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 391–400 of 800 papers

TitleStatusHype
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
Using EBGAN for Anomaly Intrusion Detection—0
Hands-on Wireless Sensing with Wi-Fi: A Tutorial—0
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic ClassificationCode0
RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System—0
Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus—0
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing—0
A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion DetectionCode0
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars—0
Multibit Tries Packet Classification with Deep Reinforcement Learning—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