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A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems

https://doi.org/10.1016/j.array.2025.100406

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What this work says

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

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Evaluation of Supervised Machine Learning Algorithms for Multi-class Intrusion Detection Systems

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

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An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset

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Unification of K-Nearest Neighbor (KNN) with Distance Aware Algorithm for Intrusion Detection in Evolving Networks Like IoT

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Advancing Network Security in Industrial IoT: A Deep Dive into AI-Enabled Intrusion Detection Systems

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Extreme minority class detection in imbalanced data for network intrusion

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Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy

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Detection of Network Attacks using Machine Learning and Deep Learning Models

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Dependable intrusion detection system using deep convolutional neural network: A Novel framework and performance evaluation approach

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Research Trends in Network-Based Intrusion Detection Systems: A Review

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A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

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LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks

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XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud

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Design and Analysis of Intrusion Detection Using Deep Learning Models

inferredcites →https://doi.org/10.1109/cube.2013.31openalex-snapshot

Intrusion Detection Using Random Forests Classifier with SMOTE and Feature Reduction

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Network Intrusion Detection Using Weighted Voting Ensemble Deep Learning Model

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A Few-Shot-Based Model-Agnostic Meta-Learning for Intrusion Detection in Security of Internet of Things

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NFV: Security Threats and Best Practices

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Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach

inferredcites →https://doi.org/10.1155/2021/1230593openalex-snapshot

A Reliable Network Intrusion Detection Approach Using Decision Tree with Enhanced Data Quality

inferredcites →https://doi.org/10.1201/9780429270567-6openalex-snapshot

Network Intrusion Detection with XGBoost

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Signature-Based Intrusion Detection System for IoT

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Intrusion Detection Using Machine Learning and Deep Learning Models on Cyber Security Attacks

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Intrusion Detection Systems, Issues, Challenges, and Needs

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On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks

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A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller

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Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

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A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions

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A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems

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Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller

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Development Cyber Risk Assessment for Intrusion Detection Using Enhanced Random Forest

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Real Time Anomaly Detection in Network Traffic: A Comparative Analysis of Machine Learning Algorithms

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