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 461–470 of 800 papers

TitleStatusHype
Orthogonal variance-based feature selection for intrusion detection systems—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
From Zero-Shot Machine Learning to Zero-Day Attack Detection—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
A Novel Online Incremental Learning Intrusion Prevention System—0
Modern Cybersecurity Solution using Supervised Machine Learning—0
Show:102550
← PrevPage 47 of 80Next →
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