Anomaly Detection and Bottleneck Identification of The Distributed Application in Cloud Data Center using Software–Defined Networking

El-Shamy, Ahmed; El-Fishawy, Nawal; Attiya, Gamal; Mohamed, Mokhtar A. A.

doi:10.1016/j.eij.2021.01.001

Cited by 16 publications

(10 citation statements)

References 57 publications

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“…For example, a forwarding loop is considered the first bug that would stay until it is related to the rule expires. Writing of additional rules (excess restrictions may decrease the network performance [26]. 12_multi, a POX platform component, involves maintaining the mapping of hosts at a switch and port for every control in the network.…”

Section: The Considerable Packets Loss In Sdnmentioning

confidence: 99%

Intelligent Transmission Control for Efficient Operations in SDN

Alkanhel¹,

Ali²,

Jamil³

et al. 2022

Computers, Materials &Amp; Continua

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Although the Software-Defined Network (SDN) is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple issues. Many solutions have been proposed so far for the prevention of errors and mistakes in it but yet, there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN, the errors include packet loss, data compromise etc. The accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state, as these approaches with open flow switches, discover bugs after their occurrence. This article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer (ITCL) layer. It monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple hosts. The layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ITCL. Then, updates flow tables accordingly to accommodate a location-change scenario with a routechange policy. ICTL is organized on prototype-based implementation using the popular POX platform. In this paper, it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss.

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Section: The Considerable Packets Loss In Sdnmentioning

confidence: 99%

Intelligent Transmission Control for Efficient Operations in SDN

Alkanhel¹,

Ali²,

Jamil³

et al. 2022

Computers, Materials &Amp; Continua

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“…As such, anomaly detection is not just a feature of SDN-it is essential for its sustained operation and reliability [4]. The complexity of network interactions in SDN environments, coupled with the sheer volume of data, necessitates the integration of machine learning [5]. Traditional statistical methods are often inadequate in parsing the nuanced patterns that may signal a network anomaly.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security

M Sri Lakshmi

2024

jes

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The research addresses the critical anomaly detection problem in Software-Defined Networking (SDN), a domain where network integrity and security are paramount. Employing the Isolation Forest algorithm, a machine learning model renowned for its efficacy in identifying outliers, the study systematically generates synthetic network traffic data to train and test the model's detection capabilities. The methodology encompasses simulating a range of contamination rates to reflect varying degrees of anomalous activities within the network. Key findings indicate that while the model exhibits potential in anomaly detection, as reflected by the progressive increase in triggered alerts and policy changes, its performance metrics, such as precision, recall, F1-score, and AUC, reveal limitations in its current application. The research contributes to the field by providing a detailed analysis of the Isolation Forest algorithm's performance in an SDN context and laying the groundwork for future enhancements in machine learning-based security measures within these networks.

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“…While cloud has a strong and safe server, there are amounts of external and internal risks that use vulnerabilities in the cloud. This may jeopardize the confidentiality of information, data integrity and information accessibility (25). Untrusted service suppliers always conceal their system's weakness to preserve their reputations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns

Gupta¹,

Khan²,

Gomathy³

et al. 2022

ECS Trans.

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Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also these cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framework in order to provide strong data access control among the multiple users. In this paper, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases, data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering and security framework has better computational efficiency than the conventional approaches on large medical decision patterns.

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Anomaly Detection and Bottleneck Identification of The Distributed Application in Cloud Data Center using Software–Defined Networking

Cited by 16 publications

References 57 publications

Intelligent Transmission Control for Efficient Operations in SDN

Intelligent Transmission Control for Efficient Operations in SDN

Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security

Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns

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