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Network Intrusion Detection

9 papers with code · Miscellaneous
Subtask of Intrusion Detection

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Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

25 Feb 2018ymirsky/KitNET-py

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.

NETWORK INTRUSION DETECTION

Hybrid Isolation Forest - Application to Intrusion Detection

10 May 2017pfmarteau/HIF

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.

ANOMALY DETECTION NETWORK INTRUSION DETECTION

Deep Anomaly Detection with Deviation Networks

19 Nov 2019GuansongPang/deviation-network

Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.

ANOMALY DETECTION CYBER ATTACK DETECTION FRAUD DETECTION NETWORK INTRUSION DETECTION REPRESENTATION LEARNING

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

10 Feb 2020haoyfan/AnomalyDAE

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.

ANOMALY DETECTION NETWORK INTRUSION DETECTION REPRESENTATION LEARNING

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

8 Feb 2018lmunoz-gonzalez/Poisoning-Attacks-with-Back-gradient-Optimization

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.

ANOMALY DETECTION DATA POISONING NETWORK INTRUSION DETECTION OUTLIER DETECTION

LuNet: A Deep Neural Network for Network Intrusion Detection

22 Sep 2019mhwong2007/LuNet

Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.

NETWORK INTRUSION DETECTION

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

13 Jun 2018GuansongPang/deep-outlier-detection

However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).

ANOMALY DETECTION DISEASE PREDICTION NETWORK INTRUSION DETECTION OUTLIER DETECTION UNSUPERVISED REPRESENTATION LEARNING