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 651–700 of 800 papers

TitleStatusHype
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed—0
Payload-Aware Intrusion Detection with CMAE and Large Language Models—0
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments—0
Pelican: A Deep Residual Network for Network Intrusion Detection—0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection—0
Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System—0
Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network—0
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection—0
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?—0
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning—0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis—0
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation—0
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security—0
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward—0
Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS—0
Precise Feature Selection and Case Study of Intrusion Detection in an Industrial Control System (ICS) Environment—0
Predicting Network Attacks Using Ontology-Driven Inference—0
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks—0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks—0
Rallying Adversarial Techniques against Deep Learning for Network Security—0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
Real-time Network Intrusion Detection via Decision Transformers—0
Real-time Regular Expression Matching—0
Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs—0
Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach—0
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection—0
REGARD: Rules of EngaGement for Automated cybeR Defense to aid in Intrusion Response—0
Reinforcement Learning for Feedback-Enabled Cyber Resilience—0
Relevant Feature Selection Model Using Data Mining for Intrusion Detection System—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems—0
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture—0
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning—0
Road Context-aware Intrusion Detection System for Autonomous Cars—0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017—0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier—0
Robust Intrusion Detection System with Explainable Artificial Intelligence—0
Robustness of ML-Enhanced IDS to Stealthy Adversaries—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