Wireless sensor networks are considered to be among the most significant and
innovative technologies in the 21st century due to their wide range of
industrial applications. Sensor nodes in these networks are susceptible to a
variety of assaults due to their special qualities and method of deployment. In
WSNs, denial of service attacks are common attacks in sensor networks. It is
difficult to design a detection and prevention system that would effectively
reduce the impact of these attacks on WSNs. In order to identify assaults on
WSNs, this study suggests using two machine learning models: decision trees and
XGBoost. The WSNs dataset was the subject of extensive tests to identify denial
of service attacks. The experimental findings demonstrate that the XGBoost
model, when applied to the entire dataset, has a higher true positive rate
(98.3%) than the Decision tree approach (97.3%) and a lower false positive rate
(1.7%) than the Decision tree technique (2.7%). Like this, with selected
dataset assaults, the XGBoost approach has a higher true positive rate (99.01%)
than the Decision tree technique (97.50%) and a lower false positive rate
(0.99%) than the Decision tree technique (2.50%).

无线传感器网络中使用决策树和 XGBoost 两种机器学习模型能够更准确地识别拒绝服务攻击并减少影响，XGBoost 模型具有更高的真正阳性率（98.3%）和更低的假阳性率（1.7%）。

利用机器学习技术在基于雾计算的无线传感器网络中缓解拒绝服务攻击

Mitigating Denial of Service Attacks in Fog-Based Wireless Sensor  Networks Using Machine Learning Techniques

Internet of Things (IoT) and its applications are the most popular research
areas at present. The characteristics of IoT on one side make it easily
applicable to real-life applications, whereas on the other side expose it to
cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks
against IoT. In this paper, we investigate the prospects of using machine
learning classification algorithms for securing IoT against DoS attacks. A
comprehensive study is carried on the classifiers which can advance the
development of anomaly-based intrusion detection systems (IDSs). Performance
assessment of classifiers is done in terms of prominent metrics and validation
methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for
benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze
the significant differences among classifiers statistically. In addition,
Raspberry Pi is used to evaluate the response time of classifiers on IoT
specific hardware. We also discuss a methodology for selecting the best
classifier as per application requirements. The main goals of this study are to
motivate IoT security researchers for developing IDSs using ensemble learning,
and suggesting appropriate methods for statistical assessment of classifier's
performance.

本文研究了使用机器学习分类算法以保证物联网 (IoT) 不受拒绝服务攻击 (DoS) 的前景。评估了流行数据集 CIDDS-001， UNSW-NB15 和 NSL-KDD 用于分类器的基准测试，并使用 Friedman 和 Nemenyi 测试来分析分类器之间的显着差异。最终提出了建议，以应用程序要求选择最佳分类器。

基于机器学习的物联网应用入侵检测系统

Machine Learning Based Intrusion Detection Systems for IoT Applications

The need for secure Internet of Things (IoT) devices is growing as IoT
devices are becoming more integrated into vital networks. Many systems rely on
these devices to remain available and provide reliable service. Denial of
service attacks against IoT devices are a real threat due to the fact these low
power devices are very susceptible to denial-of-service attacks. Machine
learning enabled network intrusion detection systems are effective at
identifying new threats, but they require a large amount of data to work well.
There are many network traffic data sets but very few that focus on IoT network
traffic. Within the IoT network data sets there is a lack of CoAP denial of
service data. We propose a novel data set covering this gap. We develop a new
data set by collecting network traffic from real CoAP denial of service attacks
and compare the data on multiple different machine learning classifiers. We
show that the data set is effective on many classifiers.

为了提高物联网设备的安全性，本研究利用机器学习技术，提出了一种新型数据集，用于收集和分析物联网 CoAP 协议下的拒绝服务攻击网络流量数据，并验证该数据集对多个分类器的有效性。

CoAP-DoS：一种物联网网络入侵数据集

CoAP-DoS: An IoT Network Intrusion Dataset

The detection of Denial of Service (DoS) attacks remains a challenge for the
cloud environment, affecting a massive number of services and applications
hosted by such virtualized infrastructures. Typically, in the literature, the
detection of DoS attacks is performed solely by analyzing the traffic of
packets in the network. This work advocates for the use of telemetry from the
cloud to detect DoS attacks using Machine Learning algorithms. Our hypothesis
is based on richness of such native data collection services, with metrics from
both physical and virtual hosts. Our preliminary results demonstrate that DoS
can be identified accurately with k-Nearest Neighbors (kNN) and decision tree
(CART).

本文提出了利用云环境中的遥测数据与机器学习算法来检测 DoS 攻击的方法，并且通过 k - 最近邻和决策树算法达到了很高的检测准确率。