Evaluation of machine learning methods for lithology classification using geophysical data
TS Bressan, MK de Souza, TJ Girelli… - Computers & Geosciences, 2020 - Elsevier
Specific computational tools assist geologists in identifying and sorting lithologies in well
surveys and reducing operational costs and practical working time. This allows for the …
surveys and reducing operational costs and practical working time. This allows for the …
A stacked ensemble learning model for intrusion detection in wireless network
H Rajadurai, UD Gandhi - Neural computing and applications, 2022 - Springer
Intrusion detection pretended to be a major technique for revealing the attacks and
guarantee the security on the network. As the data increases tremendously every year on …
guarantee the security on the network. As the data increases tremendously every year on …
Anomaly-based intrusion detection approach for IoT networks using machine learning
The proliferation of the Internet of Things (IoT) devices in smart environments such as smart
cities or smart home facilitate communication between various objects. Nevertheless, this …
cities or smart home facilitate communication between various objects. Nevertheless, this …
A ZigBee intrusion detection system for IoT using secure and efficient data collection
The market for Internet of Things (IoT) products and services has grown rapidly. It has been
predicted that the deployment of these IoT applications will grow exponentially in the near …
predicted that the deployment of these IoT applications will grow exponentially in the near …
Multi-layer perceptron for network intrusion detection: From a study on two recent data sets to deployment on automotive processor
The Internet connection is becoming ubiquitous in embedded systems, making them
potential victims of intrusion. Although gaining popularity in recent years, deep learning …
potential victims of intrusion. Although gaining popularity in recent years, deep learning …
[PDF][PDF] Detecting Intrusions in Computer Network Traffic with Machine Learning Approaches.
Security has been a crucial factor in this modern digital period due to the rapid development
of information technology, which is followed by serious computer crimes that, in turn, led to …
of information technology, which is followed by serious computer crimes that, in turn, led to …
Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques
Network security and data security are the biggest concerns now a days. Every organization
decides their future business process based on the past and day to day transactional data …
decides their future business process based on the past and day to day transactional data …
Feed-forward neural network for Network Intrusion Detection
The Internet connection is becoming ubiquitous in embedded systems, making them
potential victims of intrusion. While in the age of deep learning, these algorithms tend to …
potential victims of intrusion. While in the age of deep learning, these algorithms tend to …
[PDF][PDF] ZigBee IoT Intrusion Detection System: A Hybrid Approach with Rule-based and Machine Learning Anomaly Detection.
The Internet of Things (IoT) is an emerging technology with potential applications in different
domains. However these IoT systems introduce new security risks and potentially open new …
domains. However these IoT systems introduce new security risks and potentially open new …
Role of machine learning in communication networks
This chapter focuses on machine learning techniques that can be deployed in the
communication networks at different stages of their usage, including traffic control, routing of …
communication networks at different stages of their usage, including traffic control, routing of …