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 251–300 of 800 papers

TitleStatusHype
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Adversarial Examples in Constrained Domains—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
BEBP: An Poisoning Method Against Machine Learning Based IDSs—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
A concise method for feature selection via normalized frequencies—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
An AutoML-based approach for Network Intrusion Detection—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Attribute Learning for Network Intrusion Detection—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
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
A Transfer Learning Approach for Network Intrusion Detection—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—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
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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