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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 800 papers

TitleStatusHype
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Using EBGAN for Anomaly Intrusion Detection—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
V-CNN: When Convolutional Neural Network encounters Data Visualization—0
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection—0
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems—0
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning—0
Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization—0
Zero-day DDoS Attack Detection—0
Zero-shot learning approach to adaptive Cybersecurity using Explainable AI—0
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV—0
Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection -- An Analysis on CIC-AWS-2018 dataset—0
Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles—0
Hands-on Wireless Sensing with Wi-Fi: A Tutorial—0
Hardening Random Forest Cyber Detectors Against Adversarial Attacks—0
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection—0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data—0
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
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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