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

Showing 411420 of 800 papers

TitleStatusHype
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces0
Nature Inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Grey Wolf Algorithm Techniques0
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data0
Zero-day DDoS Attack Detection0
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networksCode0
ECU Identification using Neural Network Classification and Hyperparameter Tuning0
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset0
Designing an Artificial Immune System inspired Intrusion Detection System0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
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1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
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1MSTREAM-AEAUC0.9Unverified