A Hybrid Intrusion Detection Method using Improved Stacking Ensemble Algorithm and False Positive Elimination Strategy for CBTC

Yin, Binyu; Bu, Bing; Gao, Bing; Li, Qichang

doi:10.1109/itsc55140.2022.9921964

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…In [183], the proposal addresses the challenge of constructing an intrusion detection system with high true and low false positive rates for recognizing cyber-attacks. They present an ensemble model that utilizes weighted classification probabilities derived from base classifiers.…”

Section: H Work Published In 2022mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Section: H Work Published In 2022mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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show abstract

“…Their experiments were done on various combinations of full and partial-scans of circuits, and obtained a false positive ratio of 3.89% and 3.31% for full and partial scans of circuits. In [24], an improved stacking ensemble algorithm was proposed to enhance the true positive rate of a intrusion detection system (IDS). Their Hybrid IDS was tested and their results showed that the method was superior than the compared techniques, in terms of True and False positives.…”

Section: Related Workmentioning

confidence: 99%

Reducing Overconfidence Predictions in Autonomous Driving Perception

Melotti,

Premebida,

Bird

et al. 2022

IEEE Access

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Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object recognition is explored by using multisensory and multimodality approaches, with the intention of reducing the false positive rate (FPR). The reduction of the FPR becomes increasingly important in perception systems since the misclassification of an object can, depending on the circumstances, potentially cause accidents. In particular, this work presents a strategy through Bayesian inference to reduce the FPR considering the likelihood function as a cumulative distribution function from Gaussian kernel density estimations, and the prior probabilities as cumulative functions of normalized histograms. The validation of the proposed methodology is performed on the KITTI dataset using deep networks (DenseNet, NasNet, and EfficientNet), and recent 3D point cloud networks (PointNet, and PointNet++), by considering three object-categories (cars, cyclists, pedestrians) and the RGB and LiDAR sensor modalities.

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A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

Lu,

Zhao,

Song

et al. 2024

Cluster Comput

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A Hybrid Intrusion Detection Method using Improved Stacking Ensemble Algorithm and False Positive Elimination Strategy for CBTC

Cited by 5 publications

References 12 publications

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Reducing Overconfidence Predictions in Autonomous Driving Perception

A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

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