Blockchain Based DDoS Mitigation Using Machine Learning Techniques

Manikumar, D.V.V.S.; Maheswari, B. Uma

doi:10.1109/icirca48905.2020.9183092

Cited by 26 publications

(18 citation statements)

References 11 publications

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“…In the papers [81] [73], the machine learning algorithms such as K-nearest neighbors (KNN), decision tree and random forest as well as deep learning technique long short-term memory (LSTM) are applied to the network traffic to determine the DDoS attack and considered blockchain technology to whitelist/blocklist the IP addresses at the autonomous system level of the network. But, the machine learning application on the network traffic requires infrastructure and computation capabilities, and ownership responsibility to allocate the resources need to be addressed.…”

Section: Rodrigues Et Al [75] [64]mentioning

confidence: 99%

The role of Blockchain in DDoS attacks mitigation: techniques, open challenges and future directions

Chaganti¹,

Bhushan²,

Sowmya³

2022

Preprint

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With the proliferation of new technologies such as Internet of Things (IOT) and Software-Defined Networking (SDN) in the recent years, the distributed denial of service (DDoS) attack vector has broadened and opened new opportunities for more sophisticated DDoS attacks on the targeted victims. The new attack vector includes unsecured and vulnerable IoT devices connected to the internet, denial of service vulnerabilities like southbound channel saturation in the SDN architecture. Given the high-volume and pervasive nature of these attacks, it is beneficial for stakeholders to collaborate in detecting and mitigating the denial of service attacks in a timely manner. The blockchain technology is considered to improve the security aspects owing to the decentralized design, secured distributed storage and privacy. A thorough exploration and classification of blockchain techniques used for DDoS attack mitigation is not explored in the prior art. This paper reviews and categorizes the existed state-of-the-art DDoS mitigation solutions based on blockchain technology. The DDoS mitigation techniques are classified based on the solution deployment location i.e. network based, near attacker location, near victim location and hybrid solutions in the network architecture with emphasis on the IoT and SDN architectures. Additionally, based on our study, the research challenges and future directions to implement the blockchain based DDoS mitigation solutions are discussed. We believe that this paper could serve as a starting point and reference resource for future researchers working on denial of service attacks detection and mitigation using blockchain technology.

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Section: Rodrigues Et Al [75] [64]mentioning

confidence: 99%

The role of Blockchain in DDoS attacks mitigation: techniques, open challenges and future directions

Chaganti¹,

Bhushan²,

Sowmya³

2022

Preprint

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“…The metrics presented in this work are comparable with those demonstrated in the previous studies that used ML/DL models to detect attacks and that used the CICDDoS2019 dataset. The authors in [43] applied KNN, Decision Tree (DT), and RF and achieved accuracy levels less than 95.19%. The combined LSTM-Fuzzy method presented in [21] achieved an accuracy of 99.74% and FPR of 0.25%.…”

Section: ) Comparison With Previous Workmentioning

confidence: 99%

SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

2021

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Distributed Denial of Service (DDoS) attacks represent the most common and critical attacks targeting conventional and new generation networks, such as the Internet of Things (IoT), cloud computing, and fifth-generation (5G) communication networks. In recent years, DDoS attacks have become not only massive but also sophisticated. Software-Defined Networking (SDN) technology has demonstrated effectiveness in counter-measuring complex attacks since it provides flexibility on global network monitoring and inline network configuration. Although several works have been proposed to detect DDoS attacks, in most of them the authors did not use up-to-date datasets that contain the newest threats. Furthermore, only a few previous works assessed their solutions using simulated scenarios, easing the migration to production networks. This document presents the implementation of a modular and flexible SDN-based architecture to detect transport and application layer DDoS attacks using multiple Machine Learning (ML) and Deep Learning (DL) models. Exploring diverse ML/DL methods allowed us to resolve which methods perform better under different attack types and conditions. We tested the ML/DL models using two up-to-date security datasets, and they showed accuracy above 99% on classifying unseen traffic (testing set). We also deployed a simulated environment using the network emulator Mininet and the Open Network Operating System (ONOS) SDN controller. In this experimental setup, we demonstrated high detection rates, above 98% for transport DDoS attacks and up to 95% for application-layer DDoS attacks.INDEX TERMS Software Defined Networking, deep learning, machine learning, DDoS attack, transport layer, application layer, slow-rate attacks.

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“…The latest state-of-the-art research has been conducted in the field of cyber-security mitigating DDoS attacks [11][12][13][14][15][16][17][18][19]. Table 1 summarizes research on different approaches to detecting, defending, and mitigating DDOS attacks.…”

Section: Related Workmentioning

confidence: 99%

The Vulnerability of the Production Line Using Industrial IoT Systems under DDoS Attack

et al. 2021

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Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

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Blockchain Based DDoS Mitigation Using Machine Learning Techniques

Cited by 26 publications

References 11 publications

The role of Blockchain in DDoS attacks mitigation: techniques, open challenges and future directions

The role of Blockchain in DDoS attacks mitigation: techniques, open challenges and future directions

SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

The Vulnerability of the Production Line Using Industrial IoT Systems under DDoS Attack

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