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 626–650 of 800 papers

TitleStatusHype
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids—0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort—0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars—0
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
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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