A Feedback Semi-Supervised Learning With Meta-Gradient for Intrusion Detection

Cai, Shaokang; Han, Dezhi; Li, Dun

doi:10.1109/jsyst.2022.3197447

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…In this paper, an intrusion detection model (RESNETCCN) is proposed that fuses traffic detection requirements. In our future work, we will introduce more new ideas such as blockchain cryptography [8], [18], [9], [19], [16], alliance chain [36], [7], [20], visual Q&A [5], [28], transformer [21], panoramic image [17], reinforcement learning [3], internet of things [23], [24], shared data [6] in our model.We will continue to explore network intrusion detection methods in more areas such as unsupervised and semi-supervised [2] areas for network anomalous traffic data detection. In addition, we also try to introduce new evaluation metrics and establish systematic evaluation methods of intrusion detection.…”

Section: Discussionmentioning

confidence: 99%

RESNETCNN: An abnormal network traffic flows detection model

Han

Cui

et al. 2023

ComSIS

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Intrusion detection is an important means to protect system security by detecting intrusions or intrusion attempts on the system through operational behaviors, security logs, and data audit. However, existing intrusion detection systems suffer from incomplete data feature extraction and low classification accuracy, which affects the intrusion detection effect. To this end, this paper proposes an intrusion detection model that fuses residual network(RESNET) and parallel cross-convolutional neural network, called RESNETCCN. RESNETCNN can efficiently learn various data stream features through the fusion of deep learning and convolutional neural network (CNN), which improves the detection accuracy of abnormal data streams in unbalanced data streams, moreover, the oversampling method into the data preprocessing, to extract multiple types of unbalanced data stream features at the same time, effectively solving the problems of incomplete data feature extraction and low classification accuracy of unbalanced data streams. Finally, three improved versions of RESNETCNN networks are designed to meet the requirements of different traffic data processing, and the highest detection accuracy reaches 99.98% on the CICIDS 2017 dataset and 99.90% on the ISCXIDS 2012 dataset.

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Section: Discussionmentioning

confidence: 99%

RESNETCNN: An abnormal network traffic flows detection model

Han

Cui

et al. 2023

ComSIS

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“…These approaches strive to give intrusion detection systems the capacity to identify and categorize various intrusion events proactively. These improvements, by leveraging the power of machine learning, contribute to the establishment of more robust and effective defense mechanisms, providing increased security in the never-ending war against invasions across varied computing platforms [32]. Supervised learning, a popular approach that uses labeled data for training, has been widely used in malware detection and classification.…”

Section: State Of the Artmentioning

confidence: 99%

“…For training, semi-supervised learning [32], a technique used in intrusion detection and classification, blends labeled and unlabeled data [41]. This strategy seeks to capitalize on the advantages of both supervised and unsupervised learning approaches [42].…”

Section: State Of the Artmentioning

confidence: 99%

A novel approach to intrusion detection using zero-shot learning hybrid partial labels

Raza,

Shaikh,

Akhtar

et al. 2024

Mehran Univ. res. j. eng. technol.

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Computer networks have become the backbone of our interconnected world in today's technologically driven landscape. Unauthorized access or malicious activity carried out by threat actors to acquire control of network resources, exploit vulnerabilities, or undermine system integrity are examples of network intrusion. ZSL(Zero-Shot Learning) is a machine learning paradigm that addresses the problem of detecting and categorizing objects or concepts that were not present in the training data. . Traditional supervised learning algorithms for intrusion detection frequently struggle with insufficient labeled data and may struggle to adapt to unexpected assault patterns. In this article We have proposed a unique zero-shot learning hybrid partial label model suited to a large image-based network intrusion dataset to overcome these difficulties. The core contribution of this study is the creation and successful implementation of a novel zero-shot learning hybrid partial label model for network intrusion detection, which has a remarkable accuracy of 99.12%. The suggested system lays the groundwork for future study into other feature selection techniques and the performance of other machine learning classifiers on larger datasets. Such research can advance the state-of-the-art in intrusion detection and improve our ability to detect and prevent the network attacks. We hope that our research will spur additional research and innovation in this critical area of cybersecurity.

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