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

Selim et al., 2023 - Google Patents

DAE-BILSTM: A Fog-Based Intrusion Detection Model Using Deep Learning for IoT

Selim et al., 2023

View PDF
Document ID
13595277051346774744
Author
Selim I
Sadek R
Publication year
Publication venue
J. Theor. Appl. Inf. Technol

External Links

Snippet

Fog computing efficiently brings services to the edge network because it facilitates processing, communication, and storage to be closer to the edge devices. Fog computing is principally used for the Internet of Things (IoT) instead of cloud computing as an ideal option …
Continue reading at www.jatit.org (PDF) (other versions)

Classifications

    • 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
    • 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
    • H04L63/1441—Countermeasures against malicious traffic
    • H04L63/1458—Denial of Service
    • 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
    • G06F21/554—Detecting local intrusion or implementing counter-measures involving event detection and direct action
    • 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
    • H04L63/1483—Countermeasures against malicious traffic service impersonation, e.g. phishing, pharming or web spoofing
    • 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
    • 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
    • H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
    • H04L63/0209—Architectural arrangements, e.g. perimeter networks or demilitarized zones
    • H04L63/0218—Distributed architectures, e.g. distributed firewalls

Similar Documents

Publication Publication Date Title
Halbouni et al. Machine learning and deep learning approaches for cybersecurity: A review
Ge et al. Towards a deep learning-driven intrusion detection approach for Internet of Things
Ravi et al. Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system
Catak et al. Distributed denial of service attack detection using autoencoder and deep neural networks
Al-Fawa'reh et al. Cyber threat intelligence using PCA-DNN model to detect abnormal network behavior
Farhan et al. Performance analysis of intrusion detection for deep learning model based on CSE-CIC-IDS2018 dataset
Strecker et al. An analysis of IoT cyber security driven by machine learning
Ortet Lopes et al. Towards effective detection of recent DDoS attacks: A deep learning approach
Hammad et al. T‐SNERF: A novel high accuracy machine learning approach for Intrusion Detection Systems
Khan et al. A hybrid deep learning-based intrusion detection system for IoT networks
Zaib et al. Deep learning based cyber bullying early detection using distributed denial of service flow
Fernando et al. Enhancing intrusion detection in iot communications through ml model generalization with a new dataset (idsai)
Hagar et al. Big Data Analytic Using Machine Learning Algorithms For Intrusion Detection System: A Survey
Selim et al. DAE-BILSTM: A Fog-Based Intrusion Detection Model Using Deep Learning for IoT
Samha et al. Intrusion detection system using hybrid convolutional neural network
Sivasakthi et al. HybridRobustNet: enhancing detection of hybrid attacks in IoT networks through advanced learning approach
Koppula et al. Lnkdsea: Machine learning based iot/iiot attack detection method
Alzahrani et al. [Retracted] Computational Intelligence Approaches in Developing Cyberattack Detection System
Nuhu et al. Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review
Abirami et al. Proactive network packet classification using artificial intelligence
Farhat et al. CADS-ML/DL: efficient cloud-based multi-attack detection system
Janani et al. Iot security and privacy using deep learning model: a review
Beulah et al. Detection of DDoS attack using ensemble machine learning techniques
Farrukh et al. Xg-nid: Dual-modality network intrusion detection using a heterogeneous graph neural network and large language model
Molcer et al. Machine learning based network intrusion detection system for internet of things cybersecurity