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PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

https://doi.org/10.3390/electronics10101148

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Machine Learning Applied to Cyber Operations

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Cybersecurity and Secure Information Systems

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Application of Deep Learning Architectures for Cyber Security

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Expansion of Cyber Attack Data from Unbalanced Datasets Using Generative Adversarial Networks

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Cyber Security: The Lifeline of Information and Communication Technology

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Anomalous Web Payload Detection: Evaluating the Resilience of 1-Grams Based Classifiers

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Domain Adaptation in Computer Vision Applications

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A Comprehensive Survey on Domain Adaptation for Visual Applications

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A Deep Learning Method to Detect Web Attacks Using a Specially Designed CNN

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Anomalous Payload-Based Network Intrusion Detection

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Knowledge Discovery in Databases: PKDD 2007

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A novel approach to intrusion detection using SVM ensemble with feature augmentation

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An effective intrusion detection framework based on SVM with feature augmentation

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A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

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Ensemble Transfer Learning Algorithm

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Machine Learning and Deep Learning Methods for Cybersecurity

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Feature-Based Transfer Learning Based on Distribution Similarity

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Deep Learning Approach for Intelligent Intrusion Detection System

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An Adaptive Ensemble Machine Learning Model for Intrusion Detection

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AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection

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Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

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Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning

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M2m: Imbalanced Classification via Major-to-Minor Translation

inferredcites →https://doi.org/10.1109/icbda.2019.8713213openalex-snapshot

A Transfer Learning Approach for Network Intrusion Detection

inferredcites →https://doi.org/10.1109/icc40277.2020.9148632openalex-snapshot

Intrusion Detection for Smart Home Security Based on Data Augmentation with Edge Computing

inferredcites →https://doi.org/10.1109/icme.2019.00211openalex-snapshot

Easy Transfer Learning By Exploiting Intra-Domain Structures

inferredcites →https://doi.org/10.1109/icmla.2018.00174openalex-snapshot

Web Application Attacks Detection Using Machine Learning Techniques

inferredcites →https://doi.org/10.1109/icsmc.2008.4811688openalex-snapshot

Semi-supervised learning methods for network intrusion detection

inferredcites →https://doi.org/10.1109/ispa.2009.92openalex-snapshot

Distributed Transfer Network Learning Based Intrusion Detection

inferredcites →https://doi.org/10.1109/iwscn.2011.6827720openalex-snapshot

Applying feature selection to payload-based Web Application Firewalls

inferredcites →https://doi.org/10.1109/jproc.2020.3004555openalex-snapshot

A Comprehensive Survey on Transfer Learning

inferredcites →https://doi.org/10.1109/milcom.2017.8170749openalex-snapshot

Feature-based transfer learning for network security

inferredcites →https://doi.org/10.1109/secon.2018.8479130openalex-snapshot

Vulnerability Analysis of Content Management Systems to SQL Injection Using SQLMAP

inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-snapshot

A Survey on Transfer Learning

inferredcites →https://doi.org/10.1109/tr.2018.2805763openalex-snapshot

A Machine-Learning-Driven Evolutionary Approach for Testing Web Application Firewalls

inferredcites →https://doi.org/10.1145/3195106.3195107openalex-snapshot

Evaluating CNN and LSTM for Web Attack Detection

inferredcites →https://doi.org/10.1145/3287921.3287946openalex-snapshot

Detecting Attacks on Web Applications using Autoencoder

inferredcites →https://doi.org/10.1145/3309537openalex-snapshot

Online Heterogeneous Transfer Learning by Knowledge Transition

inferredcites →https://doi.org/10.1145/3386723.3387847openalex-snapshot

A Review on Cybersecurity

inferredcites →https://doi.org/10.1145/972374.972384openalex-snapshot

Honeycomb

inferredcites →https://doi.org/10.1155/2018/4943509openalex-snapshot

TR-IDS: Anomaly-Based Intrusion Detection through Text-Convolutional Neural Network and Random Forest

inferredcites →https://doi.org/10.11591/ijins.v1i3.704openalex-snapshot

Pattern Based Network Security Using Semi-supervised Learning

inferredcites →https://doi.org/10.1186/s13635-019-0084-4openalex-snapshot

Transfer learning for detecting unknown network attacks

inferredcites →https://doi.org/10.1186/s40537-016-0043-6openalex-snapshot

A survey of transfer learning

inferredcites →https://doi.org/10.1186/s42400-019-0038-7openalex-snapshot

Survey of intrusion detection systems: techniques, datasets and challenges

inferredcites →https://doi.org/10.14257/ijsia.2018.12.4.02openalex-snapshot

A Survey of Cyber Security Approaches for Attack Detection, Prediction, and Prevention

inferredcites →https://doi.org/10.48550/arxiv.1212.5701openalex-snapshot

ADADELTA: An Adaptive Learning Rate Method

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Machine learning-assisted virtual patching of web applications

inferredcites →https://doi.org/10.48550/arxiv.1901.07949openalex-snapshot

Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework

inferredcites →https://doi.org/10.5281/zenodo.1336811openalex-snapshot

Anomaly-Based Web Application Firewall Using Http-Specific Features And One-Class Svm

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review

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Performance Analysis of Visualization-Based Malware Classification Using CNN

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Zero-Day Threat Detection in Autonomous Vehicles Using Few-Shot LSTM Autoencoders on CAN Bus Data

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Deep Learning Methods in Web Intrusion Detection: A Systematic Review

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Efficient Malware Classification using Transfer Learning and Stacked Ensemble Techniques

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Towards Zero-Shot Flow-Based Cyber-Security Anomaly Detection Framework

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Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders

inferred← citeshttps://doi.org/10.3390/electronics11091346openalex-snapshot

EmSM: Ensemble Mixed Sampling Method for Classifying Imbalanced Intrusion Detection Data

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