An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments

Hezavehi, Sasha Mahdavi; Rahmani, Rouhollah

doi:10.1007/s10586-019-03031-y

Cited by 30 publications

(3 citation statements)

References 31 publications

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“…The evaluated density functions for the group j at location F set . Further, the estimated density values are then allowed to the neurons in the summation layer, which combines the data from the n training cases with prior probabilities given in Equation (23).…”

Section: Neural Networkmentioning

confidence: 99%

“…In 2020, Mahdavi et al 23 examined an anomaly‐based DDoS assault detection framework in a cloud environment using a TPA. Second, they introduced simulation tests to validate the proposed work by providing some configurations and assumptions of cloud environments.…”

Section: Related Workmentioning

confidence: 99%

“…However, detecting these attacks with high accuracy is a major issue. ELM 23,24 is a part of ANN and has a unique hidden level, which is referred to as SLFN. Further, the weights of the links between input and hidden layers are termed input‐hidden link weights, together with hidden biases are initialized with random values 25 .…”

Section: Introductionmentioning

confidence: 99%

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DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

Reddy,

Meera

2024

Adaptive Control & Signal

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SummaryDDoS attacks are a type of cloud incursion that lessen service degradation. DDoS attacks target the cloud network with invalid requests, rejecting legitimate requests. Such attacks disrupt the entire cloud architecture, thus it needs efficient detection methods to spot their presence. This study proposes a novel ensemble classification model for DDoS incursion detection. Pre‐processing, feature extraction, and attack detection are the three main components of the suggested intrusion detection system. Improved data imbalance processing is processed during pre‐processing. Features including HOS‐based features enhanced entropy‐based features, correlation features, and raw features are extracted from the pre‐processed data. The generated features are trained using an ensemble model, which integrates classifiers like SVM, RF, NN, LSTM, and DRN, during the attack detection phase. A new hybrid approach known as TUDMA optimally trains the model by setting the ideal weight because DRN provides the final detected outcome. By reducing errors, this ideal training will guarantee an improvement in detection results. The suggested hybrid optimization combines the two techniques, TDO and DMO. The accuracy of the methods FS‐WOA‐DNN, RBF‐PSO, SMA, BRO, SLO, and NMRA was 86.73%, 85.69%, 84.58%, 87.43%, 88.91%, and 89.78%, respectively, while the accuracy of the TUDMA was 95.34% in the 80th learning percentage. Finally, for many measurements, the suggested efficiency is superior to the traditional methods.

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Section: Neural Networkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

Reddy,

Meera

2024

Adaptive Control & Signal

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Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

Rubai¹

2023

Trans Emerging Tel Tech

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Cloud computing is a paradigm that acts as an emerging technology with the intent of multimedia. Nevertheless, multimedia computing faces a financial burden. The essential characteristic of cloud computing is the cloud's pay‐as‐you‐go pricing model. Hence, several attacks are affecting cloud services since the threats are present long term. Such harmful attacks are named Fraudulent Resource Consumption (FRC) attacks. Due to this nature, attack detection is a critical task in cloud computing. To alleviate the problem, the proposed work is intended to detect and mitigate the FRC attack in the cloud without any vital issues. Initially, the HTTP web‐server logs are collected from the benchmark source, and the data preprocessing is performed for sequence generation, followed by the sequence decomposition process. Here, the Heuristic‐based Discrete Wavelet Transform is developed by the Adaptive Deer Hunting Optimization Algorithm (ADHOA) that is to be adopted for decomposing the sequence. Further, the hyper‐parameter Tuned‐Recurrent Neural Network (HT‐RNN) with estimating attack percentage is conducted for FRC detection. Once the detection of the FRC attack is done, then the mitigation procedure is processed for blocking them. Throughout the experimental analysis, the accuracy of the proposed ADHOA‐HT‐RNN method has attained 96%, and also, the precision of the proposed work offers 94%. The specificity of the designed model has secured 92%. The performance is assessed, and its result proves that the recommended system exploits the better detection performance. Thus, the simulation outcome has shown that the proposed ADHOA‐HT‐RNN model has attained superior performance compared to the other conventional approaches.

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Kafka‐Shield: Kafka Streams‐based distributed detection scheme for IoT traffic‐based DDoS attacks

Shukla,

Krishna,

Patil

2024

Security and Privacy

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With the rapid proliferation of insecure Internet of Things (IoT) devices, the security of Internet‐based applications and networks has become a prominent concern. One of the most significant security threats encountered in IoT environments is a Distributed Denial of Service (DDoS) attack. This attack can severely disrupt critical services and prevent smart devices from functioning normally, leading to severe consequences for businesses and individuals. It aims to overwhelm victims' resources, websites, and other services by flooding them with massive attack packets, making them inaccessible to legitimate users. Researchers have developed multiple detection schemes to detect DDoS attacks. As technology advances and other facilitating factors have increased, it is a challenge to identify such powerful attacks in real‐time. In this paper, we propose a novel distributed detection scheme for IoT network traffic‐based DDoS attacks by deploying it in a Kafka Streams processing framework named Kafka‐Shield. The Kafka‐Shield comprises two stages: design and deployment. Firstly, the detection scheme is designed on the Hadoop cluster employing a highly scalable H2O.ai machine learning platform. Secondly, a portable, scalable, and distributed detection scheme is deployed on the Kafka Streams processing framework. To analyze the incoming traffic data and categorize it into nine target classes in real time. Additionally, Kafka‐Shield stores each network flow with significant input features and the predicted outcome in the Hadoop Distributed File System (HDFS). It enables the development of new models or updating current ones. To validate the effectiveness of the Kafka‐Shield, we performed critical analysis using various configured attack scenarios. The experimental results affirm Kafka‐Shield's remarkable efficiency in detecting DDoS attacks. It has a detection rate of over 99% and can process 0.928 million traces in nearly 3.027 s.

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An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments

Cited by 30 publications

References 31 publications

DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

Kafka‐Shield: Kafka Streams‐based distributed detection scheme for IoT traffic‐based DDoS attacks

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