A review of detection approaches for distributed denial of service attacks
P Kaur, M Kumar, A Bhandari - Systems Science & Control …, 2017 - Taylor & Francis
ABSTRACT Distributed Denial of Service (DDoS) attacks are the intimidation trials on the
Internet that depletes the network bandwidth or exhausts the victim's resources …
Internet that depletes the network bandwidth or exhausts the victim's resources …
A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids
Smart grid technology increases reliability, security, and efficiency of the electrical grids.
However, its strong dependencies on digital communication technology bring up new …
However, its strong dependencies on digital communication technology bring up new …
Critical transitions and their early warning signals in thermoacoustic systems
Many complex systems undergo critical transitions. A thermoacoustic system is one such
system which exhibits a catastrophic transition to a state of oscillatory instability known as …
system which exhibits a catastrophic transition to a state of oscillatory instability known as …
[PDF][PDF] Outlier detection: A survey
Outlier detection has been a very important concept in the realm of data analysis. Recently,
several application domains have realized the direct mapping between outliers in data and …
several application domains have realized the direct mapping between outliers in data and …
Symbolic time series analysis via wavelet-based partitioning
V Rajagopalan, A Ray - Signal processing, 2006 - Elsevier
Symbolic time series analysis (STSA) of complex systems for anomaly detection has been
recently introduced in literature. An important feature of the STSA method is extraction of …
recently introduced in literature. An important feature of the STSA method is extraction of …
K-Means+ ID3: A novel method for supervised anomaly detection by cascading K-Means clustering and ID3 decision tree learning methods
SR Gaddam, VV Phoha… - IEEE transactions on …, 2007 - ieeexplore.ieee.org
In this paper, we present" k-means+ ID3", a method to cascade k-means clustering and the
ID3 decision tree learning methods for classifying anomalous and normal activities in a …
ID3 decision tree learning methods for classifying anomalous and normal activities in a …
Time series forecasting with multi-headed attention-based deep learning for residential energy consumption
Predicting residential energy consumption is tantamount to forecasting a multivariate time
series. A specific window for several sensor signals can induce various features extracted to …
series. A specific window for several sensor signals can induce various features extracted to …
Target detection and classification using seismic and PIR sensors
Unattended ground sensors (UGS) are widely used to monitor human activities, such as
pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is …
pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is …
[PDF][PDF] ORA user's guide 2013
14. ABSTRACT ORA is a network analysis tool that detects risks or vulnerabilities of an
organization? s design structure. The design structure of an organization is the relationship …
organization? s design structure. The design structure of an organization is the relationship …
Multiscale symbolic diversity entropy: a novel measurement approach for time-series analysis and its application in fault diagnosis of planetary gearboxes
The health condition monitoring of planetary gearboxes has drawn increasing attention due
to the importance for safety operation and failure prevention. A novel diagnosis methodology …
to the importance for safety operation and failure prevention. A novel diagnosis methodology …