Handling Labeled Data Insufficiency: Semi-supervised Learning with Self-Training Mixup Decision Tree for Classification of Network Attacking Traffic

Hou, Yubo; Teo, Sin G.; Chen, Zhenghua; Wu, Min; Kwoh, Chee Keong; Truong-Huu, Tram

doi:10.1109/tdsc.2022.3195534

Cited by 7 publications

(3 citation statements)

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“…There are two main categories of NODS; supervised and unsupervised. If a system utilizes both supervised and unsupervised features, it is classified as semi-supervised [32] Supervised NODSs use labeled data to train a model that can then be used to detect outliers in new, unlabeled data sets. These systems are based on supervised learning techniques, such as decision trees, neural networks, and support vector machines (SVMs) [33].…”

Section: Network Outlier Detection System (Nods)mentioning

confidence: 99%

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An Efficient Support Vector Machine Algorithm Based Network Outlier Detection System

Alghushairy,

Alsini,

Alhassan

et al. 2024

IEEE Access

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With the increase of cyber-attacks and security threats in the recent decade, it is necessary to safeguard sensitive data and provide robust protection to information systems and computer networks. In this paper, an anomaly-based network outlier detection system (NODS) is proposed and optimized to check and classify the incoming network traffic stream's behaviours that affect the computer networks. The proposed NODS has high classification efficiency. Network connection events classified as outliers are reported to the network admin to drop and block its packets. The NSL-KDD and CICIDS2017 intrusion datasets were employed to build the proposed system and test its detection capabilities. Sequential scenarios were implemented to optimize the system's effectiveness. Network features were normalized by min-max and Z-Score approaches, while the relevant features were selected individually by the principal component analysis (PCA) and correlated features selection (CFS) techniques. Support vector machine (SVM) and Gaussian Naive Bayes (GNB) algorithms are used to build the detection model, while the Genetic algorithm (GA) was employed to tune their control parameters. The obtained evaluation results proved that the proposed SVM based NODS is characterized by low false alarms and detection time as well as high classification accuracy. Furthermore, a comparative analysis was conducted with other existing techniques, and the results obtained demonstrate the effectiveness of the proposed SVM-IDS

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Section: Network Outlier Detection System (Nods)mentioning

confidence: 99%

“…These techniques are used to identify patterns in the data that indicate the presence of outliers. In decision tree-based NODSs, the data is split into multiple nodes based on the value of a certain feature [32,34]. The nodes are then classified as outliers or inliers.…”

Section: Network Outlier Detection System (Nods)mentioning

confidence: 99%

An Efficient Support Vector Machine Algorithm Based Network Outlier Detection System

Alghushairy,

Alsini,

Alhassan

et al. 2024

IEEE Access

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“…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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Tackling Evolving Botnet Threats: A Gradual Self-Training Neural Network Approach

Lo,

Chang,

et al. 2024

IEEE Access

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Handling Labeled Data Insufficiency: Semi-supervised Learning with Self-Training Mixup Decision Tree for Classification of Network Attacking Traffic

Cited by 7 publications

References 0 publications

An Efficient Support Vector Machine Algorithm Based Network Outlier Detection System

An Efficient Support Vector Machine Algorithm Based Network Outlier Detection System

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

Tackling Evolving Botnet Threats: A Gradual Self-Training Neural Network Approach

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