Transfer-learning-based Network Traffic Automatic Generation Framework

Li, Yanjie; Liu, Tianrui; Jiang, Dongxiao; Meng, Tao

doi:10.1109/icsp51882.2021.9408767

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…Transfer learning offers a transformative approach to object detection on customized image datasets by leveraging the capabilities of pre-trained models to enhance accuracy for new, task-specific challenges [14]. By fine-tuning models [15] that have been pre-trained on large, comprehensive datasets like ImageNet, researchers can jumpstart the object detection process on their specialized datasets.…”

Section: Transfer Learningmentioning

confidence: 99%

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

Dang,

Ma,

et al. 2024

AJST

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Visually impaired individuals face substantial challenges in kitchens, where identifying objects accurately is crucial yet difficult due to the complexity and variability of the environment. Traditional object detection1 methods fall short in these settings, struggling with the assortment of items. This research highlights the need for advanced, kitchen-specific solutions that leverage deep learning to improve detection accuracy and offer real-time, interactive guidance through speech technologies. By focusing on the unique demands of kitchen environments, the proposed system aims to significantly enhance the autonomy and safety of visually impaired users, presenting a notable advancement in assistive technology. The effectiveness of this approach is assessed by its ability to accurately identify kitchen items for visually impaired individuals.

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Section: Transfer Learningmentioning

confidence: 99%

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

Dang,

Ma,

et al. 2024

AJST

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“…The problem can be thought two-fold as it connects computer vision and natural language processing: 1) use an encoder architecture (CNN, Transformer) [32] to process the image; 2) use a decoder to decode the encoded image representation into sentences. [16] The model is trained in a supervised learning pattern [11] with some existing image to caption (ground truth) pairs. In the training stage we maximize the similarity of generated caption with ground truth, [14] and in the testing stage we decode the encoded image representation directly to obtain the outcome.…”

Section: Image Captioningmentioning

confidence: 99%

Image Captioning in News Report Scenario

Liu,

Cai,

et al. 2024

AJST

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Image captioning[1] strives to generate pertinent captions for specified images[56], situating itself at the crossroads of Computer Vision (CV) [2] and Natural Language Processing (NLP)[54]. This endeavor is of paramount importance with far-reaching applications in recommendation systems, news outlets, social media, and beyond. Particularly within the realm of news reporting, captions are expected to encompass detailed information, such as the identities of celebrities captured in the images. However, much of the existing body of work primarily centers around understanding scenes and actions.[3] In this paper, we explore the realm of image captioning specifically tailored for celebrity photographs, illustrating its broad potential for enhancing news industry practices. This exploration aims to augment automated news content generation, thereby facilitating a more nuanced dissemination of information.[57] Our endeavor shows a broader horizon, enriching the narrative in news reporting through a more intuitive image captioning framework.

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“…The transfer learning (TL) model proposed by Taghiyarrenani et al, [30] for intrusion detection shows more efficient performance in both labeled and unlabeled data. To extract the attack invariant from the existing attack data set and transfer the knowledge to the target network system, Yanjie et al, [31] proposed a framework for transfer-learning-based network flow generation for deeplearning-based IDS.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

et al. 2023

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Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to move IoT devices to a centralized server with higher memory capacity and a more robust management interface. Hence, a larger volume of data will be transmitted, resulting in more network security issues. Cloud IoT offers more advantages for deploying and managing IoT systems through minimizing response delays, optimal latency, and effective network load distribution. As a result, sophisticated network attack strategies are deployed to leverage the vulnerabilities in the extensive network space and exploit user information. Several attempts have been made to provide network intrusion detection systems (IDS) to the cloud IoT interface using machine learning and deep learning approaches on dedicated IDS datasets. This paper proposes a transfer learning IDS based on the Convolutional Neural Network (CNN) architecture that has shown excellent results on image classification. We use five pre-trained CNN models, including VGG16, VGG19, Inception, MobileNet, and EfficientNets, to train on two selected datasets: CIC-IDS2017 and CSE-CICIDS2018. Before the training, we carry out preprocessing, imbalance treatment, dimensionality reduction, and conversion of the feature vector into images suitable for the CNN architecture using Quantile Transformer. Three best-performing models (InceptionV3, MobileNetV3Small, and EfficientNetV2B0) are selected to develop an ensemble model called efficient-lightweight ensemble transfer learning (ELETL-IDS) using the model averaging approach. On evaluation, the findings show that the ELETL-IDS outperformed existing state-of-the-art proposals in all evaluation metrics, reaching 100% in accuracy, precision, recall, and F-score. We use Matthew's Correlation Coefficient (MCC) to validate this result and compared it to the AUC-ROC, which maintained an exact value of 0.9996. To this end, our proposed model is lightweight, efficient, and reliable enough to be deployed in cloud IoT systems for intrusion detection.

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Transfer-learning-based Network Traffic Automatic Generation Framework

Cited by 8 publications

References 3 publications

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

Image Captioning in News Report Scenario

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

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