Adaptive Scheme for ANOVA Models

Biney, Gilbert; Okyere, Gabriel Asare; Alhassan, Abukari

doi:10.9734/jamcs/2020/v35i430266

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, data features with irrelevant and redundant data are eliminated, and necessary features are selected. For feature selection, the univariate feature selection technique with analysis of a variance (ANOVA) F-test is used [79,80]. An ANOVA f-test is used to analyze to determine x individual performance of all the features and strength of the relationship between the data feature and label features of the dataset.…”

Section: Datasetsmentioning

confidence: 99%

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021

View full text Add to dashboard Cite

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

show abstract

Section: Datasetsmentioning

confidence: 99%

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021

View full text Add to dashboard Cite

show abstract

“…ε is a random variable that has a normal with zero mean, constant and uncorrelated variance (Aparicio et al, 2004). A one-way ANOVA was used to determine the statistical significance of the analyzed variables in the model (Biney et al, 2020). The data was analyzed using statistical software R version 3.6.1…”

Section: R=xmax-xminmentioning

confidence: 99%

Determinant aspects of the deciduous fruit production in the province of Tungurahua, Ecuador

Viera

Merino

Martínez

et al. 2020

manglar

View full text Add to dashboard Cite

The province of Tungurahua is well known for the production of deciduous fruit trees in Ecuador, however, its production is impacted by some issues that affect productivity. The purpose of this investigation was to conduct a diagnosis of the production system of deciduous fruit trees in 6 locations in Tungurahua province. Surveys were carried out to obtain information on 117 production units (farms). A regression model was used to determine the relationship between the variables and their incidence in the production system. The results showed that peach (Prunus persica) is the fruit crop with highest yield (16.19 t ha-1), followed by apple (Malus domestica) (13.36 t ha-1) and pear (Pyrus communis) (13.15 t ha-1). Apple cultivars Emilia and Golden Delicious, cv. Uvilla of pear and Conservero Amarillo of peach were the most cultivated in Tungurahua. The main factors that influenced the farmer income were: Land area used for cultivation, reason for growing fruit trees, age of the producer, fruit selection and type of production. It is necessary to advocate to expand the cultivated land area of these fruit crops in order to satisfy local demand and compete with imports of these fruits.

show abstract

A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection

2022

View full text Add to dashboard Cite

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results.

show abstract

Adaptive Scheme for ANOVA Models

Cited by 4 publications

References 0 publications

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Determinant aspects of the deciduous fruit production in the province of Tungurahua, Ecuador

A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection

Contact Info

Product

Resources

About