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 301–350 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
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion DetectionCode1
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour—0
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
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
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial NetworksCode1
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
EdgeServe: A Streaming System for Decentralized Model Serving—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—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
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
IoT Botnet Detection Using an Economic Deep Learning Model—0
Behavioural Reports of Multi-Stage MalwareCode0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Leveraging Planning Landmarks for Hybrid Online Goal Recognition—0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification—0
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection—0
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods—0
Ensemble learning techniques for intrusion detection system in the context of cybersecurity—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial NetworksCode0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs—0
Separating Flows in Encrypted Tunnel TrafficCode0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Network Security Modelling with Distributional Data—0
Intrusion Detection in Internet of Things using Convolutional Neural Networks—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
GowFed -- A novel Federated Network 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