+

Wang et al., 2020 - Google Patents

A host‐based anomaly detection framework using XGBoost and LSTM for IoT devices

Wang et al., 2020

View PDF @Full View
Document ID
3784228546530125160
Author
Wang X
Lu X
Publication year
Publication venue
Wireless Communications and Mobile Computing

External Links

Snippet

The Internet of Things (IoT) is rapidly spreading in various application scenarios through its salient features in ubiquitous device connections, ranging from agriculture and industry to transportation and other fields. As the increasing spread of IoT applications, IoT security is …
Continue reading at onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • 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
    • G06F21/56Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/562Static detection
    • G06F21/563Static detection by source code 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
    • G06F21/56Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/566Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
    • 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
    • G06F21/554Detecting local intrusion or implementing counter-measures involving event detection and direct action
    • 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
    • G06F21/552Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
    • 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
    • G06F21/57Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
    • G06F21/577Assessing vulnerabilities and evaluating computer system security
    • 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
    • G06F21/52Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity ; Preventing unwanted data erasure; Buffer overflow
    • 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
    • H04L63/1408Network 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
    • H04L63/1441Countermeasures against malicious traffic
    • H04L63/145Countermeasures against malicious traffic the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms

Similar Documents

Publication Publication Date Title
Wang et al. A host‐based anomaly detection framework using XGBoost and LSTM for IoT devices
Tsimenidis et al. Deep learning in IoT intrusion detection
Wani et al. SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)
Disha et al. Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique
Rehman Javed et al. Ensemble adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles
Wu et al. Research on artificial intelligence enhancing internet of things security: A survey
Shrivastava et al. Attack detection and forensics using honeypot in IoT environment
Malik et al. [Retracted] An Improved Deep Belief Network IDS on IoT‐Based Network for Traffic Systems
Manhas et al. Implementation of intrusion detection system for internet of things using machine learning techniques
Pakmehr et al. DDoS attack detection techniques in IoT networks: a survey
KR102259760B1 (en) System for providing whitelist based abnormal process analysis service
Abirami et al. Building an ensemble learning based algorithm for improving intrusion detection system
Smiliotopoulos et al. Detecting lateral movement: A systematic survey
Kuppa et al. Finding rats in cats: Detecting stealthy attacks using group anomaly detection
Mathane et al. Predictive analysis of ransomware attacks using context-aware AI in IoT systems
Kalpana Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system
Rani et al. A framework for the identification of suspicious packets to detect anti-forensic attacks in the cloud environment
Pashamokhtari et al. AdIoTack: quantifying and refining resilience of decision tree ensemble inference models against adversarial volumetric attacks on IoT networks
Anande et al. Synthetic network traffic data generation and classification of advanced persistent threat samples: A case study with gans and xgboost
Wu et al. Improving convolutional neural network-based webshell detection through reinforcement learning
Mallidi et al. Optimizing intrusion detection for IoT: a systematic review of machine learning and deep learning approaches with feature selection and data balancing
Nalinipriya et al. Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN
Mangayarkarasi et al. A robust malware traffic classifier to combat security breaches in industry 4.0 applications
Khoulimi et al. An Overview of Explainable Artificial Intelligence for Cyber Security
Yang et al. A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载