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 501–550 of 800 papers

TitleStatusHype
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic—0
Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset—0
Hybrid Model For Intrusion Detection Systems—0
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems—0
Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System—0
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation—0
IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset—0
Image Classifiers for Network Intrusions—0
Immune System Approaches to Intrusion Detection - A Review (ICARIS)—0
IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction—0
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems—0
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing—0
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset—0
Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration—0
TINKER: A framework for Open source Cyberthreat Intelligence—0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments—0
Integrating Sensing and Communication in Cellular Networks via NR Sidelink—0
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security—0
Intelligent Green Efficiency for Intrusion Detection—0
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques—0
A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS—0
Intrusion Detection: A Deep Learning Approach—0
Intrusion Detection and Localization for Networked Embedded Control Systems—0
Intrusion Detection at Scale with the Assistance of a Command-line Language Model—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches—0
Intrusion Detection in Internet of Things using Convolutional Neural Networks—0
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model—0
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning—0
Intrusion Detection Systems Using Adaptive Regression Splines—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey—0
Intrusion Detection System with Machine Learning and Multiple Datasets—0
Intrusion Detection using Continuous Time Bayesian Networks—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Intrusion Detection using Sequential Hybrid Model—0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold—0
Investigating Application of Deep Neural Networks in Intrusion Detection System Design—0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization—0
IoT Behavioral Monitoring via Network Traffic Analysis—0
IoT Botnet Detection Using an Economic Deep Learning Model—0
Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments—0
IT Intrusion Detection Using Statistical Learning and Testbed Measurements—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