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Partial grid false data injection attacks against state estimation
Published 2019“…The addition of an external communication layer to the power system has left it vulnerable to cyberattacks. False data injection (FDI) can be used to manipulate measurements that are used to estimate the state of the power system. …”
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Modified Particle Filters for Detection of False Data Injection Attacks and State Estimation in Networked Nonlinear Systems
Published 2022“…<p>Networked control systems which transfer data over communication networks may suffer from malicious cyber attacks by injecting false data to the transferred information. …”
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A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems
Published 2023“…A MATLAB/Simulink-based simulation model of the process validated with actual data from a local plant is used. …”
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Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data
Published 2021“…In addition, for grouping similar antipatterns, a clustering process was performed to eradicate the design errors. …”
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Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems
Published 2021“…Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.…”
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Non-Linear Profile Monitoring Using Artificial Neural Network Fault Detection
Published 2018Get full text
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7
Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning
Published 2023“…After that, we introduce three novel attacks, namely Distillation, No-Adversarial-Sample-Training, and False-Labeling, which can be launched during the AT process to make the global model susceptible to evasion at inference time. …”
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ASTER mapping of gypsum deposits of Thumrait region of southern Oman
Published 2020“…<p>This study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. …”
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Growing hierarchical self-organizing map for filtering intrusion detection alarms. (c2007)
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Filtering intrusion detection alarms
Published 2010“…We present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by a NIDS. …”
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TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Published 2020“…However, high dimensional data present a significant challenge for machine learning techniques. …”
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Growing hierarchical self-organizing map for filtering intrusion detection alarms
Published 2008“…Our data mining technique is based on a Growing Hierarchical Self-Organizing Map (GHSOM) that adjusts its architecture during an unsupervised training process according to the characteristics of the input alarm data. …”
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Design of A Theoretical Framework For A Real-Time Fire Evacuation Guidance System
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15
A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. …”
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16
Forecasting Emerging Stock Market Crashes via Machine Learning
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17
DASSI: differential architecture search for splice identification from DNA sequences
Published 2022“…<h2>Background</h2> <p>The data explosion caused by unprecedented advancements in the field of genomics is constantly challenging the conventional methods used in the interpretation of the human genome. …”
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Security attacks on smart grid scheduling and their defences: a game-theoretic approach
Published 2019“…The existence of a novel class of false data injection attacks that are based on modifying forecasted demand data is demonstrated, and the impact of the attacks on a typical system’s parameters is identified, using a simulated scenario. …”
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Using machine learning algorithm for detection of cyber-attacks in cyber physical systems
Published 2022“…We present the new framework for the detection of cyberattacks, which makes use of AI and ML. We begin a process to cleaning up the data in the CPS database by applying normalization to eliminate errors and duplication. …”
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