Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
A survey of random forest based methods for intrusion detection systems
Over the past decades, researchers have been proposing different Intrusion Detection
approaches to deal with the increasing number and complexity of threats for computer …
approaches to deal with the increasing number and complexity of threats for computer …
Hate speech detection: Challenges and solutions
As online content continues to grow, so does the spread of hate speech. We identify and
examine challenges faced by online automatic approaches for hate speech detection in text …
examine challenges faced by online automatic approaches for hate speech detection in text …
On the feasibility of deep learning in sensor network intrusion detection
In this letter, we present a comprehensive analysis of the use of machine and deep learning
(DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we …
(DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we …
Intrusion detection based on autoencoder and isolation forest in fog computing
Fog Computing has emerged as an extension to cloud computing by providing an efficient
infrastructure to support IoT. Fog computing acting as a mediator provides local processing …
infrastructure to support IoT. Fog computing acting as a mediator provides local processing …
A brief survey of machine learning methods and their sensor and IoT applications
This paper provides a brief survey of the basic concepts and algorithms used for Machine
Learning and its applications. We begin with a broader definition of machine learning and …
Learning and its applications. We begin with a broader definition of machine learning and …
A stacking ensemble for network intrusion detection using heterogeneous datasets
The problem of network intrusion detection poses innumerable challenges to the research
community, industry, and commercial sectors. Moreover, the persistent attacks occurring on …
community, industry, and commercial sectors. Moreover, the persistent attacks occurring on …
Shallow and deep networks intrusion detection system: A taxonomy and survey
E Hodo, X Bellekens, A Hamilton, C Tachtatzis… - ar** study and cross-benchmark evaluation
Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of
cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design …
cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design …
Machine learning for cloud security: a systematic review
The popularity and usage of Cloud computing is increasing rapidly. Several companies are
investing in this field either for their own use or to provide it as a service for others. One of …
investing in this field either for their own use or to provide it as a service for others. One of …