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

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Showing 276–300 of 800 papers

TitleStatusHype
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Real-time Regular Expression Matching—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection—0
Kairos: Practical Intrusion Detection and Investigation using Whole-system ProvenanceCode1
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System—0
Towards Reliable Rare Category Analysis on Graphs via Individual CalibrationCode0
Man-in-the-Middle Intrusion Detection Based on CNN-LSTM Model—0
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks—0
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation—0
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction—0
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection—0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Online Self-Supervised Deep Learning for Intrusion Detection Systems—0
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier—0
Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments—0
Intrusion Detection: A Deep Learning 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