연구동향 분석

IOT 환경에서의 오토인코더 기반 특징 추출을 이용한 네트워크 침입탐지 시스템

이주화 박기현 2019년

논문상세정보
인용/피인용
' IOT 환경에서의 오토인코더 기반 특징 추출을 이용한 네트워크 침입탐지 시스템' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • IOT
  • Machine Learning
  • auto encoder
  • nids
  • unsupervised learning
  • 기계학습
  • 네트워크 침입탐지 시스템
  • 비지도 학습
  • 사물인터넷
  • 오토인코더
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
3,699 0

0.0%

' IOT 환경에서의 오토인코더 기반 특징 추출을 이용한 네트워크 침입탐지 시스템' 의 참고문헌

  • Using Different Cost Functions to Train Stacked Auto-Encoders
  • Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
  • Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services
  • Preprocessing Based Solution for the Vanishing Gradient Problem in Recurrent Neural Networks
  • Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection
    Ahmad I. [2018]
  • Performance Comparison of Features Reduction Techniques for Intrusion Detection System
    R. Datti [2012]
  • Overfitting Problem and the Over-training in the Era of Data: Particularly for Artificial Neural Networks
  • Machine Learning Repository
  • Linear Regression-Based Efficient SVM Learning for Large-Scale Classification
    Jianxin Wu [2015]
  • Learning Automata based Feature Selection for Network Traffic Intrusion Detection
    Yu Su [2018]
  • Intrusion Detection in Network Systems Through Hybrid Supervised and Unsupervised Machine Learning Process: A Case Study on the ISCX Dataset
  • Hybrid Intrusion Detection: Combining Decision Tree and Gaussian Mixture Model
  • Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning
  • Feature Selection for efficient Intrusion Detection using Attribute Ratio
    H. Chae [2014]
  • Efficient Feature Extraction using Apache Spark for Network Behavior Anomaly Detection
    Xiaoming Ye [2018]
  • Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System
    INGHAO YAN [2018]
  • Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
    Tie Luo [2018]
  • Deep Learning Models for Cyber Security in IoT Networks
  • Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection
  • Comparative Analysis of the Impact of Discretization on the Classification with Naïve Bayes and Semi-Naïve Bayes Classifiers
  • Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm
    K. Park [2018]
  • Autoencoder-based Network Anomaly Detection
  • AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning
    I. Alrashdi [2019]
  • A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks
  • A Hybrid Network Intrusion Detection Technique using Random Forests
    J. Zhang [2006]