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 351–375 of 800 papers

TitleStatusHype
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection—0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning—0
Exploring Edge TPU for Network Intrusion Detection in IoT—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Are Trees Really Green? A Detection Approach of IoT Malware Attacks—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning—0
Extreme bandits—0
Fair Anomaly Detection For Imbalanced Groups—0
Fast Feature Reduction in intrusion detection datasets—0
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS—0
Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave—0
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems—0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection—0
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks—0
Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems—0
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification—0
Feature selection for intrusion detection systems—0
Feature Selection for Network Intrusion Detection—0
Feature Selection using the concept of Peafowl Mating in IDS—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—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