A Framework for Agent-Based Detection and Prevention of DDoS Attacks in Distributed P2P Networks

Gera, Jaideep; Battula, Bhanu Prakash

doi:10.1007/978-981-13-3462-7_2

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…As stated above, in this paper we do not consider direct threat to the whole P2P communication channel and, therefore, we do not deal with this aspect. However, as stated above, the recent scientific literature report several solutions to DoS attacks [61]- [63]. Any of them could be integrated in our approach, thus making it robust also to full DoS attacks.…”

Section: Sp2 -Resistance To Package Correlation Attacksmentioning

confidence: 91%

“…In addition, our approach focuses on the protection against privacy leakages deriving from the availability of access log information from a video surveillance based access monitoring system, therefore we do not consider direct threats to the P2P communication channel, such as DoS attacks [60]. Anyway, these menaces are thoroughly studied in the scientific literature and, therefore, even if the identification of suitable strategies for the protection from them is orthogonal to our solution, it is possible to leverage any available solution already proposed in the literature to make our approach robust also against these kinds of attack [61]- [63].…”

Section: A Attack Modelmentioning

confidence: 99%

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Anonymous Access Monitoring of Indoor Areas

2021

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Video surveillance of public spaces is a feature of modern society that has expanded quite quickly and in a pervasive way during the last decades becoming a fundamental need for both individual and collective security. But, as the sophistication of this type of systems increases, the concern about threat to individuals' right of privacy raises as well. Indeed, the video surveillance systems could breach personal privacy because location is clearly one of the most sensitive people information. Hence, preserving location privacy while achieving utility from it, is a challenging problem demanding the investigation of researchers. This paper tackles this non-trivial issue by designing a novel privacy-preserving architecture able to anonymously monitoring people access at the entrance of critical areas in an indoor space. At the same time our approach is able to provide full accountability in case of an accident or a legal requirement. Interestingly, our protocol is robust to server-side attacks and is efficient enough to be applied indoors through a set of IoT (Internet of Things) smart camera devices.

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Section: Sp2 -Resistance To Package Correlation Attacksmentioning

confidence: 91%

Section: A Attack Modelmentioning

confidence: 99%

Anonymous Access Monitoring of Indoor Areas

2021

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“…e IP segment was 192.168.6.0/24, and the computers were connected to Port2 of the Router via Switch2 [30].…”

Section: Experimental Environment Setupmentioning

confidence: 99%

“…erefore, the CMCNN-based detection method can effectively conduct multiclass detection of massive DDoS- attack traffic on the blockchain network layer and can also discriminate specific types of attack traffic. Finally, the multiclass detection performances of CMCNN and the existing methods on the blockchain network layer were compared using the model data and methods proposed in the literature [30,31]. e results are shown in Figure 11.…”

Section: Multiclass Detection Performance Of Models Undermentioning

confidence: 99%

A DDoS-Attack Detection Method Oriented to the Blockchain Network Layer

Dai

Zhang

Dong

2022

Security and Communication Networks

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By nature, a traditional attack method, denial-of-service (DDoS) attack poses a considerable threat to the security of the blockchain network layer. This paper proposes a distributed DDoS-attack traffic detection method based on a cross multilayer convolutional neural network model in the blockchain network layer. The method resolves the low generalisation, high misreporting rate, and low detection efficiency problems of the existing detection methods, which are caused by nondistinctive core features and the high complexity of robust features when detecting DDoS attacks transmitted by mixed protocols on a blockchain network layer. First, the model performs a convolution operation on preprocessed traffic on the blockchain network layer using a cross-layer method based on L2 regularisation. After this operation, the model can perceive the detailed features of attack traffic from multiple levels while enhancing the representational performance of key features; specifically, the parameters with high-variance terms are penalised to limit changes in the model’s weight parameters. The highly robust abstract features of attack traffic are extracted, thereby increasing the generalisation ability and reducing the misreporting rate of the model. Second, parametric encoding of the abstract features is performed by a stacked sparse autoencoder based on Kullback–Leibler divergence, and the sparsity of the model is adjusted to reduce the redundant data and the coupling between abstract features. The outputs of the encoded features are then effectively categorised. Finally, the global optimisation of parameters is performed by an improved random gradient-descent algorithm, which prevents oscillation of the training parameters and accelerates the model convergence. In an experimental evaluation, the proposed method achieved satisfactory binary- and multiclass detection of DDoS-attack traffic on both CSE-CIC-IDS 2018 on the AWS dataset and on the real mixed data of a blockchain network layer.

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