Proceedings ArticleDOI
DeepDefense: Identifying DDoS Attack via Deep Learning
Xiaoyong Yuan,Chuanhuang Li,Xiaolin Li +2 more
- pp 1-8
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TLDR
A recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities and reduces the error rate compared with conventional machine learning method in the larger data set.Abstract:
Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate a better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.read more
Citations
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Journal ArticleDOI
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Hongyu Liu,Bo Lang +1 more
TL;DR: A taxonomy of IDS is proposed that takes data objects as the main dimension to classify and summarize machine learning- based and deep learning-based IDS literature, and believes that this type of taxonomy framework is fit for cyber security researchers.
Proceedings ArticleDOI
Deep Learning Models for Cyber Security in IoT Networks
TL;DR: This paper proposes deep learning models for the cyber security in IoT (Internet of Things) networks and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms.
Journal ArticleDOI
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection
Roberto Doriguzzi-Corin,Stuart Millar,Sandra Scott-Hayward,Jesus Martinez-del-Rincon,Domenico Siracusa +4 more
TL;DR: In this paper, the authors presented a lightweight deep learning DDoS detection system called Lucid, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign.
Journal ArticleDOI
Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications
TL;DR: An LSTM network for distributed cyber-attack detection in fog-to-things communication is proposed and critical attacks and threats targeting IoT devices are identified, especially attacks exploiting vulnerabilities of wireless communications.
Journal ArticleDOI
Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data
TL;DR: A new network intrusion detection model is proposed named the deep hierarchical network, which integrates the improved LeNet-5 and LSTM neural network structures, while learning the spatial and temporal features of flow and an analysis method for traffic features which has an important contribution to abnormal traffic detection.
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