Anomaly detection in NetFlow network traffic using supervised machine learning algorithms
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 …
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 …
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
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 …
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
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 …
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 …
protective measures as hackers launch attacks to paralyze or steal information from victim …
Network Intrusion Detection: An IoT and Non IoT-Related Survey
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 …
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
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 …
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
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 …
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 …
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 …
pressure on the global economy. Challenges for the US include not only economic factors …