Leveraging Machine Learning Approach to Setup Software-Defined Network(SDN) Controller Rules During DDoS Attack

Sen, Sajib; Gupta, Kishor Datta; Ahsan, Md Manjurul

doi:10.1007/978-981-13-7564-4_5

Cited by 33 publications

(19 citation statements)

References 10 publications

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“…Conti et al [18] Image processing IA Immune Ink-on-paper fingerprints identification Sen et al [152] Power distribution VR GA Optimize dispatching for micro-grid energy Yeom et al [153], Kitchens et al [154] Face& Keystroke analysis IA NN Person authentication using NN Ku [155] Keystroke analysis IA NN Password authentication for multiserver architecture Gai et al [40] Adaptive access ACS GA Allocate multimedia data in diverse memory Ye et al [41] Adaptive access ACS GA QoS-aware service Hartman et al [42] Scalable storage ACS Immune Cost-effective storage systems Lin et al [46] Trust based ACS Immune A mutual trust based ACS Tzeng [50] Hierarchical key ACS Immune Time-bound cryptographic scheme Sen et al [156] DDoS attack detection PNS NN Identify incoming DDoS type traffic attack…”

Section: Discussionmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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Section: Discussionmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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“…The encoding algorithms [17] that are based on the correlation between high cardinal categorical attributes and target or class variables provides a nice solution to this problem. The supervised ratio algorithm computes the numerical value for a certain category that is the ratio between the total number of rows that category present in the positive class and total data points.…”

Section: Methodsmentioning

confidence: 99%

Map Reduce Implementation for Malicious Websites Classification

Islam¹,

Poudyal²,

Gupta³

2019

IJNSA

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Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websitessome of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities. In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using Map Reduce to make the classification speeds faster than the traditional approaches.[2].

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“…Classification: The algorithm learns how to classify new observations from a labeled training dataset [44]. The output of the classification can be either bi-class (e.g., whether a given client is a regular user or an attacker, or whether an email is spam or non-spam) or multi-class, in which case it contains more than two classes.…”

Section: A Supervised Learningmentioning

confidence: 99%

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

Aljuhani

2021

IEEE Access

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Leveraging Machine Learning Approach to Setup Software-Defined Network(SDN) Controller Rules During DDoS Attack

Cited by 33 publications

References 10 publications

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Map Reduce Implementation for Malicious Websites Classification

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

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