相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Machine learning methods for cyber security intrusion detection: Datasets and comparative study
Ilhan Firat Kilincer et al.
COMPUTER NETWORKS (2021)
A Review on the Security of the Internet of Things: Challenges and Solutions
Oludare Isaac Abiodun et al.
WIRELESS PERSONAL COMMUNICATIONS (2021)
Machine Learning Based Intrusion Detection Systems for IoT Applications
Abhishek Verma et al.
WIRELESS PERSONAL COMMUNICATIONS (2020)
A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets
Smitha Rajagopal et al.
SECURITY AND COMMUNICATION NETWORKS (2020)
Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features
Yan Naung Soe et al.
ELECTRONICS (2020)
Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture†
Yan Naung Soe et al.
SENSORS (2020)
Anomaly-Based Intrusion Detection System Using Support Vector Machine
S. Krishnaveni et al.
ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS (2020)
A Review of the Advancement in Intrusion Detection Datasets
Ankit Thakkar et al.
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE (2020)
Network Intrusion Detection System Using Random Forest and Decision Tree Machine Learning Techniques
T. Tulasi Bhavani et al.
FIRST INTERNATIONAL CONFERENCE ON SUSTAINABLE TECHNOLOGIES FOR COMPUTATIONAL INTELLIGENCE (2020)
CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection
Kurniabudi et al.
IEEE ACCESS (2020)
Internet of Things: A survey on machine learning-based intrusion detection approaches
Kelton A. P. da Costa et al.
COMPUTER NETWORKS (2019)
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Hongyu Liu et al.
APPLIED SCIENCES-BASEL (2019)
Intrusion Detection Using Big Data and Deep Learning Techniques
Osama Faker et al.
PROCEEDINGS OF THE 2019 ANNUAL ACM SOUTHEAST CONFERENCE (ACMSE 2019) (2019)
Intrusion Detection Based on Approximate Information Entropy for Random Forest Classification
Le Yang et al.
ICBDC 2019: PROCEEDINGS OF 2019 4TH INTERNATIONAL CONFERENCE ON BIG DATA AND COMPUTING (2019)
A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks
Imtiaz Ullah et al.
2019 16TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) (2019)
A Multi-Level Intrusion Detection System for Wireless Sensor Networks Based on Immune Theory
Vishwa Teja Alaparthy et al.
IEEE ACCESS (2018)
Statistical analysis of CIDDS-001 dataset for Network Intrusion Detection Systems using Distance-based Machine Learning
Abhishek Verma et al.
6TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS (2018)
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
Gisung Kim et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
A Survey of Intrusion Detection Systems in Wireless Sensor Networks
Ismail Butun et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2014)
A comparative study on the effect of feature selection on classification accuracy
Esra Mahsereci Karabulut et al.
FIRST WORLD CONFERENCE ON INNOVATION AND COMPUTER SCIENCES (INSODE 2011) (2012)
Specification-based Intrusion Detection for Advanced Metering Infrastructures
Robin Berthier et al.
2011 IEEE 17TH PACIFIC RIM INTERNATIONAL SYMPOSIUM ON DEPENDABLE COMPUTING (PRDC) (2011)