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
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS—0
Federated Deep Learning for Intrusion Detection in IoT Networks—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
REGARD: Rules of EngaGement for Automated cybeR Defense to aid in Intrusion Response—0
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation—0
Anomaly Detection Dataset for Industrial Control Systems—0
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
Blockchain Large Language Models—0
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review—0
Training Automated Defense Strategies Using Graph-based Cyber Attack Simulations—0
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection—0
Explainable Intrusion Detection Systems Using Competitive Learning Techniques—0
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—0
Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems—0
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
EdgeServe: A Streaming System for Decentralized Model Serving—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Anomaly based network intrusion detection for IoT attacks using deep learning technique—0
IoT Botnet Detection Using an Economic Deep Learning Model—0
Show:102550
← PrevPage 15 of 32Next →
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