Relevant and Non-Redundant Feature Subset Selection Applied to the Detection of Malware in a Network

Bravo, Franklin Parrales; Torres-Urresto, Joel; Avila-Maldonado, Dayannara; Barzola-Monteses, Julio

doi:10.1109/etcm53643.2021.9590777

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…Wrapper methods: Wrappers consider subsets of features by the quality of the performance of the subset on a modeling algorithm, which is taken as a black box evaluator. 43 It has been empirically proven that wrappers obtain subsets with better performance than filters because the subsets are evaluated using a real modeling algorithm. 38 3.…”

Section: Standard Feature Selectionmentioning

confidence: 99%

“…Importantly, many filter methods allow for parallelized score calculations, resulting in increased computational efficiency, as highlighted in Reference 41. This approach encompasses a wide range of algorithms, as described in Reference 42. Wrapper methods: Wrappers consider subsets of features by the quality of the performance of the subset on a modeling algorithm, which is taken as a black box evaluator 43 . It has been empirically proven that wrappers obtain subsets with better performance than filters because the subsets are evaluated using a real modeling algorithm 38 …”

Section: Introductionmentioning

confidence: 99%

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Explainable artificial intelligence for feature selection in network traffic classification: A comparative study

Khani,

Moeinaddini,

Abnavi

et al. 2024

Trans Emerging Tel Tech

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Over the past decade, there has been a growing surge of interest in leveraging artificial intelligence and machine learning models to address real‐world challenges within the field of telecommunications and networking. Among these challenges, network traffic classification has consistently remained a key focal point within the community. The emergence of new paradigms such as internet of things has added complexity to this challenge, owing to the diverse array of traffic classes it introduces. This complexity is further compounded when dealing with encrypted traffic data. In the context of this paper, we employ explainable artificial intelligence (XAI) techniques to offer insightful explanations for the task of classifying encrypted network traffic. Our approach involves presenting a ranked list of the model's input features, organized based on their significance in influencing the classifier's predictions. These ranked lists shed light on the underlying functionality of the encrypted traffic classification model, enabling users to discern which features hold greater importance in shaping the model's output by harnessing the insights provided by XAI approaches. Moreover, we carry out a quantitative assessment and comparison of the results generated by these XAI methods and outputs of three other conventional, widely adopted feature selection approaches, utilizing them as feature selection techniques when applied to a convolutional neural network for classifying data from the CIC‐Darknet2020 and CIC‐IDS2017 datasets. The results show that XAI techniques have high performance in feature selection for network traffic classification.

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Section: Standard Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence for feature selection in network traffic classification: A comparative study

Khani,

Moeinaddini,

Abnavi

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…We'll go over the most important stages below. The importance of preprocessing the dataset before model training is emphasized in the ML literature [24]. Tokenizing and lemmatizing the text is thus required before creating the model; they are crucial procedures to get the data ready for training and testing tasks [25].…”

Section: Model Trainingmentioning

confidence: 99%

“…In this context, the field of Artificial Intelligence (AI) can be used to automate that task. One of its subfields, Machine Learning (ML), enables the automated recognition of patterns in gathered data [5]. A deep learning approach based on Artificial Neural Networks (ANN) can be applied to train the model [6].…”

Section: Introductionmentioning

confidence: 99%

CSM: a Chatbot Solution to Manage Student Questions About payments and Enrollment In University

Parrales-Bravo,

Caicedo-Quiroz,

Barzola-Monteses

et al. 2024

IEEE Access

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Students at the University of Guayaquil pursuing a degree in software engineering who struggle to obtain information on enrollment and tuition payments are the target audience for the application developed in this study. To do this, a chatbot is proposed. It uses Machine Learning (ML) and Natural Language Processing (NLP) to answer student's questions. To evaluate the ease of use of the system from a quantitative point of view, the time used by 6 students to complete user registration (T1), make five searches (T2), and unsubscribe (T3), was measured in minutes, with an average duration of three minutes for each activity. To assess the students' perception with the use of the proposed chatbot, a sample of sixty students who had previously interacted with the proposed solution. Aspects assessed included: Ease of use of the system (A), speed (B), appropriate behavior (C), confidence to use it again (D), and quality of the answer provided (E). Each evaluated characteristic (A-E) was rated above 3 out of 5 in most cases. Thus, the chatbot solution achieved good acceptance by students, even considering its use in future academic semesters.

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From Descriptive to Prescriptive Analytics on Time Series of the Number of Preeclampsia Inpatient Beds

Parrales-Bravo,

Gómez-Rodríguez,

Barzola-Monteses

et al. 2024

IEEE Access

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Relevant and Non-Redundant Feature Subset Selection Applied to the Detection of Malware in a Network

Cited by 11 publications

References 11 publications

Explainable artificial intelligence for feature selection in network traffic classification: A comparative study

Explainable artificial intelligence for feature selection in network traffic classification: A comparative study

CSM: a Chatbot Solution to Manage Student Questions About payments and Enrollment In University

From Descriptive to Prescriptive Analytics on Time Series of the Number of Preeclampsia Inpatient Beds

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