Super learning for anomaly detection in cellular networks
P Casas, J Vanerio - 2017 IEEE 13th International Conference …, 2017 - ieeexplore.ieee.org
The ever-growing population of smartphones connected to mobile networks is changing the
cellular traffic ecosystem. The traffic volumes and patterns generated by smartphone apps …
cellular traffic ecosystem. The traffic volumes and patterns generated by smartphone apps …
Real-time intruder detection in surveillance networks using adaptive kernel methods
In this paper we apply a recursive algorithm based on kernel mappings to propose an
automated, real-time intruder detection mechanism for surveillance networks. Our proposed …
automated, real-time intruder detection mechanism for surveillance networks. Our proposed …
Application of machine learning techniques to detecting anomalies in communication networks: Classification algorithms
In this chapter, we apply various machine learning techniques for classification of known
network anomalies. The models are trained and tested on various collected datasets. With …
network anomalies. The models are trained and tested on various collected datasets. With …
Mark: Fill in the blanks through a jointgan based data augmentation for network anomaly detection
R Patil, V Sachidananda, H Peng, A Sachdeva… - Computers & …, 2022 - Elsevier
In this paper, we present MARK, a data synthesis method to synthesize patterns for evolving
normal network behaviors as well as unknown network attacks for detection of an anomaly …
normal network behaviors as well as unknown network attacks for detection of an anomaly …
Spatio-temporal forecasting of network data
J Haworth - 2014 - discovery.ucl.ac.uk
In the digital age, data are collected in unprecedented volumes on a plethora of networks.
These data provide opportunities to develop our understanding of network processes by …
These data provide opportunities to develop our understanding of network processes by …
On the analysis of network measurements through machine learning: The power of the crowd
P Casas - 2018 Network Traffic Measurement and Analysis …, 2018 - ieeexplore.ieee.org
The application of Machine Learning (ML) models to the analysis of network measurement
problems has largely increased in the last decade; however, there is still no clear best …
problems has largely increased in the last decade; however, there is still no clear best …
RAMAN: Robust Approaches for Multimodal ANomaly detection in Mars Rover Power Systems
This paper proposes RAMAN, a framework of approaches for multimodal anomaly detection
that is robust to different anomaly types, input data, and domain constraints for the Mars …
that is robust to different anomaly types, input data, and domain constraints for the Mars …
Traffic dynamics-aware probe selection for fault detection in networks
Fault detection in modern networks is done with a set of specially instrumented nodes which
send probes to find faults. These probes generate additional traffic in network and compete …
send probes to find faults. These probes generate additional traffic in network and compete …
Multi-granular aggregation of network flows for security analysis
Investigating network flows is an approach of detecting attacks by identifying known
patterns. Flow statistics are used to discover anomalies by aggregating network traces and …
patterns. Flow statistics are used to discover anomalies by aggregating network traces and …
Performance and security evaluation of SDN networks in OMNeT++/INET
Software Defined Networking (SDN) has been recently introduced as a new communication
paradigm in computer networks. By separating the control plane from the data plane and …
paradigm in computer networks. By separating the control plane from the data plane and …