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 751–775 of 800 papers

TitleStatusHype
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Energy-based Models for Video Anomaly Detection—0
Model Selection for Anomaly Detection—0
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Data Mining model in the discovery of trends and patterns of intruder attacks on the data network as a public-sector innovation—0
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry KeysCode0
On the (Statistical) Detection of Adversarial Examples—0
Learning detectors of malicious web requests for intrusion detection in network traffic—0
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey—0
SoK: Applying Machine Learning in Security - A Survey—0
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems—0
"Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Attribute Learning for Network Intrusion Detection—0
Sorting out typicality with the inverse moment matrix SOS polynomial—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection Technique based on Probabilistic Models—0
Learning Privately from Multiparty Data—0
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection—0
More Efficient Topic Modelling Through a Noun Only Approach—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