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 151–175 of 800 papers

TitleStatusHype
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
A Compendium on Network and Host based Intrusion Detection Systems—0
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
A SVM and K-means clustering based fast and efficient intrusion detection system—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
A Transfer Learning Approach for Network Intrusion Detection—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
Attribute Learning for Network Intrusion Detection—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—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