SPN: A Method of Few-Shot Traffic Classification With Out-of-Distribution Detection Based on Siamese Prototypical Network

Miao, Gongxun; Wu, Guohua; Zhang, Zhen; Tong, Yongjie; Lu, Bing

doi:10.1109/access.2023.3325065

Cited by 3 publications

(2 citation statements)

References 35 publications

(34 reference statements)

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“…Feature engineering and LightGBMbased detection achieve high accuracy and a significant reduction in false alarms. Traffic classification plays a crucial role in cyber security, as noted in [12]. SPN is a quick traffic multi-classification tool that detects out-ofdistribution.…”

Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.

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Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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“…The embedding and use of FSL in IDS may aid in overcoming the issues of data collection, rapidly training the model by using only a few samples, and harnessing the ability to detect novel attacks. The advantages of few-shot learning classification include its strong adaptability, low overheads in terms of resources, and the ability to transfer across various scenarios easily [10]. However, existing FSL intrusion detection systems models are complex and may be inappropriate for low-power networks such as IoT, and their performance is still not satisfying.…”

Section: Few-shot Learningmentioning

confidence: 99%

Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection

Althiyabi,

Ahmad,

Alassafi

2024

Mathematics

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Recently, the number of Internet of Things (IoT)-connected devices has increased daily. Consequently, cybersecurity challenges have increased due to the natural diversity of the IoT, limited hardware resources, and limited security capabilities. Intrusion detection systems (IDSs) play a substantial role in securing IoT networks. Several researchers have focused on machine learning (ML) and deep learning (DL) to develop intrusion detection techniques. Although ML is good for classification, other methods perform better in feature transformation. However, at the level of accuracy, both learning techniques have their own certain compromises. Although IDSs based on ML and DL methods can achieve a high detection rate, the performance depends on the training dataset size. Incidentally, collecting a large amount of data is one of the main drawbacks that limits performance when training datasets are lacking, and such methods can fail to detect novel attacks. Few-shot learning (FSL) is an emerging approach that is employed in different domains because of its proven ability to learn from a few training samples. Although numerous studies have addressed the issues of IDSs and improved IDS performance, the literature on FSL-based IDSs is scarce. Therefore, an investigation is required to explore the performance of FSL in IoT IDSs. This work proposes an IoT intrusion detection model based on a convolutional neural network as a feature extractor and a prototypical network as an FSL classifier. The empirical results were analyzed and compared with those of recent intrusion detection approaches. The accuracy results reached 99.44%, which shows a promising direction for involving FSL in IoT IDSs.

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Malicious Data Classification in Packet Data Network Through Hybrid Meta Deep Learning

Tapu,

Shopnil,

Tamanna

et al. 2023

IEEE Access

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SPN: A Method of Few-Shot Traffic Classification With Out-of-Distribution Detection Based on Siamese Prototypical Network

Cited by 3 publications

References 35 publications

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection

Malicious Data Classification in Packet Data Network Through Hybrid Meta Deep Learning

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