Transport and Application Layer DDoS Attacks Detection to IoT Devices by Using Machine Learning and Deep Learning Models

Almaraz-Rivera, Josue Genaro; Díaz, Jesús Arturo Pérez; Cantoral-Ceballos, Jose A.

doi:10.3390/s22093367

Cited by 56 publications

(33 citation statements)

References 24 publications

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“…In addition, in the edge server, algorithms can be implemented to predict traffic and cache popular data, which increases the service quality. Today, machine learning has been used in different telecommunication layers, from the physical layer and coding of telecommunication signals [98] to the detection of cyberattacks in the application layer [99]. Designing distributed machine learning algorithms such as federated learning [100], split learning [101] and gossip learning [102] is a hot research topic because it adds new capabilities to the network.…”

Section: A Metaverse Requirements To Be Met By Edge Computingmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

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In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.

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Section: A Metaverse Requirements To Be Met By Edge Computingmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

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“…The DDoS attack is a type of DoS attack whereby multiple systems or machines or devices operate together to carry out an attack on a single target, which makes it difficult to track and turn off the attack machines. DDoS attackers often use a botnet to disrupt network services [27,28]. On 21 October 2016, the biggest DDoS attack was performed using Mirai IoT malware.…”

Section: Overview Of Denial-of-service (Dos) Attackmentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.

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“…Experiments show that this method can identify unknown attacks through reinforcement learning. In 2022, Almaraz-Rivera et al [ 35 ] designed a new intrusion detection system based on machine learning and deep learning models to solve the unbalanced detection of DDoS attack categories. It combines decision trees and multilayer perceptrons to test binary classification performance on different datasets, avoiding data and fragmentation effects.…”

Section: Related Workmentioning

confidence: 99%

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion

Guo¹,

Qiu²,

Liu³

et al. 2022

Computational Intelligence and Neuroscience

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Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and deep learning methods to improve classification performance. However, most classifiers are trained with statistical flow features as input, ignoring topological connection changes. This one-sidedness affects the detection accuracy and cannot provide a basis for the distribution of attack sources for defense deployment. In this study, we propose a topological and flow feature-based deep learning method (GLD-Net), which simultaneously extracts flow and topological features from time-series flow data and exploits graph attention network (GAT) to mine correlations between non-Euclidean features to fuse flow and topological features. The long short-term memory (LSTM) network connected behind GAT obtains the node neighborhood relationship, and the fully connected layer is utilized to achieve feature dimension reduction and traffic type mapping. Experiments on the NSL-KDD2009 and CIC-IDS2017 datasets show that the detection accuracy of the GLD-Net method for two classifications (normal and DDoS flow) and three classifications (normal, fast DDoS flow, and slow DDoS flow) reaches 0.993 and 0.942, respectively. Compared with the existing DDoS attack detection methods, its average improvement is 0.11 and 0.081, respectively. In addition, the correlation coefficient between the detection accuracy of attack flow and the four source distribution indicators ranges from 0.7 to 0.83, which lays a foundation for the inference of attack source distribution. Notably, we are the first to fuse topology and flow features and achieve high-performance DDoS attack intrusion detection through graph-style neural networks. This study has important implications for related research and development of network security systems in other fields.

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Transport and Application Layer DDoS Attacks Detection to IoT Devices by Using Machine Learning and Deep Learning Models

Cited by 56 publications

References 24 publications

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion

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