TFDPM: Attack detection for cyber–physical systems with diffusion probabilistic models
With the development of AIoT, data-driven attack detection methods for cyber–physical
systems (CPSs) have attracted lots of attention. However, existing methods usually adopt …
systems (CPSs) have attracted lots of attention. However, existing methods usually adopt …
Deep learning based attack detection for cyber-physical system cybersecurity: A survey
With the booming of cyber attacks and cyber criminals against cyber-physical systems
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …
A heterogeneous graph learning model for cyber-attack detection
M Lv, C Dong, T Chen, T Zhu, Q Song, Y Fan - arXiv preprint arXiv …, 2021 - arxiv.org
A cyber-attack is a malicious attempt by experienced hackers to breach the target
information system. Usually, the cyber-attacks are characterized as hybrid TTPs (Tactics …
information system. Usually, the cyber-attacks are characterized as hybrid TTPs (Tactics …
[HTML][HTML] Emerging framework for attack detection in cyber-physical systems using heuristic-based optimization algorithm
In recent days, cyber-physical systems (CPS) have become a new wave generation of
human life, exploiting various smart and intelligent uses of automotive systems. In these …
human life, exploiting various smart and intelligent uses of automotive systems. In these …
False data-injection attack detection in cyber–physical systems with unknown parameters: A deep reinforcement learning approach
K Liu, H Zhang, Y Zhang, C Sun - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This article studies the detection of discontinuous false data-injection (FDI) attacks on cyber–
physical systems (CPSs). Considering the unknown stochastic properties of the process …
physical systems (CPSs). Considering the unknown stochastic properties of the process …
An identification strategy for unknown attack through the joint learning of space–time features
H Wang, S Mumtaz, H Li, JX Liu, F Yang - Future Generation Computer …, 2021 - Elsevier
Deep learning (DL) can effectively extract the features of attack behaviours and identify
unknown attack behaviours. However, the current DL-based methods separately learn …
unknown attack behaviours. However, the current DL-based methods separately learn …
Machine learning approaches in cyber attack detection and characterization in IoT enabled cyber-physical systems
S Kantimahanthi, JVD Prasad… - … on Intelligent Data …, 2023 - ieeexplore.ieee.org
Cyber-physical systems (CPS) enabled by the Internet of Things (IoT) provide unique
security challenges since solutions designed for traditional Operational Technology (OT) …
security challenges since solutions designed for traditional Operational Technology (OT) …
Exquisite feature selection for machine learning powered probing attack detection
Network attacks have been intensively studied by recent research. Probing attacks,
however, seem not receiving as much attention as others, because they do not explicitly …
however, seem not receiving as much attention as others, because they do not explicitly …
[HTML][HTML] Transfer learning for detecting unknown network attacks
Network attacks are serious concerns in today's increasingly interconnected society. Recent
studies have applied conventional machine learning to network attack detection by learning …
studies have applied conventional machine learning to network attack detection by learning …
Interpretable deep learning method for attack detection based on spatial domain attention
H Liu, B Lang, S Chen, M Yuan - 2021 IEEE Symposium on …, 2021 - ieeexplore.ieee.org
Deep learning methods can directly extract effective features from original data. However,
this type of model is complex and considered to be a “black box”, which leads to low …
this type of model is complex and considered to be a “black box”, which leads to low …