Anomaly detection in NetFlow network traffic using supervised machine learning algorithms

Fosić, I.; Žagar, Drago; Grgić, Krešimir; Križanović, Višnja

doi:10.1016/j.jii.2023.100466

Cited by 12 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the paper "Anomaly detection in NetFlow network traffic using supervised machine learning algorithms," Igor et al tested several classification algorithms (stochastic gradient descent (SGD), support vector machine (SVM), K-nearest neighbors (K-NN), Gaussian naive Bayes (GNB), decision tree (DT), random forest (RF), AdaBoost (AB)) on the UNSW-NB15 public dataset [4]. Different encoding methods and the ratio of training data to testing data led to the optimal parameter classifiers.…”

Section: Supervised Anomaly Detectionmentioning

confidence: 99%

Detection of anomalous ticket purchasing behavior for concerts based on machine learning

Lingyang,

Lilin

2024

ACE

View full text Add to dashboard Cite

In recent years, there has been a continuous increase in the demand for entertainment activities. With the changing policies related to epidemic prevention and control, consumers ticket demands for various entertainment events, particularly concerts, have seen a significant rise. This has led to the emergence of anomalous ticket purchasing behaviors, including ticket scalping software, bulk purchasing, and fraudulent transactions. Such behaviors not only infringe upon the rights of legitimate audiences but also harm the interests of concert organizers. Therefore, the detection and prevention of anomalous ticket purchasing behaviors for concerts have become essential. This paper focuses on the use of automated systems, robots, or malicious software for ticket purchases, limiting consumers participation in abnormal ticket-buying activities. Combining previous research on railway ticketing systems and real-life experiences, this study selects ticket-purchasing frequency, speed, quantity, seat selection, payment method, IP address changes, and historical ticket-purchasing records as feature values. Different machine learning models are chosen for different feature values to conceptualize the construction of an anomalous ticket purchasing system for concerts. This paper emphasizes the importance of detecting anomalous concert ticket purchasing behavior, provides different machine learning models for various feature values, and lays the foundation for building an overall anomaly detection system. This research serves as a crucial reference for the security and user experience of ticketing systems, aiming to continually improve and upgrade anomaly detection systems to adapt to evolving challenges. The hope is to enhance the ticket-buying experience for future concerts and large-scale events.

show abstract

Section: Supervised Anomaly Detectionmentioning

confidence: 99%

Detection of anomalous ticket purchasing behavior for concerts based on machine learning

Lingyang,

Lilin

2024

ACE

View full text Add to dashboard Cite

show abstract

Value co‐creation mechanism for AI‐based enterprises with systems thinking: Case study of Baidu

Su,

Du,

Chen

et al. 2024

Syst Res Behav Sci

View full text Add to dashboard Cite

How to realize value co‐creation is a challenge for artificial intelligence (AI)‐based enterprises. From the perspective of systems thinking, this paper conducts a case study on Baidu to explore the mechanism for Ai‐based enterprises to realize value co‐creation. The results indicate that value co‐creation of AI companies goes through a dynamic iteration of three stages: start‐up, growth, and maturity. Based on the finding, this paper proposes a model for AI‐based enterprises to realize value co‐creation with stakeholders under the interaction of innovation resilience and value proposition, as well as the combined effect of digital‐intelligence empowerment, resource‐focused action, and network embedding. Findings in this study not only fill the gap in existing literature but also provide insights for AI‐based enterprises to realize sustainable development.

show abstract

A Survey on Anomaly Detection in Network with ML Techniques

Karthika,

Gunasundari

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Anomaly detection in NetFlow network traffic using supervised machine learning algorithms

Cited by 12 publications

References 21 publications

Detection of anomalous ticket purchasing behavior for concerts based on machine learning

Detection of anomalous ticket purchasing behavior for concerts based on machine learning

Value co‐creation mechanism for AI‐based enterprises with systems thinking: Case study of Baidu

A Survey on Anomaly Detection in Network with ML Techniques

Contact Info

Product

Resources

About