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

Diro et al., 2018 - Google Patents

Leveraging LSTM networks for attack detection in fog-to-things communications

Diro et al., 2018

Document ID
4732052466966724832
Author
Diro A
Chilamkurti N
Publication year
Publication venue
IEEE Communications Magazine

External Links

Snippet

The evolution and sophistication of cyber-attacks need resilient and evolving cybersecurity schemes. As an emerging technology, the Internet of Things (IoT) inherits cyber-attacks and threats from the IT environment despite the existence of a layered defensive security …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425Traffic logging, e.g. anomaly detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416Event detection, e.g. attack signature detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441Countermeasures against malicious traffic
    • H04L63/1458Denial of Service
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1433Vulnerability analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55Detecting local intrusion or implementing counter-measures
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance or administration or management of packet switching networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00Cryptographic mechanisms or cryptographic arrangements for secret or secure communication
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00Data switching networks
    • H04L12/54Store-and-forward switching systems
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W12/00Security arrangements, e.g. access security or fraud detection; Authentication, e.g. verifying user identity or authorisation; Protecting privacy or anonymity

Similar Documents

Publication Publication Date Title
Diro et al. Leveraging LSTM networks for attack detection in fog-to-things communications
Ge et al. Towards a deep learning-driven intrusion detection approach for Internet of Things
Kumar et al. A Distributed framework for detecting DDoS attacks in smart contract‐based Blockchain‐IoT Systems by leveraging Fog computing
Rathore et al. Semi-supervised learning based distributed attack detection framework for IoT
Khraisat et al. A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
Amaizu et al. Composite and efficient DDoS attack detection framework for B5G networks
Samarakoon et al. 5g-nidd: A comprehensive network intrusion detection dataset generated over 5g wireless network
Jothi et al. WILS-TRS—A novel optimized deep learning based intrusion detection framework for IoT networks
Tayyab et al. ICMPv6-based DoS and DDoS attacks detection using machine learning techniques, open challenges, and blockchain applicability: A review
Rajendran et al. Detection of DoS attacks in cloud networks using intelligent rule based classification system
Tufan et al. Anomaly-based intrusion detection by machine learning: A case study on probing attacks to an institutional network
Ortet Lopes et al. Towards effective detection of recent DDoS attacks: A deep learning approach
Rawat et al. Rooted learning model at fog computing analysis for crime incident surveillance
Kachavimath et al. A deep learning-based framework for distributed denial-of-service attacks detection in cloud environment
Quincozes et al. An extended evaluation on machine learning techniques for Denial-of-Service detection in Wireless Sensor Networks
Prajisha et al. An efficient intrusion detection system for MQTT-IoT using enhanced chaotic salp swarm algorithm and LightGBM
Hassan et al. New advancements in cybersecurity: A comprehensive survey
Saini et al. A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection
Verma et al. A detailed survey of denial of service for IoT and multimedia systems: Past, present and futuristic development
Zaib et al. Deep learning based cyber bullying early detection using distributed denial of service flow
Chen et al. A DDoS attack defense method based on blockchain for IoTs devices
Srilatha et al. DDoSNet: A deep learning model for detecting network attacks in cloud computing
Alshehri et al. SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT
Radivilova et al. Statistical and Signature Analysis Methods of Intrusion Detection
Selim et al. DAE-BILSTM: A Fog-Based Intrusion Detection Model Using Deep Learning for IoT