An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks

Zainudin, Ahmad; Ahakonye, Love Allen Chijioke; Akter, Rubina; Kim, Dong‐Seong; Lee, Jae-Min

doi:10.1109/jiot.2022.3196942

Cited by 47 publications

(11 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zainudin et al 78 proposed a deep learning method that combines a CNN and a LSTM network for detecting and classifying DDoS attacks in Software‐Defined Industrial Internet of Things (IIoT) networks. The architecture consists of three main components: data preprocessing, feature extraction, and classification.…”

Section: Ml‐based Ddos Detection Methodsmentioning

confidence: 99%

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

Najafimehr

Zarifzadeh

Mostafavi

2023

Engineering Reports

View full text Add to dashboard Cite

Distributed denial of service (DDoS) attacks represent a significant cybersecurity challenge, posing a critical risk to computer networks. Developing an effective defense mechanism against these attacks is crucial but challenging, given their diverse attack types, network and computing platform heterogeneity, and complex communication protocols. Moreover, the emergence of innovative DDoS attack methods presents a formidable threat to existing countermeasures. Various machine learning techniques have shown promise in detecting DDoS attacks with low false‐positive rates and high detection rates. This survey paper offers a comprehensive taxonomy of machine learning‐based methods for detecting DDoS attacks, reviewing supervised, unsupervised, hybrid approaches, and analyzing the related challenges. Further, we explore relevant datasets, highlighting their strengths and limitations, and propose future research directions to address the current gaps in this domain. This paper aims to provide a profound understanding of DDoS attack detection mechanisms, aiding researchers, and practitioners in developing effective cybersecurity approaches against such attacks. This research is essential because DDoS attacks are diverse and pose a formidable threat to computer networks, and various machine learning techniques have shown promise in detecting them. Its implications include providing insights that can inform the development of robust defense mechanisms against DDoS attacks.

show abstract

Section: Ml‐based Ddos Detection Methodsmentioning

confidence: 99%

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

Najafimehr

Zarifzadeh

Mostafavi

2023

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…Zainudin et al [13] proposed a low-cost approach for DDoS attack classification. This study combined CNN and LSTM and designed extreme gradient boosting.…”

Section: Related Workmentioning

confidence: 99%

An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments

et al. 2023

View full text Add to dashboard Cite

As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers try to fool the classification/prediction mechanism by crafting the input data to create adversarial attacks, which is hard to defend for ML-based Network Intrusion Detection Systems (NIDSs). This paper proposes an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which applies generative adversarial networks (GAN) as well as deep belief networks and long short-term memory (DBN-LSTM) to make the system less sensitive to adversarial attacks and faster feature extraction. We conducted the experiments using the public dataset CICDDoS 2019. The experimental results demonstrated that our method efficiently detected up-to-date common types of DDoS attacks compared to other approaches.

show abstract

“…A. PAPERS USING CICDDoS2019 DATASET Zainudin et al [13] proposed a deep learning approach (CNN, LSTM) for the detection and classification of DDoS attacks VOLUME 11, 2023 in the IIoT environment that utilizes the feature selection technique based on XGBoost. This proposed approach achieved 99.…”

Section: Related Workmentioning

confidence: 99%

Distributed Denial of Service Attack Detection for the Internet of Things Using Hybrid Deep Learning Model

Ahmim,

Maazouzi,

Ahmim

et al. 2023

IEEE Access

View full text Add to dashboard Cite

As a result of the widespread adoption of the Internet of Things, there are now hundreds of millions of connected devices, increasing the likelihood that they may be vulnerable to various types of cyberattacks. In recent years, distributed denial of service (DDoS) has emerged as one of the most destructive tools utilized by attackers. Traditional machine learning approaches are typically ineffective and unable to cope with actual traffic properties when used to identify DDoS attacks. This paper introduces a novel deep learning-based intrusion detection system, specifically designed for deployment at either the Cloud or Fog level in the IoT environment. The proposed model aims to detect all types of DDoS attacks with their specific subcategory. Our hybrid model combines different types of deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Deep Autoencoder, and Deep Neural Networks (DNNs). Our proposed model is made up of two main levels. The first one contains different parallel sub-neural networks trained with specific algorithms. The second level uses the output of the frozen first level combined with the initial data as input. The idea behind the combination of these various types of deep neural networks is to exploit their different properties to achieve very high performance. To evaluate our model, we used the CIC-DDoS2019 dataset, which satisfies all the constraints of an intrusion detection dataset. The results obtained demonstrate that our proposed model outperformed various well-known machine learning and deep learning models in terms of the true positive rate, accuracy, false alarm rate, average accuracy, and average detection rate.

show abstract

An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks

Cited by 47 publications

References 49 publications

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments

Distributed Denial of Service Attack Detection for the Internet of Things Using Hybrid Deep Learning Model

Contact Info

Product

Resources

About