Intrusion Detection
101 papers with code • 4 benchmarks • 7 datasets
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.
Benchmarks
These leaderboards are used to track progress in Intrusion Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Intrusion Detection models and implementationsDatasets
UNSW-NB15
20NewsGroups
IoT Network Intrusion Dataset
IoT ENVIRONMENT DATASET
Latest papers with no code
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems
The Internet of Medical Things (IoMT) transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention.
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques
Importantly, in UKM-IDS20, IG successfully identifies all three anomalous instances without prior exposure, demonstrating its generalization capabilities.
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network
The STcAM with fine-pruning uses one-dimensional convolution (Conv1D) to extract spatial features and subsequently utilizes the Bidirectional Long Short Term Memory (Bi-LSTM) to extract the temporal features, where the attention mechanism will focus on the important time steps.
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes.
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron
This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters.
IT Intrusion Detection Using Statistical Learning and Testbed Measurements
We study automated intrusion detection in an IT infrastructure, specifically the problem of identifying the start of an attack, the type of attack, and the sequence of actions an attacker takes, based on continuous measurements from the infrastructure.
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs
This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs.
Utilizing Deep Learning for Enhancing Network Resilience in Finance
In the age of the Internet, people's lives are increasingly dependent on today's network technology.
ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System
Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer.
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol
This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet.
