De Souza et al., 2022 - Google Patents
Two-step ensemble approach for intrusion detection and identification in IoT and fog computing environmentsDe Souza et al., 2022
- Document ID
- 8249948893533628219
- Author
- De Souza C
- Westphall C
- Machado R
- Publication year
- Publication venue
- Computers & Electrical Engineering
External Links
Snippet
Abstract Due to Internet of Things devices resource limitations, security often does not receive enough attention. Intrusion detection approaches are important for identifying attacks and taking appropriate countermeasures for each specific threat. This work presents …
- 238000001514 detection method 0 title abstract description 113
Classifications
-
- 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
- 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
- G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1416—Event detection, e.g. attack signature detection
-
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- 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
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1433—Vulnerability analysis
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
De Souza et al. | Two-step ensemble approach for intrusion detection and identification in IoT and fog computing environments | |
Aleesa et al. | Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions | |
Aljawarneh et al. | Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model | |
Zhao et al. | A review of computer vision methods in network security | |
Chapaneri et al. | A comprehensive survey of machine learning-based network intrusion detection | |
Sandosh et al. | Enhanced intrusion detection system via agent clustering and classification based on outlier detection | |
Indrasiri et al. | Robust ensemble machine learning model for filtering phishing URLs: Expandable random gradient stacked voting classifier (ERG-SVC) | |
Garg et al. | HyClass: Hybrid classification model for anomaly detection in cloud environment | |
Geetha et al. | A FKPCA-GWO WDBiLSTM classifier for intrusion detection system in cloud environments | |
Patil et al. | Majority voting and feature selection based network intrusion detection system | |
Srinivas et al. | A novel machine learning inspired algorithm to predict real-time network intrusions | |
Obaid et al. | An adaptive approach for internet phishing detection based on log data | |
Hammad et al. | MMM-RF: A novel high accuracy multinomial mixture model for network intrusion detection systems | |
Dharaneish et al. | Comparative analysis of deep learning and machine learning models for network intrusion detection | |
Brittorameshkumar | OSS-RF: Intrusion detection using optimized sine swarm based random forest classifier on unsw-nb15 dataset | |
Sheikhi et al. | Safeguarding cyberspace: Enhancing malicious website detection with PSOoptimized XGBoost and firefly-based feature selection | |
Rao et al. | Cyberattack defense mechanism using deep learning techniques in software-defined networks | |
Shorfuzzaman | Detection of cyber attacks in IoT using tree-based ensemble and feedforward neural network | |
Lin et al. | Behaviour classification of cyber attacks using convolutional neural networks | |
Pantelakis | Adversarial machine learning attacks against network intrusion detection systems | |
Khan et al. | Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions | |
Ali et al. | Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey | |
Lugo-Cordero et al. | What defines an intruder? an intelligent approach | |
Kumar et al. | Advancements in metaverse security: phishing website detection through optimal feature selection and random forest classifier | |
Mc Carthy | Methods for Improving Robustness against Adversarial Machine Learning Attacks |