Analysis of Machine Learning for Securing Software-Defined Networking

Alamri, Hassan A.; Thayananthan, Vijey

doi:10.1016/j.procs.2021.10.078

Cited by 14 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, different approaches rely on multiple ML algorithms, such as [71]. They used six machine-learning algorithms (NB, SVM, K-NN, extreme gradient boosting (XG-Boost), DT, and RF) to protect an SDN network from DDoS attacks.…”

Section: Hybrid ML Approachesmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

Bahashwan

Anbar

Manickam

et al. 2023

Sensors

View full text Add to dashboard Cite

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

show abstract

Section: Hybrid ML Approachesmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

Bahashwan

Anbar

Manickam

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The article by Alhijawi et al (2022) reviews and classifies the research efforts on SDN and DoS. In Hassan and Thayananthan (2021), the applications of machine learning for securing SDN were discussed.…”

Section: Overview Of Cloud Securitymentioning

confidence: 99%

Integration of the Internet of Things and Cloud

Surianarayanan

Raj

2023

International Journal of Cloud Applications and Computing

View full text Add to dashboard Cite

The integration of IoT and cloud poses increased security challenges. Implementing security mechanisms in IoT systems is challenging due to the availability of limited resources, large number of devices, heterogeneity of devices, generation of bulk data, etc. Likewise, cloud resources are also vulnerable to security issues due to virtualization, insider threats, data loss, data breaches, insecure APIs, etc. Security is of major concern with the integration of IoT and cloud. The primary objective of this review is to highlight the security issues associated with an IoT system and cloud system and with the integration of the two, as well as to highlight solutions in each case. The secondary objective is to describe popular IoT-cloud platforms and also to highlight how such platforms facilitate secure integration. Ultimately a highlight on a shared responsibility model of implementing security is emphasized as both IoT users and cloud service providers have to cooperatively share the responsibility to deploy secure cloud-based IoT applications.

show abstract

“…Machine learning techniques have also been used and detection is done using algorithms. The challenge with using such algorithms is that the algorithms used may require more resources and may produce overhead (6) . Authors of (7) have proposed a hybrid entropy-based intrusion detection mechanism.…”

Section: Introductionmentioning

confidence: 99%

SDN-Based Security for Smart Devices Against Denial of Service Attacks

Jenny¹,

Sugirtham²

2023

IJST

View full text Add to dashboard Cite

Objectives: To achieve a reliable IoT environment by mitigating the DoS attack. Methods: The study focuses on building a firewall on an Open Flow-based Software Defined Networking (SDN) controller to secure a network. We chose the POX controller, which is based on the Python programming language, from among various controllers. Furthermore, a detailed analysis of denial of service attacks against IoT devices has been carried out using Wireshark. Findings: SDN design allows networks to actively monitor traffic and diagnose threats, making network management easier. DoS & DDoS attack is simulated for IoT application which resulted in the loss of packets. Novelty: When compared to the traditional firewall where the internal traffic cannot be seen and filtered, SDN based firewall can filter the packet based on the policy defined. Here the experiment is carried out using open-source software.SDN security was built into the architecture to ensure the availability, integrity, and privacy of all connected resources and information. SDN-based Firewall is used as a solution against the denial of service attack. Through simulation, we have found and analyzed an efficient unsupervised method for detecting DDoS.

show abstract

Analysis of Machine Learning for Securing Software-Defined Networking

Cited by 14 publications

References 19 publications

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

Integration of the Internet of Things and Cloud

SDN-Based Security for Smart Devices Against Denial of Service Attacks

Contact Info

Product

Resources

About