SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 151–200 of 800 papers

TitleStatusHype
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments—0
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning—0
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity—0
Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection—0
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
Flow Exporter Impact on Intelligent Intrusion Detection Systems—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model SelectionCode0
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Machine Learning-based Android Intrusion Detection System—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
Optimized IoT Intrusion Detection using Machine Learning Technique—0
Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles—0
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications—0
Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks—0
An AutoML-based approach for Network Intrusion Detection—0
The importance of the clustering model to detect new types of intrusion in data traffic—0
Feature Selection for Network Intrusion Detection—0
Take Package as Language: Anomaly Detection Using TransformerCode0
Intelligent Green Efficiency for Intrusion Detection—0
Sdn Intrusion Detection Using Machine Learning Method—0
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection—0
SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type ClassificationCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability—0
A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection SystemsCode0
XAI-based Feature Selection for Improved Network Intrusion Detection SystemsCode0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data—0
Data Distribution ValuationCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security—0
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion DetectionCode0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified