Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset

Talita, Aini Suri; Nataza, O. S.; Rustam, Zuherman

doi:10.1088/1742-6596/1752/1/012021

Cited by 24 publications

(11 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was tested with comparison to other CNN-based methods, and the outcomes showed that the application of PSO has a significant impact on the performance of the CNN. The PSO was also adopted in other IDS systems, such as [ 35 , 36 , 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani

Dahou

Al-qaness

et al. 2021

Sensors

118

View full text Add to dashboard Cite

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

show abstract

Section: Related Workmentioning

confidence: 99%

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani

Dahou

Al-qaness

et al. 2021

Sensors

118

View full text Add to dashboard Cite

show abstract

“…The processing of subsets results in feature optimization, the optimized features are applied over the random forest algorithm in the form of different sized feature sets, such as 15, 20, and 35, and found that the random forest results into a better classification performance. An IDS for advanced metering infrastructure (AMI) in grid systems has been done by [7], as they are the target due to its two-way communication ability over internetwork. To overcome the problem of considering global and temporal characteristics of malicious information, the long short-term memory (LSTM) networks, based on the convolution neural network (CNN) algorithm, is used, which is fused with cross layer features over AMI IDS.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

et al. 2022

View full text Add to dashboard Cite

Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.

show abstract

“…e intrusion detection system can monitor the traffic on the network and the log data stored in the computer and judge whether there is a malicious attack through analyzing various characteristics of the data [16]. e main components of the proposed intrusion detection system are hardware and software.…”

Section: Intrusion Detection Technologymentioning

confidence: 99%

A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning

Liu

Lin

2022

Scientific Programming

View full text Add to dashboard Cite

At present, the quality of material life and richness of the Chinese residents have been improved. Some people have high requirements for quality of their personal lives and put forward the problem of health preservation. Honey, as a nutritious food, is deeply loved by people. There are a large number of trace elements in honey, such as VC, VA, VD, VB1, and VB2. The honey from the Chinese honeybee has a very high nutritional value and plays an important role in the pollination and reproduction of some plants. Therefore, the Chinese honeybee plays a very significant role in the ecological environment. Moreover, it is protected as the main species resource of the country, which also fully proves the importance of the Chinese honeybee. Chinese bees can survive in various ecological and geographical environments in China and have strong heat as well as cold resistance. They can survive in the hot environment in the south and withstand the dynamics of severe cold in the north. At the same time, they can make full use of a small number of honey sources and have strong resistance to a variety of diseases and pests. In fact, there will be a variety of insect invasion problems in the beehive culture of Chinese bees, and it is necessary to accurately detect various diseases and pests during the breeding of Chinese bees. However, there are a large amount of insect invasion and various disease sources in the breeding stage of the Chinese bees. Therefore, in this paper, we use a deep learning algorithm to detect the insect invasion of the Chinese beehive culture and analyze the bee colonies in six bee farms in the province of Sichuan. In addition, we measure the common insect and disease indexes of the Chinese bee and analyze the parasitism rate, microsporidia infection rate, virus infection rate, and virus infection titer of bee colonies in overwintering and spring breeding. The experimental results show that the anti-insect invasion situation of bees in the six bee farms is significantly different; however, the antimite ability is basically the same.

show abstract

Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset

Cited by 24 publications

References 1 publication

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning

Contact Info

Product

Resources

About