model achieved an AUC of 82.80 surpassing the classical model’s AUC of 75.14. While the newly proposed T-DRUM achieves a R2 score of 0.982.
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    Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles
    (IEEE, 2021-04) Chamola, Vinay; Singh, Dheerendra
    Internet 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.
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    Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Social Surveillance Systems
    (IEEE, 2023-01) Chamola, Vinay
    Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset: https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.
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    Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks
    (IEEE, 2022-09) Chamola, Vinay
    There has been a notable increase in the research and development of Vehicular Ad-hoc Networks (VANETs) to efficiently and safely manage large amounts of traffic. Such networks are, however, also prone to various cyber threats to data integrity, privacy, authentication, and network availability, and given the potential risk to life under the event of a malfunction and misinformation, it is important to provide security measures against such threats. This paper presents the Multi-branch Reconstruction Error (MbRE) Intrusion Detection System (IDS) for edge-based anomaly detection in VANETs for data integrity, network availability and user authentication-based misbehaviors without the need to train on them. Vehicular data is first sequenced and separated into three data branches -frequency (F) derived from the message timestamps, pseudo-identities (I), and the motion data (M) i.e. position and velocity. The proposed model comprises of three Convolutional Neural Networks (CNN)-based reconstruction models trained to reconstruct normal F-I-M vehicular behavior. The IDS classifies each branch of a sequence as 0/1 based on the reconstruction error threshold for the respective branch and, therefore, has the ability to detect 8 possible binary encoded behaviors for each sequence of vehicular data. These results are then used to find the overall behavior of each vehicle using carefully selected detection thresholds. MbRE is able to classify frequency, identity and motion-based behavior samples with an accuracy of 100%, 98.5-100%, and 95.4-100%, respectively, without the need to train on such behaviors. The study also emulates the IDS on Google Colaboratory and Jetson Nano to show its practicality in cloud and edge environments.
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    DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs
    (IEEE, 2021-09) Alladi, Tejasvi; Chamola, Vinay
    We 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.
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    Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles
    (IEEE, 2021-04) Alladi, Tejasvi; Chamola, Vinay; Singh, Dheerendra
    Internet 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.
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    NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks
    (IEEE, 2022-11) Alladi, Tejasvi; Chamola, Vinay
    Modern 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.