Distributed Denial of Service (DDoS) attacks pose an increasingly substantial
cybersecurity threat to organizations across the globe. In this paper, we
introduce a new deep learning-based technique for detecting DDoS attacks, a
paramount cybersecurity challenge with evolving complexity and scale.
Specifically, we propose a new dual-space prototypical network that leverages a
unique dual-space loss function to enhance detection accuracy for various
attack patterns through geometric and angular similarity measures. This
approach capitalizes on the strengths of representation learning within the
latent space (a lower-dimensional representation of data that captures complex
patterns for machine learning analysis), improving the model's adaptability and
sensitivity towards varying DDoS attack vectors. Our comprehensive evaluation
spans multiple training environments, including offline training, simulated
online training, and prototypical network scenarios, to validate the model's
robustness under diverse data abundance and scarcity conditions. The Multilayer
Perceptron (MLP) with Attention, trained with our dual-space prototypical
design over a reduced training set, achieves an average accuracy of 94.85% and
an F1-Score of 94.71% across our tests, showcasing its effectiveness in dynamic
and constrained real-world scenarios.

介绍了一种基于深度学习的新技术，用于检测分布式拒绝服务攻击，在各种攻击模式中通过几何和角度相似性度量改善检测准确性。使用离线训练、模拟在线训练和原型网络场景进行了全面评估，表明该模型在不同数据充裕和稀缺条件下具有稳健性。

基于双空间原型网络的重新定义的 DDoS 攻击检测方法

Redefining DDoS Attack Detection Using A Dual-Space Prototypical  Network-Based Approach

As the current detection solutions of distributed denial of service attacks
(DDoS) need additional infrastructures to handle high aggregate data rates,
they are not suitable for sensor networks or the Internet of Things. Besides,
the security architecture of software-defined sensor networks needs to pay
attention to the vulnerabilities of both software-defined networks and sensor
networks. In this paper, we propose a network-aware automated machine learning
(AutoML) framework which detects DDoS attacks in software-defined sensor
networks. Our framework selects an ideal machine learning algorithm to detect
DDoS attacks in network-constrained environments, using metrics such as
variable traffic load, heterogeneous traffic rate, and detection time while
preventing over-fitting. Our contributions are two-fold: (i) we first
investigate the trade-off between the efficiency of ML algorithms and
network/traffic state in the scope of DDoS detection. (ii) we design and
implement a software architecture containing open-source network tools, with
the deployment of multiple ML algorithms. Lastly, we show that under the denial
of service attacks, our framework ensures the traffic packets are still
delivered within the network with additional delays.

提出了一种网络感知的自动化机器学习框架，用于在软件定义的传感器网络中检测分布式拒绝服务攻击（DDoS）。该框架通过选择理想的机器学习算法，根据可变流量负载、异构流量速率和检测时间等指标，能够在网络受限环境中检测到 DDoS 攻击并避免过拟合。

面向软件定义传感器网络的网络感知自动机器学习框架

Network-Aware AutoML Framework for Software-Defined Sensor Networks

Cybersecurity attacks are becoming increasingly sophisticated and pose a
growing threat to individuals, and private and public sectors. Distributed
Denial of Service attacks are one of the most harmful of these threats in
today's internet, disrupting the availability of essential services. This
project presents a novel deep learning-based approach for detecting DDoS
attacks in network traffic using the industry-recognized DDoS evaluation
dataset from the University of New Brunswick, which contains packet captures
from real-time DDoS attacks, creating a broader and more applicable model for
the real world. The algorithm employed in this study exploits the properties of
Convolutional Neural Networks (CNN) and common deep learning algorithms to
build a novel mitigation technique that classifies benign and malicious
traffic. The proposed model preprocesses the data by extracting packet flows
and normalizing them to a fixed length which is fed into a custom architecture
containing layers regulating node dropout, normalization, and a sigmoid
activation function to out a binary classification. This allows for the model
to process the flows effectively and look for the nodes that contribute to DDoS
attacks while dropping the "noise" or the distractors. The results of this
study demonstrate the effectiveness of the proposed algorithm in detecting DDOS
attacks, achieving an accuracy of .9883 on 2000 unseen flows in network
traffic, while being scalable for any network environment.

本研究提出了一种基于深度学习的新型分布式拒绝服务攻击检测方法，利用新不伦瑞克大学 DDoS 评估数据集中的实时 DDoS 攻击数据来构建一个更广泛和更适用于现实世界的模型。该模型通过提取数据流并将其归一化到一个固定长度，然后通过自定义架构进行二进制分类，利用卷积神经网络和常见的深度学习算法实现分类和减轻 DDoS 流量的技术。结果表明，该算法在网络流量中检测 DDoS 攻击方面表现出很高的准确性，针对 2000 个未见过的流量达到了 0.9883 的准确率，并且可在任何网络环境中扩展应用。

一种新的监督深度学习解决方案，用于使用卷积神经网络（CNN）在边缘系统上检测分布式拒绝服务（DDoS）攻击

A Novel Supervised Deep Learning Solution to Detect Distributed Denial  of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks  (CNN)