This paper explores the critical issue of enhancing cybersecurity measures
for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed
Denial of Service (DDoS) attacks. In the current work, we have explored three
variants of DDoS attacks, namely Transmission Control Protocol (TCP), Internet
Control Message Protocol (ICMP), and TCP + ICMP flooding attacks, and developed
a detection mechanism that runs on the companion computer of the UAV system. As
a part of the detection mechanism, we have evaluated various machine learning,
and deep learning algorithms, such as XGBoost, Isolation Forest, Long
Short-Term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), LSTM with attention,
Bi-LSTM with attention, and Time Series Transformer (TST) in terms of various
classification metrics. Our evaluation reveals that algorithms with attention
mechanisms outperform their counterparts in general, and TST stands out as the
most efficient model with a run time of 0.1 seconds. TST has demonstrated an F1
score of 0.999, 0.997, and 0.943 for TCP, ICMP, and TCP + ICMP flooding attacks
respectively. In this work, we present the necessary steps required to build an
on-board DDoS detection mechanism. Further, we also present the ablation study
to identify the best TST hyperparameters for DDoS detection, and we have also
underscored the advantage of adapting learnable positional embeddings in TST
for DDoS detection with an improvement in F1 score from 0.94 to 0.99.

探索低成本、基于 Wi-Fi 的无人机系统对抗分布式拒绝服务攻击的关键问题，研究了三种 DDoS 攻击变种，利用机器学习和时间序列转换等算法开发了伴随计算机上运行的检测机制，发现时间序列转换是最高效的模型，具有 0.1 秒的运行时间和在不同分类指标下的优越性能。同时，提出了构建无人机 DDoS 检测机制的必要步骤，并改进了时间序列转换的学习位置嵌入以提高 DDoS 检测性能。

注意力与无人机的结合：低成本无人机中 DDoS 检测的全面评估

Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in  Low-Cost UAVs

The rapid proliferation of the Internet of Things (IoT) has ushered in
transformative connectivity between physical devices and the digital realm.
Nonetheless, the escalating threat of Distributed Denial of Service (DDoS)
attacks jeopardizes the integrity and reliability of IoT networks. Conventional
DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT
ecosystems, potentially compromising data privacy. This paper introduces an
innovative strategy to bolster the security of IoT networks against DDoS
attacks by harnessing the power of Federated Learning that allows multiple IoT
devices or edge nodes to collaboratively build a global model while preserving
data privacy and minimizing communication overhead. The research aims to
investigate Federated Learning's effectiveness in detecting and mitigating DDoS
attacks in IoT. Our proposed framework leverages IoT devices' collective
intelligence for real-time attack detection without compromising sensitive
data. This study proposes innovative deep autoencoder approaches for data
dimensionality reduction, retraining, and partial selection to enhance the
performance and stability of the proposed model. Additionally, two renowned
aggregation algorithms, FedAvg and FedAvgM, are employed in this research.
Various metrics, including true positive rate, false positive rate, and
F1-score, are employed to evaluate the model. The dataset utilized in this
research, N-BaIoT, exhibits non-IID data distribution, where data categories
are distributed quite differently. The negative impact of these distribution
disparities is managed by employing retraining and partial selection
techniques, enhancing the final model's stability. Furthermore, evaluation
results demonstrate that the FedAvgM aggregation algorithm outperforms FedAvg,
indicating that in non-IID datasets, FedAvgM provides better stability and
performance.

通过利用联邦学习技术，本研究提出了一种创新的策略来加强物联网网络对分布式拒绝服务攻击的安全性，实时检测并减轻攻击的影响，同时保护敏感数据的隐私。研究结果表明，在非独立非同分布数据集中，FedAvgM 的聚合算法优于 FedAvg，提供更好的稳定性和性能。

通过联邦学习增强物联网安全对抗 DDoS 攻击

Enhancing IoT Security Against DDoS Attacks through Federated Learning

Distributed Denial of Service (DDoS) attacks pose a significant threat to the
stability and reliability of online systems. Effective and early detection of
such attacks is pivotal for safeguarding the integrity of networks. In this
work, we introduce an enhanced approach for DDoS attack detection by leveraging
the capabilities of Deep Residual Neural Networks (ResNets) coupled with
synthetic oversampling techniques. Because of the inherent class imbalance in
many cyber-security datasets, conventional methods often struggle with false
negatives, misclassifying subtle DDoS patterns as benign. By applying the
Synthetic Minority Over-sampling Technique (SMOTE) to the CICIDS dataset, we
balance the representation of benign and malicious data points, enabling the
model to better discern intricate patterns indicative of an attack. Our deep
residual network, tailored for this specific task, further refines the
detection process. Experimental results on a real-world dataset demonstrate
that our approach achieves an accuracy of 99.98%, significantly outperforming
traditional methods. This work underscores the potential of combining advanced
data augmentation techniques with deep learning models to bolster
cyber-security defenses.

通过利用深度残差神经网络（ResNets）和合成过采样技术，我们引入了一种增强的分布式拒绝服务（DDoS）攻击检测方法，用于有效和早期检测此类攻击，并平衡良性和恶意数据点的表示以更好地辨别攻击特征，实验证明我们的方法在真实数据集上达到了 99.98% 的准确度，显著优于传统方法，突显了将先进的数据增强技术与深度学习模型相结合以增强网络安全防御的潜力。