Intrusion Detection

51 papers with code • 4 benchmarks • 2 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.

Source: Machine Learning Techniques for Intrusion Detection

Most implemented papers

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

AFAgarap/cnn-svm 10 Sep 2017

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.

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

ymirsky/KitNET-py 25 Feb 2018

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.

eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys

MJafarMashhadi/Haplophysh 27 Feb 2017

For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack.

A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

AbertayMachineLearningGroup/network-threats-taxonomy 9 Jun 2018

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.

Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

antonpuz/DeROL 4 Aug 2018

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 17 Jan 2020

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.

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

haoyfan/AnomalyDAE 10 Feb 2020

In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings.

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

ISorokos/SafeML 27 May 2020

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems.

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

waimorris/E-GraphSAGE 30 Mar 2021

This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).