Feature Selection Algorithm Characterization for NIDS using Machine and Deep learning

Verma, Jyoti; Bhandari, Abhinav; Singh, Gurpreet

doi:10.1109/iemtronics55184.2022.9795709

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Recent research on NIDS has partly focused on FS (feature selection) techniques and optimization techniques to select the most important features. Jyoti et al [21] provide an overview of feature selection methods used in NIDS, describe existing feature selection frameworks and classifications for NIDS, and argue that there is an urgent need for new FS to meet the demands of big data. Su et al [22] consider the feature redundancy of FS and propose a learning automata approach to select optimal and salient features for network traffic intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement

Wang¹,

Liu

Zheng

et al. 2023

Applied Sciences

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Feature loss in IoT scenarios is a common problem. This situation poses a greater challenge in terms of real-time and accuracy for the security of intelligent edge computing systems, which also includes network security intrusion detection systems (NIDS). Losing some packet information can easily confuse NIDS and cause an oversight of security systems. We propose a novel network intrusion detection framework based on an improved neural network. The new framework uses 23 subframes and a mixer for multi-classification work, which improves the parallelism of NIDS and is more adaptable to edge networks. We also incorporate the K-Nearest Neighbors (KNN) algorithm and Genetic Algorithm (GA) for feature selection, reducing parameters, communication, and memory overhead. We named the above system as Combinatorial Multi-Classification-NIDS (CM-NIDS). Experiments demonstrate that our framework can be more flexible in terms of the parameters of binary classification, has a fairly high accuracy in multi-classification, and is less affected by feature loss.

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Section: Related Workmentioning

confidence: 99%

A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement

Wang¹,

Liu

Zheng

et al. 2023

Applied Sciences

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“…By combining digital image processing methods [12,13] and machine and deep learning algorithms [14][15][16] with images, crack detection can be performed in numerous ways, as described in this section. Fu Tao et al [17] conducted a thorough analysis of the body of research on crack identification using image processing methods.…”

Section: Literature Reviewmentioning

confidence: 99%

ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

et al. 2023

Self Cite

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Crack detection on roads is essential nowadays because it has a significant impact on ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more effective and precise crack detection techniques. A safer road network and a better driving experience for all road users can result from the implementation of the ROAD (Robotics-Assisted Onsite Data Collecting) system for spotting road cracks using deep learning and robots. The suggested solution makes use of a robot vision system’s capabilities to gather high-quality data about the road and incorporates deep learning methods for automatically identifying cracks. Among the tested algorithms, Xception stands out as the most accurate and predictive model, with an accuracy of over 90% during the validation process and a mean square error of only 0.03. In contrast, other deep neural networks, such as DenseNet201, InceptionResNetV2, MobileNetV2, VGG16, and VGG19, result in inferior accuracy and higher losses. Xception also achieves high accuracy and recall scores, indicating its capability to accurately identify and classify different data points. The high accuracy and superior performance of Xception make it a valuable tool for various machine learning tasks, including image classification and object recognition.

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Network Intrusion Detection System Employing Big Data and Intelligent Learning Methods

Verma

Bhandari

Singh

2022

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)

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Feature Selection Algorithm Characterization for NIDS using Machine and Deep learning

Cited by 5 publications

References 25 publications

A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement

A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement

ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

Network Intrusion Detection System Employing Big Data and Intelligent Learning Methods

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