A Survey of Network-based Intrusion Detection Data Sets
TL;DR: In this article, the authors provide a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet-and flow-based network data in detail, identifying 15 different properties to assess the suitability of individual data sets.
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About: This article is published in Computers & Security. The article was published on 01 Sep 2019. and is currently open access. The article focuses on the topics: Intrusion detection system & Literature survey.
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Citations
A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs
TL;DR: In this article , a Deep Learning (DL) technique based on Long Short Term Memory (LSTM) and Autoencoder was proposed to detect DDoS attacks in SDNs.
Intrusion Detection Using Payload Embeddings
TL;DR: This study proposes a payload-based intrusion detection scheme, PayloadEmbeddings , using byte embeddings of the payloads of network packets, using a shallow neural network to generate vector representations for bytes and their corresponding payloads.
Threat Intelligence with Non-IID Data in Federated Learning enabled Intrusion Detection for SDN: An Experimental Study
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- 06 Dec 2023
TL;DR: The study investigates the effectiveness of Federated Learning (FL) enabled Intrusion Detection Systems (IDS) in Software-Defined Networking (SDN) environments, focusing on Non-IID data and threat-specific feature selection. The results demonstrate significant variations in features importance for Non-IID data compared to traditional centralized data processing approaches.
Machine Learning for Detecting Data Exfiltration: A Review
TL;DR: In this paper, a systematic review of machine learning-based data exfiltration countermeasures is presented to identify and classify ML approaches, feature engineering techniques, evaluation datasets and performance metrics used for these countermeasures.
Crook-sourced intrusion detection as a service
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TL;DR: In this article, a next-generation cyber defense is proposed in which cyber attacks are unconventionally reimagined as free sources of live IDS training data, and adversarial interactions are selectively prolonged to maximize the defender's harvest of useful threat intelligence.
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