Smart and Lightweight DDoS Detection Using NFV

Alharbi, Talal; Aljuhani, Ahamed; Liu, Huan; Hu, Chunqiang

doi:10.1145/3093241.3093253

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Attackers have the ability to conduct different types of DDoS attacks and examine a defense system with each type of attack to perform a successful cyberattack. Today, many systems make use of modern technologies due to the advantages this environment offers, including flexibility, scalability, and cost reduction [58], [59]. However, modern networking environments remain vulnerable to a diverse range of DDoS attacks.…”

Section: Related Workmentioning

confidence: 99%

“…A defense system designer should implement an automated mitigation model that can identify and block attacks without the need for involvement on the part of the administration. A modern technology such as NFV is able to provide the automation feature [113], which should help developers to implement an automated online defense system. A detailed report concerning the attack details is automatically generated so that the administrator can make necessary changes or update the system's security measures to be more robust in the face of attacks.…”

Section: Automationmentioning

confidence: 99%

“…number of different approaches have been proposed with regard to DDoS attack detection based on ML techniques in modern networking environments. Today, many systems make use of modern networking technologies due to the advantages that such an environment offers, including flexibility, scalability, and cost reduction[58] [59]. This section discusses DDoS defense systems based on ML techniques using a diverse range of modern networking environments.…”

mentioning

confidence: 99%

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Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Section: Automationmentioning

confidence: 99%

mentioning

confidence: 99%

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Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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“…The framework achieves high scalability in a large IoT network environment. 133 Alharbi et al 139 proposed a mechanism that conducts flow screening to classify the nature of the traffic and define the process required to be implemented and executed automatically by the NFVs to the delivery of security functions. The mechanism does not show enough evidence to demonstrate the efficiency against DDoS attacks despite the scalability of the mechanism.…”

Section: Heterogeneous Sdn-and Nfv-based Solutionsmentioning

confidence: 99%

A comprehensive survey on secure software‐defined network for the Internet of Things

Mohamed

Alofe

Azad

et al. 2021

Trans Emerging Tel Tech

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The Internet of Things (IoT) is the network of smart devices, sensors, and machines that continuously monitored the surrounding environment and execute meaningful decisions on the data or information it receives. The Internet-enabled devices could facilitate computer-mediated strategies for various tasks, for example, smart health care, managing the cities or smart factories, smart manufacturing, automating the home and business, etc. IoT commonly uses Internet technology for establishing communication among devices, thus inherits all the security threats that are currently affecting Internet users along with other security threats that are specific to IoTs due to resources constrained nature of the smart devices and sensors. The greater footprint, the distributed nature of the network and the existence of a huge number of IoT devices has also attracted criminals, fraudsters, and attackers to utilize this medium for spreading malicious content or making devices unavailable for legitimate use.It is imperative to ensure that the Confidentiality, Integrity, Security, and Privacy of information and users should remain intact while using these devices and thus they require an effective security system. Software-defined Networks (SDN) and Network Function are the way to control and configure devices from a centralized location and have been proven to offer scalability and versatility to their deployed ecosystems. In this paper, we systematically review the adoption of SDN and Network Function Virtualization (NFV) for securing the IoT network from emerging threats. To this extent, we provide a comprehensive survey on security solutions based on SDN, Blockchain, NFV, and SDN/NFV proposed for the security of the IoT network. We have also identified open challenges in this domain which includes lack of standardization, low cost, and effective machine learning systems for identifying malicious traffic and handling great attack surface and different attack vectors. The deployed technologies exhibit positive strides in their usage for the provision of security in IoT environments offering security enhancements, scalability, and versatility.

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Future IoT‐enabled threats and vulnerabilities: State of the art, challenges, and future prospects

Srivastava

Gupta

Quamara

et al. 2020

Int J Communication

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SummaryIn the recent era, the security issues affecting the future Internet‐of‐Things (IoT) standards has fascinated noteworthy consideration from numerous research communities. In this view, numerous assessments in the form of surveys were proposed highlighting several future IoT‐centric subjects together with threat modeling, intrusion detection systems (IDS), and various emergent technologies. In contrast, in this article, we have focused exclusively on the emerging IoT‐related vulnerabilities. This article is a multi‐fold survey that emphasizes on understanding the crucial causes of novel vulnerabilities in IoT paradigms and issues in existing research. Initially, we have emphasized on different layers of IoT architecture and highlight various emerging security challenges associated with each layer along with the key issues of different IoT systems. Secondly, we discuss the exploitation, detection, and defense methodologies of IoT malware‐enabled distributed denial of service (DDoS), Sybil, and collusion attack capabilities. We have also discussed numerous state‐of‐the‐art strategies for intrusion detection and methods for IDS setup in future IoT systems. Third, we have presented a brief classification of existing IoT authentication protocols and a comparative analysis of such protocols based on different IoT‐enabled cyber attacks. For conducting a real‐time future IoT research, we have presented some emerging blockchain solutions. We have also discussed a comparative examination of some of the recently developed simulation tools and IoT test beds that are characterized based on different layers of IoT infrastructure. We have also outlined some of the open issues and future research directions and also facilitate the readers with broad classification of existing surveys in this domain that addresses several scopes related to the IoT paradigm. This survey article focuses in enabling IoT‐related research activities by comparing and merging scattered surveys in this domain.

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Smart and Lightweight DDoS Detection Using NFV

Cited by 12 publications

References 8 publications

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

A comprehensive survey on secure software‐defined network for the Internet of Things

Future IoT‐enabled threats and vulnerabilities: State of the art, challenges, and future prospects

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