Intrusion Detection
100 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
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Libraries
Use these libraries to find Intrusion Detection models and implementationsDatasets
UNSW-NB15
20NewsGroups
IoT Network Intrusion Dataset
IoT ENVIRONMENT DATASET
Most implemented papers
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
TOD: GPU-accelerated Outlier Detection via Tensor Operations
yzhao062/pytod
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Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
In this paper we present an approach for modelling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance.
Hybrid Isolation Forest - Application to Intrusion Detection
From the identification of a drawback in the Isolation Forest (IF) algorithm that limits its use in the scope of anomaly detection, we propose two extensions that allow to firstly overcome the previously mention limitation and secondly to provide it with some supervised learning capability.
A Renewal Model of Intrusion
We present a probabilistic model of an intrusion in a renewal process.
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
A dataset for malware detection in Grid computing is described.
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack.
Intrusion Detection Using Mouse Dynamics
Drag and drop mouse actions proved to be the best actions for impostor detection.
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks.
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
For reproducibility of the method, our synthetic data is publicly available.
