Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Venkateswaran, N.; Umadevi, K. S.

doi:10.32604/csse.2022.021217

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Supervised learning, a popular approach that uses labeled data for training, has been widely used in malware detection and classification. Various supervised learning techniques, including as decision trees [33], support vector machines [34], and deep neural networks [35], have been investigated in the context of this job. However, the application of these technologies frequently necessitates a large quantity of labeled data for each malware strain [36], resulting in timeconsuming and expensive data collecting methods [37].…”

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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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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“…The methodologies designed for object detection to help visually-challenged people focus on directionbased access to freedom concerning mobility, orientation, barrier mitigation, and analysis. In a general context, the scenario deals with generating information regarding the environment and surrounding in textual format for visually-challenged people [8]. However, the methods still suffer from detecting complex scenarios that require multiple object detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Devi¹,

Albraikan²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.

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“…The best-known wrapper algorithms are the genetic algorithm, recursive feature elimination, and sequential feature selection. The authors of [68] also present a hybrid version of feature selection method that uses both filter and wrapper techniques. The embedded method combines the efficiency of unsupervised and supervised methods.…”

Section: Feature Selectionmentioning

confidence: 99%

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Protić,

Stanković,

Prodanović

et al. 2023

Electronics

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Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems with supervised learning are related to the large amount of data required to train the classifiers. Feature selection can be used to reduce datasets. The goal of feature selection is to select a subset of relevant input features to optimize the evaluation and improve performance of a given classifier. Feature scaling normalizes all features to the same range, preventing the large size of features from affecting classification models or other features. The most commonly used supervised machine learning models, including decision trees, support vector machine, k-nearest neighbors, weighted k-nearest neighbors and feedforward neural network, can all be improved by using feature selection and feature scaling. This paper introduces a new feature scaling technique based on a hyperbolic tangent function and damping strategy of the Levenberg–Marquardt algorithm.

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Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Cited by 8 publications

References 29 publications

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

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

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

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