Real-time big data processing for anomaly detection: A survey
The advent of connected devices and omnipresence of Internet have paved way for
intruders to attack networks, which leads to cyber-attack, financial loss, information theft in …
intruders to attack networks, which leads to cyber-attack, financial loss, information theft in …
Time-series data mining
P Esling, C Agon - ACM Computing Surveys (CSUR), 2012 - dl.acm.org
In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …
lead to a collection of organized data called time series. The purpose of time-series data …
Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments
Abstract Software Defined Networks (SDNs) based on the OpenFlow (OF) protocol export
control-plane programmability of switched substrates. As a result, rich functionality in traffic …
control-plane programmability of switched substrates. As a result, rich functionality in traffic …
Network anomaly detection with the restricted Boltzmann machine
With the rapid growth and the increasing complexity of network infrastructures and the
evolution of attacks, identifying and preventing network abuses is getting more and more …
evolution of attacks, identifying and preventing network abuses is getting more and more …
IEEE 802.11 network anomaly detection and attack classification: A deep learning approach
VLL Thing - 2017 IEEE wireless communications and …, 2017 - ieeexplore.ieee.org
Despite the significant advancement in wireless technologies over the years, IEEE 802.11
still emerges as the de-facto standard to achieve the required short to medium range …
still emerges as the de-facto standard to achieve the required short to medium range …
Automap: Diagnose your microservice-based web applications automatically
The high complexity and dynamics of the microservice architecture make its application
diagnosis extremely challenging. Static troubleshooting approaches may fail to obtain …
diagnosis extremely challenging. Static troubleshooting approaches may fail to obtain …
[图书][B] Temporal data mining
T Mitsa - 2010 - taylorfrancis.com
Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …
New initiatives in health care and business organizations have increased the importance of …
Cloudranger: Root cause identification for cloud native systems
As more and more systems are migrating to cloud environment, the cloud native system
becomes a trend. This paper presents the challenges and implications when diagnosing …
becomes a trend. This paper presents the challenges and implications when diagnosing …
Deepmal-deep learning models for malware traffic detection and classification
G Marín, P Caasas, G Capdehourat - … of the 3rd International Data Science …, 2021 - Springer
Robust network security systems are essential to prevent and mitigate the harming effects of
the ever-growing occurrence of network attacks. In recent years, machine learning-based …
the ever-growing occurrence of network attacks. In recent years, machine learning-based …
Multidisciplinary pattern recognition applications: A review
M Paolanti, E Frontoni - Computer Science Review, 2020 - Elsevier
Pattern recognition (PR) is the study of how machines can examine the environment, learn
to distinguish patterns of interest from their background, and make reliable and feasible …
to distinguish patterns of interest from their background, and make reliable and feasible …