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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 111–120 of 800 papers

Title	Date	Tasks	Status	Score
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection	Feb 8, 2018	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection	Nov 27, 2021	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	5
Deep Learning Applications for Intrusion Detection in Network Traffic	Jan 13, 2024	Deep LearningFeature Engineering	CodeCode Available	5
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems	Aug 4, 2018	ClassificationGeneral Classification	CodeCode Available	5
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets	May 7, 2025	Anomaly DetectionBinary Classification	CodeCode Available	5
Data Distribution Valuation	Oct 6, 2024	Data ValuationFraud Detection	CodeCode Available	5
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning	Aug 10, 2024	Intrusion DetectionTime Series	CodeCode Available	5
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine Learning	Nov 28, 2023	Intrusion Detection	CodeCode Available	5
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks	Aug 25, 2022	Intrusion Detection	CodeCode Available	5
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection	Apr 2, 2025	Federated LearningIntrusion Detection	CodeCode Available	5

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All datasets CICIDS2017 CIC-DDoS CIC-DoS UNSW-NB15

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Random Forest	Accuracy (%)	98.13	—	Unverified
2	K-Nearest Neighbors	Accuracy (%)	98.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSTREAM-PCA	AUC	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSTREAM-IB	AUC	0.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSTREAM-AE	AUC	0.9	—	Unverified