SDMTA: Attack Detection and Mitigation Mechanism for DDoS Vulnerabilities in Hybrid Cloud Environment

Kautish, Sandeep; Reyana, A.; Vidyarthi, Ankit

doi:10.1109/tii.2022.3146290

Cited by 39 publications

(11 citation statements)

References 28 publications

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“…Simulation result shows 98% accuracy rate and has a great performance over traditional methods [27]. In 2022, Sandeep Kautish propose DDOS strategy for cloud computing environment when we compare it with existing methods, it's accuracy is 96% [28]. In 2022, Qiuhua Wang focus deep learning based approach for cyber attack detection.…”

Section: Literature Reviewmentioning

confidence: 99%

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Agarwal¹,

Sharma²

2022

IJCDS

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Various types of security attacks are normal in vehicular communication networks. The current study uses a support vector machine to implement a Point to Point Critical Path Monitoring (P2PCPM) based Denial of Service (DOS) Attack detection technique for Vehicular Communication Network (VCN) resource management. Greatest quality of P2PCPM is that it eliminates attacked nodes from the network for the smooth process of vehicular communication. This scheme works well in terms of accuracy as well as attack detection rate. The whole simulation is made and tried by utilizing MATLAB Software. Simulation result shows 99% accuracy in case of security attack detection as well as reduced training and testing error upto 2%. Experimental results indicate that this scheme has a great efficiency and works well up to 1000 nodes, which is the limitation of current implementation. In future, simulation test may be done for unlimited nodes using similar or other techniques of attack detection.

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Section: Literature Reviewmentioning

confidence: 99%

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Agarwal¹,

Sharma²

2022

IJCDS

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“…The term K g and P g in Equation ( 15) represents the insignificant gate weights. The gate masses are displayed in Equations ( 16) and (17).…”

Section: F I G U R Ementioning

confidence: 99%

“…In 2022, Kautish and Vidyarthi 17 . have proposed a unique mitigation technique for DDoS attacks using the Hybrid Cloud Setting with the aid of the “Dispersed Denial‐Of‐Mitigation Tree Architecture (SDMTA).” The suggested SDMTA mitigating design incorporated continuous network surveillance to sustain prediction approaches.…”

Section: Introductionmentioning

confidence: 99%

Detection of distributed denial of service attack using enhanced adaptive deep dilated ensemble with hybrid meta‐heuristic approach

Aliar,

Gowri,

Abins

2024

Trans Emerging Tel Tech

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The biggest firms throughout the world now are the ones that offer services in the cloud. One of the top problems for cloud users (CUs) and cloud service providers (CSPs) is the availability of cloud‐based services whenever needed. A distributed denial of service (DDoS) assault has been a significant threat to the security of the system in recent years. DDoS defense and detection of these attacks have become hot topics of research for academia and business. However, most approaches cannot accomplish efficient detection outcomes with few false alarms. As a result, minimizing the consequences of DDoS attacks allows CSPs to offer CUs high‐quality services. In the cloud sector, a collective deep structured algorithm is suggested to identify DDoS attacks successfully. The recommended method contains several stages: data acquisition, pre‐processing, optimal feature detection, and selection. The first step is the acquisition of data using the help of publicly available sources. Further, the input data undergoes preprocessing. Consequently, the optimal weighted feature selection takes place on the preprocessed data, where the optimization is done with the aid of the Hybrid Border Collie and Dragonfly Algorithm (HBCDA). Finally, DDoS attack detection is achieved via a novel method known as adaptive deep dilated ensemble (ADDE), which includes, one‐dimensional convolutional neural network (1DCNN), deep temporal convolutional neural network (DTCNN), recurrent neural network (RNN), and bidirectional long short‐term memory (Bi‐LSTM). For the attainment of optimal results, the parameter tuning is accomplished by using the HBCDA approach. The detection outcome is computed by using the fuzzy ranking mechanism. The validation is done for the suggested method model, and its corresponding findings are validated with conventional techniques. Hence, the suggested approach outperforms the detection performance and ensures more efficiency than traditional approaches.

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“…• Mitigation tree architecture: Kautish et al 63 proposed scattered denial-of-service mitigation tree architecture (SDMTA) mitigation architecture that integrates network monitoring for efficient DDoS detection mechanisms. With businesses operating globally, network devices like routers, switches, firewalls, VMs, and storage devices must be connected and monitored.…”

Section: • Duplication Of Os and Isolation Of Applicationmentioning

confidence: 99%

“…It is assumed that legitimate users and the service provider are observing the network for so long and gaining probabilistic information about another user being malicious or not. Therefore, this probabilistic information is used by the provider and legitimate users to change their actions to deal with malicious users present in the network. Mitigation tree architecture : Kautish et al 63 proposed scattered denial‐of‐service mitigation tree architecture (SDMTA) mitigation architecture that integrates network monitoring for efficient DDoS detection mechanisms. With businesses operating globally, network devices like routers, switches, firewalls, VMs, and storage devices must be connected and monitored.…”

Section: Defense Mechanism For Direct Attackmentioning

confidence: 99%

Uncovering collateral damages and advanced defense strategies in cloud environments against DDoS attacks: A comprehensive review

Verma,

Bharot,

Breslin

et al. 2024

Trans Emerging Tel Tech

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Cloud computing (CC) offers on‐demand computing and resources to users, and organizations, and is also used in many human‐centric intelligent systems. Attacks in cloud networks cause huge damage to service providers and users. Distributed Denial of Service (DDoS) is one of them that greatly impacts the cloud network. The unavailability of resources is the major concern pertaining to cloud service and resources. Apart from the direct targets of a DDoS attack, there are also indirect effects on non‐targets within a cloud network. These effects on the non‐target stakeholders of the cloud are called collateral damages. However, this area of research is not explored much by researchers. Thus, the defense methods pertaining to direct and indirect effects need to be explored. This article aims to describe the consequences of DDoS attacks and the solutions available in the cloud network. The novelty of this article lies in shedding light on the indirect impacts of DDoS in cloud networks and possible solution approaches. The article also provides a comparative analysis of the existing defense solution under different categories and available tools and datasets to evaluate the proposed solutions. The article also mentions the solution considerations and effective solution scenarios. The idea behind this article is to impart guidelines to the researchers, for developing efficient defense solutions against direct target and indirect target DDoS attacks. Moreover, the research shortcomings, pros–cons, and existing challenges are outlined, and directions for future research are mentioned.

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SDMTA: Attack Detection and Mitigation Mechanism for DDoS Vulnerabilities in Hybrid Cloud Environment

Cited by 39 publications

References 28 publications

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Detection of distributed denial of service attack using enhanced adaptive deep dilated ensemble with hybrid meta‐heuristic approach

Uncovering collateral damages and advanced defense strategies in cloud environments against DDoS attacks: A comprehensive review

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