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 201–250 of 800 papers

TitleStatusHype
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems—0
CoAP-DoS: An IoT Network Intrusion Dataset—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Conformalized density- and distance-based anomaly detection in time-series data—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks—0
Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries—0
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation—0
Anomaly Detection Dataset for Industrial Control Systems—0
Convolutional Neural Network for Intrusion Detection System In Cyber Physical Systems—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—0
A survey on deep packet inspection for intrusion detection systems—0
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss—0
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
Data Analysis of Wireless Networks Using Classification Techniques—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Data Mining model in the discovery of trends and patterns of intruder attacks on the data network as a public-sector innovation—0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN—0
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure—0
Deep Learning-based Intrusion Detection Systems: A Survey—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—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