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 601–625 of 800 papers

TitleStatusHype
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
NERD: Neural Network for Edict of Risky Data Streams—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks—0
Learning With Differential Privacy—0
G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System—0
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation—0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review—0
A cognitive based Intrusion detection system—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks—0
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction—0
Hybrid Model For Intrusion Detection Systems—0
Machine Learning based Anomaly Detection for 5G Networks—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system—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