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 671–680 of 800 papers

TitleStatusHype
Deep Reinforcement Learning for Cyber Security—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data—0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?—0
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework—0
Modern Problems Require Modern Solutions: Hybrid Concepts for Industrial Intrusion Detection—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—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