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 …

A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids

H Karimipour, A Dehghantanha, RM Parizi… - Ieee …, 2019 - ieeexplore.ieee.org
Smart grid technology increases reliability, security, and efficiency of the electrical grids.
However, its strong dependencies on digital communication technology bring up new …

Critical transitions and their early warning signals in thermoacoustic systems

I Pavithran, VR Unni, RI Sujith - The European Physical Journal Special …, 2021 - Springer
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 …

[PDF][PDF] Outlier detection: A survey

V Chandola, A Banerjee, V Kumar - ACM Computing Surveys, 2007 - researchgate.net
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 …

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 …

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 …

Time series forecasting with multi-headed attention-based deep learning for residential energy consumption

SJ Bu, SB Cho - Energies, 2020 - mdpi.com
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 …

Target detection and classification using seismic and PIR sensors

X Jin, S Sarkar, A Ray, S Gupta… - IEEE sensors …, 2011 - ieeexplore.ieee.org
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 …

[PDF][PDF] ORA user's guide 2013

KM Carley, J Reminga, J Storrick, D Columbus - DTIC Document, 2013 - academia.edu
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 …

Multiscale symbolic diversity entropy: a novel measurement approach for time-series analysis and its application in fault diagnosis of planetary gearboxes

Y Li, S Wang, N Li, Z Deng - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
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 …