Anomaly detection in NetFlow network traffic using supervised machine learning algorithms

I Fosić, D Žagar, K Grgić, V Križanović - Journal of industrial information …, 2023 - Elsevier
Anomaly detection is an important method for monitoring network traffic where is important to
successfully distinguish normal traffic from abnormal traffic. For this purpose, one could use …

A hybrid machine learning method for increasing the performance of network intrusion detection systems

AA Megantara, T Ahmad - Journal of Big Data, 2021 - Springer
The internet has grown enormously for many years. It is not just connecting computer
networks but also a group of devices worldwide involving big data. The internet provides an …

A detailed analysis of benchmark datasets for network intrusion detection system

M Ghurab, G Gaphari, F Alshami… - Asian Journal of …, 2021 - papers.ssrn.com
The enormous increase in the use of the Internet in daily life has provided an opportunity for
the intruder attempt to compromise the security principles of availability, confidentiality, and …

Recursive feature elimination with cross-validation with decision tree: Feature selection method for machine learning-based intrusion detection systems

M Awad, S Fraihat - Journal of Sensor and Actuator Networks, 2023 - mdpi.com
The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …

Network intrusion detection based on LSTM and feature embedding

H Gwon, C Lee, R Keum, H Choi - arXiv preprint arXiv:1911.11552, 2019 - arxiv.org
Growing number of network devices and services have led to increasing demand for
protective measures as hackers launch attacks to paralyze or steal information from victim …

Network Intrusion Detection: An IoT and Non IoT-Related Survey

SA Abdulkareem, CH Foh, M Shojafar, F Carrez… - IEEE …, 2024 - ieeexplore.ieee.org
The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing.
The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent …

Improved security in cloud using sandpiper and extended equilibrium deep transfer learning based intrusion detection

G Sreelatha, AV Babu, D Midhunchakkaravarthy - Cluster computing, 2022 - Springer
Cloud computing (CC) offers various types of services for the users and it is also termed on-
demand computing. Because of its increasing popularity, it is vulnerable to a variety of …

User preferences on cloud computing and open innovation: A case study for university employees in Greece

EC Gkika, T Anagnostopoulos, S Ntanos… - Journal of Open …, 2020 - mdpi.com
Cloud computing hastens technology driven innovation by taking advantage of the speed,
the cost-effectiveness, the efficiency and the security that such applications offer. By using …

Flow-data gathering using netflow sensors for fitting malicious-traffic detection models

A Campazas-Vega, IS Crespo-Martínez… - Sensors, 2020 - mdpi.com
Advanced persistent threats (APTs) are a growing concern in cybersecurity. Many
companies and governments have reported incidents related to these threats. Throughout …

[HTML][HTML] Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession

L Omolo, N Nguyen - Journal of Risk and Financial Management, 2024 - mdpi.com
The COVID-19 pandemic and the current wars in some countries have put incredible
pressure on the global economy. Challenges for the US include not only economic factors …