A Botnet-Oriented Collaborative Defense Scheme Description Language

Huan, Liming; Yu, Yang; Lv, Liangshuang; Li, Shiying; Xia, Chunhe

doi:10.1109/cis.2013.143

Cited by 1 publication

(1 citation statement)

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“…Other researchers and corporations can contribute to the system with modules to improve upon BotFlex. In other community-driven approaches, Reference [213] proposes the use of a botnet defence description language to describe the tasks and information sharing primitives between devices handling botnet defence. Some community-driven efforts attempt to detect and prevent botnets by providing databases with known spam bots such as the The Spamhaus Project [218] and IBM X-Force exchange [219], where IT researchers can report suspected IP addresses and see a list of IP addresses along with a % indicator of how likely the IP is used for C&C. Furthermore Structured Threat Information eXpression (STIX) is used for exchanging cyber threat intelligence (CTI) as described in [220].…”

Section: Community Driven Tools Against Botnetsmentioning

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: Community Driven Tools Against Botnetsmentioning

confidence: 99%