IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

Sarker, Iqbal H.; Abushark, Yoosef B.; Alsolami, Fawaz; Khan, Asif Irshad

doi:10.3390/sym12050754

Cited by 196 publications

(79 citation statements)

References 45 publications

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“…The decision tree algorithm used in this article is only traditional recursive partitioning and regression trees, implemented as the rpart code in R. There are novel decision tree models that can enhance the classification performance, such as IntruDTree, which takes into account the ranking of security features according to their importance [36]. A behavioral decision tree based on the context-aware predictive model is also very impressive on the classification performance [37].…”

Section: Discussionmentioning

confidence: 99%

Animated Character Style Investigation with Decision Tree Classification

et al. 2020

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Although animated characters are based on human features, these features are exaggerated. These exaggerations greatly differ by country, gender, and the character’s role in the story. This study investigated the characteristics of US and Japanese character designs and the similarities and differences or even the differences in exaggerations between them. In particular, these similarities and differences can be used to formulate a shared set of principles for US and Japanese animated character designs; 90 Japanese and 90 US cartoon characters were analyzed. Lengths for 20 parts of the body were obtained for prototypical real human bodies and animated characters from Japan and the United States. The distributions of lengths were determined, for all characters and for characters as segmented by country, gender, and the character’s role in the story. We also compared the body part lengths of animated characters and prototypical real human bodies, noting whether exaggerations were towards augmentation or diminishment. In addition, a decision tree classification method was used to determine the required body length parameters for identifying the classification conditions of animated characters by country, gender, and character’s role in the story. The results indicated that both US and Japanese male animated characters tend to feature exaggerations in head and body sizes, with exaggerations for US characters being more obvious. The decision tree only required five length parameters of the head and chest to distinguish between US and Japanese animated characters (accuracy = 94.48% and 67.46% for the training and testing groups, respectively). Through a decision tree method, this study quantitatively revealed the exaggeration patterns in animated characters and their differences by country, gender, and character’s role in the story. The results serve as a reference for designers and researchers of animated character model designs with regards to quantifying and classifying character exaggerations.

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Section: Discussionmentioning

confidence: 99%

Animated Character Style Investigation with Decision Tree Classification

et al. 2020

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“…Table 5 and Fig. 7 illustrate the comparative results analysis of the QBSO-FDNN model with recently developed models interms of accuracy [22,23]. From the attained results, it is evident that the CS-PSO and GBT models have demonstrated insignificant detection results with the accuracy of 75.51% and 84.25% respectively.…”

Section: Performance Validationmentioning

confidence: 92%

A Novel Feature Selection With Fuzzy Deep Neural Network for Attack Detection in Big Data Environment

Kumar¹,

Mohan²

2021

INDJCSE

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In recent times, the massive quantity of data and its continual expansion have transformed the significance of information security and data analysis systems for Big Data. An intrusion detection system (IDS) is commonly employed to monitor and analyze data for the detection of intrusions in the network. The conventional IDS models are not adequate to handle the high volume, variety, and speed of big data. This paper presents a new quantum brain storm optimization (QBSO) based feature selection with fuzzy deep neural network (FDNN), called QBSO-FDNN model for IDS in big data environment. The proposed model enables to detection of intrusions in the big data environment. The presented model initially performs preprocessing to enhance the quality of the big data. Also, to reduce the computational complexity, QBSO algorithm is applied to elect an optimal set of features. The choice of optimal features by the QBSO algorithm helps to boost the detection performance. Besides, FDNN model is applied as a classification model for identifying the occurrence of intrusions in the network. An extensive set of simulations was carried out to highlight the results on benchmark dataset. The resultant experimental values showcased the superior performance of the QBSO-FNN model with the detection accuracy of 98.90%.

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“…Figure 4 shows an example of a random forest structure considering multiple decision trees. In addition, BehavDT recently proposed by Sarker et al [109], and IntrudTree [106] can be used for building effective classification or prediction models in the relevant tasks within the domain of data science and analytics.…”

Section: Classification Analysismentioning

confidence: 99%

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Sarker

2021

SN COMPUT. SCI.

Self Cite

219

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The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science" including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

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IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

Cited by 196 publications

References 45 publications

Animated Character Style Investigation with Decision Tree Classification

Animated Character Style Investigation with Decision Tree Classification

A Novel Feature Selection With Fuzzy Deep Neural Network for Attack Detection in Big Data Environment

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

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