BotViz: A memory forensic-based botnet detection and visualization approach

Sharafaldin, Iman; Gharib, Amirhossein; Lashkari, Arash Habibi; Ghorbani, Ali A.

doi:10.1109/ccst.2017.8167804

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Monitoring activity from DNS-queries during C&C communication or updates and applying semi-supervised fuzzy c-means clustering to produce security scenarios is the basis of the self-adaptive system called BotGRABBER [161]. Not much different is the method proposed by Sharalfaldin et al in [168], where a novel botnet detection framework, BotViz, is presented. BotViz uses a combination of DNS-based analysis of host PC DNS records and API hook forensics on memory dumps to detect potentially vulnerable systems.…”

Section: Domain Name System (Dns) Based Detectionmentioning

confidence: 99%

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

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Section: Domain Name System (Dns) Based Detectionmentioning

confidence: 99%