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 601–625 of 800 papers

TitleStatusHype
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation—0
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review—0
A cognitive based Intrusion detection system—0
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet DataCode1
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks—0
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction—0
Hybrid Model For Intrusion Detection Systems—0
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
Machine Learning based Anomaly Detection for 5G Networks—0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
IoT Behavioral Monitoring via Network Traffic Analysis—0
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process—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