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 471–480 of 800 papers

TitleStatusHype
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
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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