About
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
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Greatest papers with code
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection
In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOMDAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection.
SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection
How to accelerate the training and predicting with a large number of heterogeneous unsupervised OD models?
DIMENSIONALITY REDUCTION FRAUD DETECTION INTRUSION DETECTION OUTLIER ENSEMBLES
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
• AFAgarap/cnn-svm
• 
Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss.
Ranked #1 on
Intrusion Detection
on 20NEWS
(using extra training data)
IMAGE CLASSIFICATION INTRUSION DETECTION SPEECH RECOGNITION TEXT CLASSIFICATION
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS).
Ranked #1 on
Network Intrusion Detection
on KDD
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats.
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?
Ranked #1 on
Intrusion Detection
on CIC-DDoS
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems
• antonpuz/DeROL
•
Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields.
Cyber Attack Detection thanks to Machine Learning Algorithms
• antoinedelplace/Cyberattack-Detection
•
The Random Forest Classifier succeeds in detecting more than 95% of the botnets in 8 out of 13 scenarios and more than 55% in the most difficult datasets.
CYBER ATTACK DETECTION FEATURE SELECTION INTRUSION DETECTION
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.