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 226–250 of 800 papers

TitleStatusHype
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol—0
Feature Selection using the concept of Peafowl Mating in IDS—0
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection SystemCode0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed—0
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks—0
A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN—0
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
Real-time Network Intrusion Detection via Decision Transformers—0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems—0
Intrusion Detection System with Machine Learning and Multiple Datasets—0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—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