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 676–700 of 800 papers

TitleStatusHype
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
Convolutional Neural Network for Intrusion Detection System In Cyber Physical Systems—0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
A Compendium on Network and Host based Intrusion Detection Systems—0
Efficient GAN-based method for cyber-intrusion detection—0
Active Learning for Network Intrusion Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning—0
Rallying Adversarial Techniques against Deep Learning for Network Security—0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
A comparative evaluation of novelty detection algorithms for discrete sequences—0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
A short review on Applications of Deep learning for Cyber security—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—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