Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree, A.; Krishnamoorthi, M.

doi:10.5755/j01.itc.51.4.31818

Cited by 11 publications

(5 citation statements)

References 27 publications

(38 reference statements)

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“…Feature engineering is considered an effective solution, and recent research has successfully utilized feature engineering to improve the performance and efficiency of IDS. Padmashree et al [26] utilized a recursive feature elimination decision tree based on the Pearson correlation method, which can eliminate irrelevant features, improve resource utilization, and reduce the complexity of IDS. Alzaqebah et al [27] used the MGWO to select the optimal feature set, excluding irrelevant and noisy features, thereby enhancing the efficiency and performance of the IDS.…”

Section: Discussionmentioning

confidence: 99%

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CBF-IDS: Addressing Class Imbalance Using CNN-BiLSTM with Focal Loss in Network Intrusion Detection System

Peng,

Wu,

Xiao

2023

Applied Sciences

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The importance of network security has become increasingly prominent due to the rapid development of network technology. Network intrusion detection systems (NIDSs) play a crucial role in safeguarding networks from malicious attacks and intrusions. However, the issue of class imbalance in the dataset presents a significant challenge to NIDSs. In order to address this concern, this paper proposes a new NIDS called CBF-IDS, which combines convolutional neural networks (CNNs) and bidirectional long short-term memory networks (BiLSTMs) while employing the focal loss function. By utilizing CBF-IDS, spatial and temporal features can be extracted from network traffic. Moreover, during model training, CBF-IDS applies the focal loss function to give more weight to minority class samples, thereby mitigating the impact of class imbalance on model performance. In order to evaluate the effectiveness of CBF-IDS, experiments were conducted on three benchmark datasets: NSL-KDD, UNSW-NB15, and CIC-IDS2017. The experimental results demonstrate that CBF-IDS outperforms other classification models, achieving superior detection performance.

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Section: Discussionmentioning

confidence: 99%

“…Padmashree et al [26] introduced a feature selection technique that utilized a recursive feature elimination decision tree based on Pearson correlation. In this study, the model was applied to analyze the BoT-IoT dataset, leading to the identification of nine highly correlated features.…”

Section: Related Workmentioning

confidence: 99%

CBF-IDS: Addressing Class Imbalance Using CNN-BiLSTM with Focal Loss in Network Intrusion Detection System

Peng,

Wu,

Xiao

2023

Applied Sciences

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“…Padmashree and Krishnamoorthi [21] introduced an effective feature selection with the feature fusion method for the recognition of intruders in IoT. From the preprocessed data, the high-order statistical features are chosen according to the presented Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) method.…”

Section: Literature Surveymentioning

confidence: 99%

Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment

Ragab,

M. Alshammari,

Maghrabi

et al. 2023

Mathematics

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The Internet of Things (IoT) refers to the network of interconnected physical devices that are embedded with software, sensors, etc., allowing them to exchange and collect information. Although IoT devices have several advantages and can improve people’s efficacy, they also pose a security risk. The malicious actor frequently attempts to find a new way to utilize and exploit specific resources, and an IoT device is an ideal candidate for such exploitation owing to the massive number of active devices. Especially, Distributed Denial of Service (DDoS) attacks include the exploitation of a considerable number of devices like IoT devices, which act as bots and transfer fraudulent requests to the services, thereby obstructing them. There needs to be a robust system of detection based on satisfactory methods for detecting and identifying whether these attacks have occurred or not in a network. The most widely used technique for these purposes is artificial intelligence (AI), which includes the usage of Deep Learning (DL) and Machine Learning (ML) to find cyberattacks. The study presents a Piecewise Harris Hawks Optimizer with an Optimal Deep Learning Classifier (PHHO-ODLC) for a secure IoT environment. The fundamental goal of the PHHO-ODLC algorithm is to detect the existence of DDoS attacks in the IoT platform. The PHHO-ODLC method follows a three-stage process. At the initial stage, the PHHO algorithm can be employed to choose relevant features and thereby enhance the classification performance. Next, an attention-based bidirectional long short-term memory (ABiLSTM) network can be applied to the DDoS attack classification process. Finally, the hyperparameter selection of the ABiLSTM network is carried out by the use of a grey wolf optimizer (GWO). A widespread simulation analysis was performed to exhibit the improved detection accuracy of the PHHO-ODLC technique. The extensive outcomes demonstrated the significance of the PHHO-ODLC technique regarding the DDoS attack detection technique in the IoT platform.

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“…It may be used in machine learning for both classification and regression problems. It is based on the concept of ensemble learning, which is the process of combining several classifiers to solve a complex problem and improve the model's performance [14,15].…”

Section: Rfe With Random Forestmentioning

confidence: 99%

A Framework for Blended Sub Feature Engineering for Chronic Disease Prediction Using in-Memory Computing

Raghavendra¹,

Mahesh²,

Rao³

2022

RIA

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Chronic diseases are among the most frequent major health concerns. Early detection of chronic illnesses can help to avoid or lessen their repercussions, potentially lowering death rates. It's an innovative technique to use machine learning algorithms to identify dangerous variables. The problem with existing feature selection procedures is that each method gives a unique collection of features that influence model validity, and current methods are incapable of performing effectively on large multidimensional datasets. We would want to present a novel model that uses a feature selection strategy to choose ideal features from large multidimensional data sets to deliver credible forecasts of chronic diseases while preserving the uniqueness of the data. To assure the success of our proposed model, we used balanced classes by applying hybrid balanced class sampling methods to the original dataset, as well as methods to provide valid data for the training model, characterization and data conversion are required. Our model was run and assessed on datasets with binary and multi-valued classifications. We utilized a variety of datasets (Parkinson's disease, arrhythmia, breast cancer, kidney disease, and diabetes). To select suitable features, the hybrid feature model is used, which includes six ensemble models and involves voting on attributes. The accuracy of the original dataset before applying the framework is recorded and compared to the accuracy of the reduced set of characteristics. The findings are given individually to allow for comparisons. We can conclude from the results that our proposed model performed best on multi-valued class datasets rather than binary class characteristics.

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Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Cited by 11 publications

References 27 publications

CBF-IDS: Addressing Class Imbalance Using CNN-BiLSTM with Focal Loss in Network Intrusion Detection System

CBF-IDS: Addressing Class Imbalance Using CNN-BiLSTM with Focal Loss in Network Intrusion Detection System

Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment

A Framework for Blended Sub Feature Engineering for Chronic Disease Prediction Using in-Memory Computing

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