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 151–200 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
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
BEBP: An Poisoning Method Against Machine Learning Based IDSs—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Blockchain Large Language Models—0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
CoAP-DoS: An IoT Network Intrusion Dataset—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection—0
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
A survey on deep packet inspection for intrusion detection systems—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—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