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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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Leveraging Artificial Neural Networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

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2025 IEEE International Conference on Computational, Communication and information technology, ICCCIT 2025

作者： Goel, Aadya Mishra, Pallavi Bhatia, Rachna School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331512965

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial Neural Networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer Image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses. © 2025 IEEE.

关键词： Lung cancer

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QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Active learning Online learning Malicious traffic Concept drift Data imbalance

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Water Quality Monitoring and Control in Urban Areas in Real-Time via IoT and Mobile Applications 3

Water Quality Monitoring and Control in Urban Areas in Real-...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohanasundaram, R. Sagar, Raghav Kumar Khandelwal, Animan Upadhyay, Diwas Poddar, Harshit Rajagopal, Sivakumar Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The presence of impurities in water resulting from the expansion of industrial activities and the disregard of regulations aimed at protecting the environment presents considerable threats to the well-being of the general population. The establishment of reliable access to uncontaminated drinking water assumes paramount importance in the realm of disease prevention. The continuous monitoring of the quality of water in real-time serves as an indispensable measure for ensuring its safety. The objective of this article is to introduce a system that capitalizes on the Internet of Things (IoT) to automate the distribution and management of water resources within urban areas. By making use of embedded technology, this system allows for the uninterrupted surveillance and regulation of water quality. To further streamline the management process, an application compatible with the Android operating system has been developed to promptly notify users of any irregularities, thereby enabling remote control. Moreover, a web-based application has been designed to cater to the needs of iOS users, thereby broadening accessibility. This comprehensive approach not only heightens the level of user awareness but also provides them with greater control over their water supply, thereby enhancing public health outcomes. © 2024 IEEE.

关键词： Internet of things

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Integrating Advanced Neural Network Architectures for Effective Skin Disorder Classification

Integrating Advanced Neural Network Architectures for Effect...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Shreya, A. Gautam, G. Dhivyaa, C.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350374933

Skin illnesses, such as vitiligo, provide considerable hurdles in proper diagnosis due to their resemblance to other conditions, necessitating time-consuming examinations by medical personnel. Taking advantage of advances in deep learning and medical image processing, this study suggests a novel way to swiftly and accurately classify these disorders. The proposed model uses many convolutional layers to extract detailed features from skin pictures, followed by Long Short-Term Memory (LSTM) layers to capture temporal dependencies and improve feature representation. This hybrid architecture consists of six convolutional layers that gradually increase in depth and spatial reduction via max-pooling, culminating in two LSTM layers and three fully connected layers for precise classification. By combining CNNs’ spatial feature extraction powers with LSTMs’ sequence processing skills, our model obtains a high training accuracy of 93.40% and precision of 94.48%, proving its usefulness in distinguishing between vitiligo and scar images. This approach shows potential for improving diagnostic accuracy and efficiency in clinical settings, resulting in better patient outcomes and healthcare delivery. © 2024 IEEE.

关键词： Convolutional neural networks

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An Experimental Study on the Deviations in Performance of FNNS and CNNS in the Realm of Grayscale Adversarial Images

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Journal of engineering science and technology Review 2023年第3期16卷 66-73页

作者： Mathew, D. A. Steve Shree, N. Durga Chowdhary, Chiranji Lal Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore India Data Science and Applications Indian Institute of Technology Madras Chennai India Botho University Botswana

Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. It is observed that neural networks are prone to having lesser accuracy in the classification of images with noise perturbation. The following study resulted in inferences to establish that CNNs are extremely vulnerable at predicting noisy images while Feed-forward Neural Networks, FNNs are least affected due to noise perturbation, maintaining their accuracy almost undisturbed. FNNs showcase better classification accuracy when tested with noise-intensive, single-channelled images that are just sheer noise to human vision. The hand-written digit images from the MNIST dataset t are classified using the architectures of FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6, and 8 convolutions, which provide the stated experimental inferences. Deviations in the performances of these architectures analyzed systematically propose that FNNs stand out to show a classification accuracy of more than 85%, irrespective of the intensity of noise and CNNs witness a trend in the reduction in speed of classification accuracy against increasing noise intensities. Correlation analysis and mathematical modelling of the accuracy trends act as roadmaps to picture that the change in the speed of classification accuracy against increasing noise intensities for CNN with 8 convolutions is half of that of the rest of the CNNs. This experimental study is a step to quantify the performance of deep learning image classification models in the context of adversarial images. © 2023 school of science, IHU. All rights reserved.

