Botnet identification in multi-clustered DDoS attacks

Matta, Vincenzo; Mauro, Mario Di; Longo, Maurizio

doi:10.23919/eusipco.2017.8081594

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…The aim of this section is to show that the BotClusterBuster algorithm proposed in [22] can identify consistently the botnet clusters hidden in the network. The core of such algorithm is a pairwise comparison between a pivot element (a single node or an estimated botnet) and a test node.…”

Section: Botnet Identification Algorithmmentioning

confidence: 99%

Application-Layer DDOS Attacks with Multiple Emulation Dictionaries

Cirillo

Mauro

Matta

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the problem of identifying the members of a botnet under an application-layer (L7) DDoS attack, where a target site is flooded with a large number of requests that emulate legitimate users' patterns. This challenging problem has been recently addressed with reference to two simplified scenarios, where either all bots pick requests from the same emulation dictionary (total overlap), or they are divided in separate clusters corresponding to distinct emulation dictionaries (no overlap at all). However, over real networks these two extreme conditions are difficult to realize, and the intermediate situation is observed where the emulation patterns of distinct bots belong to partially overlapped dictionaries. This intermediate situation introduces significant sophistication in the bot identification problem. In order to address this issue, we provide an analytical characterization of the pairwise cluster interaction, which is exploited to devise an identification rule to discriminate legitimate users from bots and to identify the individual bot clusters.

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Section: Botnet Identification Algorithmmentioning

confidence: 99%

Application-Layer DDOS Attacks with Multiple Emulation Dictionaries

Cirillo

Mauro

Matta

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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“…One‐to‐many data sharing has been achieved successfully with attribute‐based encryption (ABE), that thought to be the most promising approach. In addition, it also offers fine‐grained access control, 11 Any user could recover a cipher text using the identity‐based encryption method known as ABE if and only if they possessed the set of attributes designated by the data's owner 2,12 . ABE technique has been closely examined as a one‐to‐many cryptographic solution for offering fine‐grained access control capability, ensuring the security of outsourced data while also allowing flexible access policy 13,14 …”

Section: Introductionmentioning

confidence: 99%

“…In addition, it also offers fine-grained access control, 11 Any user could recover a cipher text using the identity-based encryption method known as ABE if and only if they possessed the set of attributes designated by the data's owner. 2,12 ABE technique has been closely examined as a one-to-many cryptographic solution for offering fine-grained access control capability, ensuring the security of outsourced data while also allowing flexible access policy. 13,14 The Medical IoT uses a secure fog-cloud architecture to protect the resources of these devices while ensuring the confidentiality of medical data.…”

mentioning

confidence: 99%

Authentication of fog‐assisted IoT networks using Advanced Encryption credibility approach with modified Diffie–Hellman encryption

Luqman

Faridi

2023

Concurrency and Computation

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SummaryCloud storage in the network enables the users to access the data from anywhere irrespective of any devices, which prevents data loss and provides centralized management. The cloud server is a pooled, centrally located server resource that many users can access as needed. More efficient data storage and processing could be achieved through fog computing devices and, the fog node handles the task including the semantic transformation of the data before transmitting it to the back‐end cloud servers, managing the process of data from several edge devices simultaneously. The security of the cloud and fog nodes acts as a key component, and that must be ensured for effective computations. In this research, the security of the fog‐assisted Internet of Things (IoT) network is enhanced using the proposed advanced encryption credibility approach (Ad‐En credibility approach), which ensures the privacy and security of the networks by preventing malicious attempts of accessing the data. The proposed Ad‐En credibility approach is performed based on a modified Diffie‐Hellman algorithm that consists of different phases such as the setup phase, entry and identity phase, fog node entry validation phase, and authentication phase. These phases perform different functions and provide strong authentication mechanisms that secure the data with high confidentiality by detecting malicious attempts within a reduced time. The efficiency of the research is proved by measuring the computational time, memory usage, and detection rate. The proposed Ad‐En credibility approach consumed less time of 7 s and memory usage of 8.17 mb with a high detection rate of 85.34%.

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“…The most challenging issues of WSN are energy efficiency, appropriate CH selection and secure data delivery. The secure and fair CH selection protocol aids in achieving classification and attacker identification [14, 10]. Clustering is considered as an effective and scalable technique in WSN [15, 16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization algorithm for security aware cluster head selection process to aid hierarchical routing in wireless sensor network

2021

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In wireless sensor networks, clustering is said to be the most noteworthy technique for increasing the lifetime of network that directly leads a better routing mechanism. This approach involves grouping of sensor nodes to clusters and choosing the appropriate cluster heads for each cluster. In fact, cluster heads gathers data from corresponding nodes in cluster and transmits those aggregated data to base station. However, the major issue in this is the selection of the appropriate cluster head. Till now, many research works have been carried out for solving this issue by considering different constraints. This paper introduces a new cluster-based routing model by selecting the optimal cluster head. Moreover, a novel algorithm known as grey wolf updated whale optimization algorithm is introduced. Here, a new multi-objective function is defined with respect to different constraints like distance, delay, security and energy, respectively. Finally, the performance of security aware clustering with grey wolf updated whale optimization algorithm is evaluated and validated over other conventional works with respect to alive node analysis, throughput and normalized network energy, respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Botnet identification in multi-clustered DDoS attacks

Cited by 13 publications

References 16 publications

Application-Layer DDOS Attacks with Multiple Emulation Dictionaries

Application-Layer DDOS Attacks with Multiple Emulation Dictionaries

Authentication of fog‐assisted IoT networks using Advanced Encryption credibility approach with modified Diffie–Hellman encryption

Hybrid optimization algorithm for security aware cluster head selection process to aid hierarchical routing in wireless sensor network

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