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 outlier detection in high dimensional data streams
The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …
streams in a wide range of fields, such as genomics, signal processing, and finance. The …
A survey on fake news and rumour detection techniques
False or unverified information spreads just like accurate information on the web, thus
possibly going viral and influencing the public opinion and its decisions. Fake news and …
possibly going viral and influencing the public opinion and its decisions. Fake news and …
[HTML][HTML] Unsupervised real-time anomaly detection for streaming data
We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …
Largely driven by the rise of connected real-time data sources, this data presents technical …
Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset
In recent years, due to the extensive use of the Internet, the number of networked computers
has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude …
has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude …
An entropy-based network anomaly detection method
Data mining is an interdisciplinary subfield of computer science involving methods at the
intersection of artificial intelligence, machine learning and statistics. One of the data mining …
intersection of artificial intelligence, machine learning and statistics. One of the data mining …
Battle of the attack detection algorithms: Disclosing cyber attacks on water distribution networks
Abstract The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent
competition on planning and management of water networks undertaken within the Water …
competition on planning and management of water networks undertaken within the Water …
Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models
An intrusion detection system has become a vital mechanism to detect a wide variety of
malicious activities in the cyber domain. However, this system still faces an important …
malicious activities in the cyber domain. However, this system still faces an important …
Synthetic temporal anomaly guided end-to-end video anomaly detection
Due to the limited availability of anomaly examples, video anomaly detection is often seen
as one-class classification (OCC) problem. A popular way to tackle this problem is by …
as one-class classification (OCC) problem. A popular way to tackle this problem is by …
A two-level hybrid approach for intrusion detection
C Guo, Y **, N Liu, SS Luo - Neurocomputing, 2016 - Elsevier
To exploit the strengths of misuse detection and anomaly detection, an intensive focus on
intrusion detection combines the two. From a novel perspective, in this paper, we proposed …
intrusion detection combines the two. From a novel perspective, in this paper, we proposed …