关键词： Convolution

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A Novel Mixed Precision Distributed TPU GAN for Accelerated Learning Curve

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computer systems science & engineering 2023年第7期46卷 563-578页

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Deep neural networks are gaining importance and popularity in applications and *** to the enormous number of learnable parameters and datasets,the training of neural networks is computationally *** and distributed computation-based strategies are used to accelerate this training *** Adversarial Networks(GAN)are a recent technological achievement in deep *** generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous ***,a GAN is trained on a single *** deep learning accelerator designs are challenged by the unique properties of GAN,like the enormous computation stages with non-traditional convolution *** work addresses the issue of distributing GANs so that they can train on datasets distributed over many TPUs(Tensor Processing Unit).Distributed learning training accelerates the learning process and decreases computation *** this paper,the Generative Adversarial Network is accelerated using the distributed multi-core TPU in distributed data-parallel synchronous *** adequate acceleration of the GAN network,the data parallel SGD(Stochastic Gradient Descent)model is implemented in multi-core TPU using distributed TensorFlow with mixed precision,bfloat16,and XLA(Accelerated Linear Algebra).The study was conducted on the MNIST dataset for varying batch sizes from 64 to 512 for 30 epochs in distributed SGD in TPU v3 with 128×128 systolic *** extensive batch technique is implemented in bfloat16 to decrease the storage cost and speed up floating-point *** accelerated learning curve for the generator and discriminator network is *** training time was reduced by 79%by varying the batch size from 64 to 512 in multi-core TPU.

关键词： Data parallel distributed model generative model learning curve mixed precision

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Enhanced Malware Image Classification through Ensemble model 3

Enhanced Malware Image Classification through Ensemble model

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kotha, Eshwar Palivela, Lakshmi Harika Begum, Afruza Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Classification of malwares and viruses is a very important work in the cyber security field to protect the computers and systems from threats and attacks. In this paper, we proposed a novel approach for the classification of the malwares by combining 2 models EfficientNet B0 and EffiCBNet models. Here we have used Malimg dataset which is available in Kaggle and it contains around 9,339 malware images and these malwares are from 25 different families, for evaluating the models. EffcientNetB0 is a Convolution Neural Network (CNN) model, which showed great and high results on various image classification works. We used transfer learning to fine tune the EfficientNet model;whereas the proposed EffiCBNet model is a deep learning model which contains of convolution layers and batch normalization layers. The results have shown that the proposed model has given the highest accuracy of 99.70 on the dataset we have used, exceeding all other models. The proposed model is the combination of EfficientNet and EffiCBNet, which showed great results for the classification. The proposed model can also be used in real time applications for detecting malware attacks and threats © 2024 IEEE.

关键词： Image classification

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Lane Detection and Traffic Sign Detection for Autonomous Driving systems 2

Lane Detection and Traffic Sign Detection for Autonomous Dri...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Biswas, Aishi Chowdhury, Anushka Roy Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781837242672

In the realm of autonomous driving, this study focuses on two critical tasks for scene understanding: lane detection and traffic sign detection. These tasks are essential to ensuring safe and reliable navigation in self-driving systems. A combination of traditional computer vision techniques and modern deep learning methods are used to achieve accurate detection and analysis of lanes and traffic signs in real-world driving scenarios. For lane detection, we utilize edge detection and the Hough Transform, while for traffic sign detection, we apply machine learning algorithms to recognize and classify various signs. This hybrid approach demonstrates the potential of integrating classical computer vision algorithms with deep learning models to enhance the perception capabilities of autonomous vehicles. The model achieves a balanced performance, with accuracy metrics ranging from 80-90% across both tasks, indicating a robust ability to detect lanes and signs effectively under varied conditions. This work serves as a foundation for developing more advanced scene understanding systems in autonomous driving technology. In the future, we aim to enhance the model's adaptability through continuous learning and incorporating additional sensory data, such as LiDAR and radar. Additionally, expanding the dataset and testing the models in diverse environmental conditions, including different lighting and weather scenarios, will further improve the robustness and reliability. © The Institution of engineering & technology 2024.

关键词： Hough transforms

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