[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Adefemi Alimi et al., 2022 - Google Patents

Refined LSTM based intrusion detection for denial-of-service attack in Internet of Things

Adefemi Alimi et al., 2022

View HTML
Document ID
15526635052093949232
Author
Adefemi Alimi K
Ouahada K
Abu-Mahfouz A
Rimer S
Alimi O
Publication year
Publication venue
Journal of sensor and actuator networks

External Links

Snippet

The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • 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
    • 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
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • 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
    • 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
    • G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • 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
    • 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
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based 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

Similar Documents

Publication Publication Date Title
Dutta et al. A deep learning ensemble for network anomaly and cyber-attack detection
Fu et al. A deep learning model for network intrusion detection with imbalanced data
Ferrag et al. Rdtids: Rules and decision tree-based intrusion detection system for internet-of-things networks
Hindy et al. Utilising deep learning techniques for effective zero-day attack detection
Adefemi Alimi et al. Refined LSTM based intrusion detection for denial-of-service attack in Internet of Things
Song et al. Analysis of autoencoders for network intrusion detection
Abdullahi et al. Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review
Chaganti et al. A particle swarm optimization and deep learning approach for intrusion detection system in internet of medical things
Ferrag et al. Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0
Toldinas et al. A novel approach for network intrusion detection using multistage deep learning image recognition
Ahmad et al. Anomaly detection using deep neural network for IoT architecture
Qazi et al. A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection
Gao et al. Research on network intrusion detection based on incremental extreme learning machine and adaptive principal component analysis
Kim et al. Intelligent detection of iot botnets using machine learning and deep learning
Fotiadou et al. Network traffic anomaly detection via deep learning
Qazi et al. HDLNIDS: hybrid deep-learning-based network intrusion detection system
Ashraf et al. A deep learning-based smart framework for cyber-physical and satellite system security threats detection
Ullah et al. Towards a hybrid deep learning model for anomalous activities detection in internet of things networks
Essop et al. Generating datasets for anomaly-based intrusion detection systems in iot and industrial iot networks
Alabsi et al. CNN-CNN: dual convolutional neural network approach for feature selection and attack detection on internet of things networks
Musafer et al. An enhanced design of sparse autoencoder for latent features extraction based on trigonometric simplexes for network intrusion detection systems
Ali et al. Low rate DDoS detection using weighted federated learning in SDN control plane in IoT network
Tomer et al. Detecting IoT attacks using an ensemble machine learning model
Bahaa et al. Monitoring real time security attacks for IoT systems using DevSecOps: a systematic literature review
Elubeyd et al. Hybrid deep learning approach for automatic DoS/DDoS attacks detection in software-defined networks