BITS Faculty Publications
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Item A cognitive digital twin for process chain anomaly detection and bottleneck analysis(Taylor & Francis, 2024-07) Sangwan, Kuldip SinghBottleneck detection and management plays a significant role in the context of Industry 4.0, wherein process chains have become more intricate. The dynamic nature of process chains shifts the bottleneck location, which requires an integrated methodology capable of identifying current as well as predicting future bottlenecks. The paper proposes a cognitive digital twin (CDT) with a novel explainable artificial intelligence (XAI) model. The proposed CDT is capable of (i) detecting existing bottlenecks, (ii) detecting data anomalies and process chain anomalies (iii) estimating shifting bottlenecks due to anomalies, (iv) predicting near future bottlenecks, and (v) the XAI model supports operational and strategic decision making. The usefulness of proposed CDT is demonstrated and validated experimentally on an industry 4.0 compliant learning factory. The proposed novel CDT effectively addresses the process chain bottlenecks (existing, shifting, and future) while the XAI model enhances transparency and trustworthiness for practical implementation.Item Detecting additive manufacturing anomalies with shallow convolutional neural networks(Springer, 2025-10) Sangwan, Kuldip SinghAdditive manufacturing often known as 3D printing, has been significant in the manufacturing industry in recent decades. However, the method encounters significant challenges in the form of printing errors, adversely impacting end-user product experience and obstacles to widespread adoption. The current manual and sensor-based continuous monitoring techniques lack a clear distinction between anomalies and healthy data points, making them ineffective for implementation in industrial environments. This research introduces a computer vision-based methodology for detecting anomalies in real-time. Two Convolutional Neural Networks versions are created, Model V1 using residual connection with decreased parameters and computational complexity and Model V2 to facilitate effortless deployment on constraint devices without compromising performance. The proposed CNN networks are evaluated against state-of-the-art classification models, namely ResNet18, ResNet34, and Deep LSTM classifier, to assess their performance. Model V1 and Model V2 achieved comparable performances with 86.7% and 11.86% reduced parameters compared to ResNet18. Afterward, quantization is applied to produce a compact model representation for edge-device deployment. The quantization model proposed has no loss in performance. Lastly, an inference study is conducted on multiple edge devices where the TI AM68A board proved fast, with 0.246 and 0.04 s inference time for models V1 and V2 respectively.Item QuARCS: Quantum Anomaly Recognition and Caption Scoring Framework for Surveillance Videos(IEEE, 2024-08) Chamola, VinayTraditional 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.Item Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles(IEEE, 2021-04) 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 Anomaly-based Intrusion Detection using GAN for Industrial Control Systems(IEEE, 2022) Viswanathan, SangeethaIn recent years, cyber-attacks on modern industrial control systems (ICS) have become more common and it acts as a victim to various kind of attackers. The percentage of attacked ICS computers in the world in 2021 is 39.6%. To identify the anomaly in a large database system is a challenging task. Deep-learning model provides better solutions for handling the huge dataset with good accuracy. On the other hand, real time datasets are highly imbalanced with their sample proportions. In this research, GAN based model, a supervised learning method which generates new fake samples that is similar to real samples has been proposed. GAN based adversarial training would address the class imbalance problem in real time datasets. Adversarial samples are combined with legitimate samples and shuffled via proper proportion and given as input to the classifiers. The generated data samples along with the original ones are classified using various machine learning classifiers and their performances have been evaluated. Gradient boosting was found to classify with 98% accuracy when compared to otherItem Classifying DNS over HTTPS Malicious/Benign Traffic Using Deep Learning Models(IEEE, 2023) Viswanathan, SangeethaAs we live in an era where privacy over the Internet has become rudimentary, protocols like DNS over HTTPS (DoH) and DNS over TLS (DoT), which promote encryption, have become popular. While these protocols were introduced to overcome the drawbacks of DNS protocol, even DoH has some security issues that need to be tackled to prevent any misuse. Herein, we implemented deep learning models to classify DNS over HTTPS traffic and found the most efficient method in regard to time-required complexity and computational requirements. Previous studies have used a variety of features from datasets to identify malicious activities. Although machine learning and deep learning models are commonly used, they require more human intervention. These models are also more computationally complex, as one is required to tune the model and its parameters for accurate results. In comparison, some deep learning models are more efficient as they work well without any human intervention and are capable of parameter tuning by themselves. In this work, we used the CIRA-CIC-DoHBrw-2020 dataset and performed data imbalance handling, one hot encoding, and feature selection to create a model that can be used for a more generalized environment. We implemented long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) models to classify DoH traffic with high accuracy. Although the mentioned models produced good accuracy, the BiLSTM model performs better than the LSTM model in the time taken for prediction and accuracy; the GRU model outperformed both LSTM and BiLSTM models in terms of accuracy, computation time, and computation complexity. Hence, it is more efficient than both LSTM and BiLSTM models.Item Threat Intelligence System for Internet of Things based Smart Environments using Unsupervised Learning(IEEE, 2022) Shenoy, Meetha.V.With the advent of the Internet of Things (IoT), our environments have become pervasive. Studies indicate that most of the existing IoT devices have inherent security flaws and are vulnerable to both internal and external attacks. Threat identification is generally done by either identification of the specific type of attack in the network or by distinguishing benign traffic from anomalous traffic. Most of the recent works for threat identification rely on supervised machine learning techniques which involve training a model using datasets containing labeled samples of prior attacks. A supervised network can thus only understand the categories on which it has been trained. Due to the enormous volume and variety of data collected by the IoT devices, and the attacks to which the networks are prone, the supervised techniques are of limited use in practical applications. We propose a novel threat identification strategy using the Clustering based Variational Autoencoder (CVA) for detecting threats (anomalous behaviors) in IoT networks. The proposed strategy uses an unsupervised technique and hence the model needs to be trained only on traffic under benign scenarios for the identification of threats. Also, the proposed technique is scalable to accommodate a large number of devices.Item Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Social Surveillance Systems(IEEE, 2023-01) Chamola, VinayReal-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.Item Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks(IEEE, 2022-09) Chamola, VinayThere 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.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.