Teng et al., 2022 - Google Patents
Deep learning-based risk management of financial market in smart gridTeng et al., 2022
- Document ID
- 11751658717107756662
- Author
- Teng T
- Ma L
- Publication year
- Publication venue
- Computers and Electrical Engineering
External Links
Snippet
Smart grid control systems (SGCSs) become more vulnerable to cyber-attacks because of the combination of the Internet of Things and communication systems. Conventional intrusion detection systems (IDSs) that have been essentially improved in order to secure …
- 238000001514 detection method 0 abstract description 27
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kim et al. | APAD: Autoencoder-based payload anomaly detection for industrial IoE | |
Reddy et al. | Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities | |
Khan et al. | HML-IDS: A hybrid-multilevel anomaly prediction approach for intrusion detection in SCADA systems | |
Foroutan et al. | Detection of false data injection attacks against state estimation in smart grids based on a mixture Gaussian distribution learning method | |
Teng et al. | Deep learning-based risk management of financial market in smart grid | |
Wang et al. | Cyber-attacks detection in industrial systems using artificial intelligence-driven methods | |
Mansouri et al. | Metaheuristic neural networks for anomaly recognition in industrial sensor networks with packet latency and jitter for smart infrastructures | |
Saheed et al. | A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and SCADA systems for smart city infrastructures | |
Nagaraj et al. | Ensemble CorrDet with adaptive statistics for bad data detection | |
Prasanna Srinivasan et al. | Multi label deep learning classification approach for false data injection attacks in smart grid | |
Reddy et al. | Using AI And Machine Learning To Secure Cloud Networks: A Modern Approach To Cybersecurity | |
Al-Ambusaidi et al. | ML-IDS: an efficient ML-enabled intrusion detection system for securing IoT networks and applications | |
Bahadoripour et al. | An explainable multi-modal model for advanced cyber-attack detection in industrial control systems | |
Eid et al. | Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing | |
Mishra et al. | Hybrid deep learning algorithm for smart cities security enhancement through blockchain and internet of things | |
Nwagwughiagwu et al. | Revolutionizing cybersecurity with deep learning: Procedural detection and hardware security in critical infrastructure | |
Yin et al. | Transfer adversarial attacks across industrial intelligent systems | |
Rabie et al. | A security model for smart grid SCADA systems using stochastic neural network | |
Bhusal et al. | Sok: Modeling explainability in security analytics for interpretability, trustworthiness, and usability | |
Li et al. | Real-time monitoring for detection of adversarial subtle process variations | |
Salem | Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review | |
Sharma et al. | Attack detection based on machine learning techniques to safe and secure for CPS—A review | |
Sandeepa et al. | From opacity to clarity: Leveraging XAI for robust network traffic classification | |
Blanc et al. | Interactions between artificial intelligence and cybersecurity to protect future networks | |
Elhag et al. | Toward an improved security performance of industrial internet of things systems |