Smart Approach for Botnet Detection Based on Network Traffic Analysis

Obeidat, Alaa; Yaqbeh, Rola

doi:10.1155/2022/3073932

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…-Elements of a blended learning model A model can be a description of a phenomenon or system that takes into account it's known or inferred attributes and is employed to learn more about its features (Figure 1). Therefore, a blended learning model is required which helps us to consider various factors, their assessment and co-relations of various elements that would produce an instructional sound learning environment [8], [9]. The learning methods component: assesses the learning environment (face-to-face/online).…”

Section: Introductionmentioning

confidence: 99%

Implementation of blended learning approach in teaching introductory computer science course

Obeidat

Yaqbeh

2023

Bulletin EEI

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Information communication and technology has great impact on modern education architecture of 21st century. This research intends to analyze the effectiveness of blended learning considering its basic principles and factors. Also, this study is focused on determining core problems of blended learning thus highlighting its design approach. A descriptive and inferential statistical analysis has been conducted on the data that reveal that the majority of students prefer blended learning system regardless their major, gender and any other factors if they have good infrastructure and effective electronic platform. It is recommended that for effective implementation of blended system there should be opportunities of face to face as well as online synchronous, and non-synchronous communication between students and teachers.

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Section: Introductionmentioning

confidence: 99%

Implementation of blended learning approach in teaching introductory computer science course

Obeidat

Yaqbeh

2023

Bulletin EEI

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“…With this type of hybrid model resulted in an improvement of accuracy. In Obeidat and Yaqbeh, 11 a smart approach for identifying data traffic behavior using machine learning classifiers and a genetic algorithm with the purpose of detecting botnet activities were presented. Moreover, by classifying behavior on the basis of time slots, the viability of botnet behavior was also investigated with high precision.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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“…The proposed genetic algorithm (GA) offers more efficient and dynamic solutions, even in the face of variations in network topology, network dynamics, link or node deletions, and network volume (with numerous routes). Obeidat and Yaqbeh (2022) used a method for identifying the behavior of data traffic using machine learning classifiers, including genetic algorithm to detect botnet activities. Zhang (2019) proposed a parallel SVM (Support Vector Machine) customer churn prediction method based on freight customer value classification with the KFAV model for railway bulk freight customers.…”

Section: Introductionmentioning

confidence: 99%

A Mining Algorithm to Improve LSTM for Predicting Customer Churn in Railway Freight Traffic

Zhao¹,

Dong²,

Peng³

et al. 2023

SIC

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Railway freight is at risk of losing customers due to intense competition in the market. Effectively managing potential customer loss is a long-term problem for railway freight. Big data technology has been widely researched and applied in recent years, and using data mining techniques to fully extract information from railway freight ticket data and discover potential customer loss is a research topic. In this study, a mining algorithm is proposed to improve the accuracy of predicting customer churn in railway freight using Long Short-Term Memory (LSTM) network. The algorithm involves three steps: constructing the time series of railway freight volume, predicting customer churn trends using a modified LSTM model, and analyzing the characteristics of customers with different degrees of loss and the characteristics of transportation demand of lost and potential lost customers. Experimental verification was conducted using customer freight ticket data from the Shanghai Railway Bureau in China and compared the proposed modified LSTM model was compared with other commonly used machine learning algorithms for churn prediction. The results showed that the present algorithm demonstrated good accuracy and adaptability. The study proposed in this paper enriches the theoretical basis of railway freight customer management, provides an effective method for predicting customer loss in railway freight, and offers technical support for railway freight management practices.

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Smart Approach for Botnet Detection Based on Network Traffic Analysis

Cited by 4 publications

References 23 publications

Implementation of blended learning approach in teaching introductory computer science course

Implementation of blended learning approach in teaching introductory computer science course

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

A Mining Algorithm to Improve LSTM for Predicting Customer Churn in Railway Freight Traffic

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