4.6 Article

DOC-IDS: A Deep Learning-Based Method for Feature Extraction and Anomaly Detection in Network Traffic

Related references

Note: Only part of the references are listed.
Proceedings Paper Computer Science, Artificial Intelligence

Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset

Mohammed M. Alani

Summary: This paper presents an implementation-oriented feature selection method that reduces the number of features while maintaining high accuracy. The proposed reduction results in a dataset focused on making machine learning models more implementable, practical, and efficient, with 5 features. Testing shows that the reduced dataset maintains an accuracy of 99% with a testing time reduction of up to 84%.

INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, ISDA 2021 (2022)

Article Computer Science, Hardware & Architecture

PBCNN: Packet Bytes-based Convolutional Neural Network for Network Intrusion Detection

Lian Yu et al.

Summary: This paper proposes a hierarchical packet byte-based CNN model to improve the reliability of network intrusion detection through feature extraction and few shot learning, achieving superior results in experiments. The hierarchical structure of network traffic plays a crucial role in this process.

COMPUTER NETWORKS (2021)

Article Engineering, Chemical

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Muhammad Ashfaq Khan

Summary: Network attacks are a crucial problem in modern society, and developing effective intrusion detection systems is essential to mitigate the impact of malicious threats. Utilizing deep learning and machine learning techniques, researchers have designed a hybrid convolutional recurrent neural network intrusion detection system that achieves high accuracy in detecting malicious cyberattacks.

PROCESSES (2021)

Article Computer Science, Artificial Intelligence

Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges

Geeta Kocher et al.

Summary: The paper provides a comprehensive overview of the applications and research status of machine learning methods and deep learning methods in intrusion detection. It discusses their performance, advantages, and experimental results. Moreover, it also explores the current research challenges and issues in the field, aiming to assist fellow researchers in the area.

SOFT COMPUTING (2021)

Review Engineering, Multidisciplinary

A Survey on Contrastive Self-Supervised Learning

Ashish Jaiswal et al.

Summary: Self-supervised learning, particularly through contrastive learning, has gained popularity for its cost-effective approach in using self-defined pseudolabels for various downstream tasks. This paper extensively reviews self-supervised methods following the contrastive approach, explaining pretext tasks and different architectures used. Performance comparisons across multiple downstream tasks demonstrate variations in method effectiveness.

TECHNOLOGIES (2021)

Article Computer Science, Information Systems

Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder

Sultan Zavrak et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

Tongtong Su et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

Ren-Hung Hwang et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Learning Deep Features for One-Class Classification

Pramuditha Perera et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

Article Computer Science, Information Systems

A survey of deep learning-based network anomaly detection

Donghwoon Kwon et al.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2019)

Article Computer Science, Information Systems

Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

Yang Yu et al.

SECURITY AND COMMUNICATION NETWORKS (2017)

Review Computer Science, Hardware & Architecture

A survey of network anomaly detection techniques

Mohiuddin Ahmed et al.

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2016)

Article Computer Science, Artificial Intelligence

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)

Article Multidisciplinary Sciences

Reducing the dimensionality of data with neural networks

G. E. Hinton et al.

SCIENCE (2006)

Article Computer Science, Artificial Intelligence

Support vector data description

DMJ Tax et al.

MACHINE LEARNING (2004)

Article Computer Science, Artificial Intelligence

Estimating the support of a high-dimensional distribution

B Schölkopf et al.

NEURAL COMPUTATION (2001)