A Combined Fusion and Data Mining Framework for the Detection of Botnets

Kiayias, Aggelos; Neumann, Justin; Walluck, David; McCusker, Owen

doi:10.1109/catch.2009.9

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…These works are complemented by comprehensive study of Barbará et al (2001) who constructed experimental testbed for intrusion detection with data mining methods. Detection model combining data fusion and mining and respective components for Botnets identification was developed by Kiayias et al (2009) too. Similar approach is presented in Alazab et al (2011) who proposed Full-size  DOI: 10.7717/peerj-cs.267/fig- 10 and implemented zero-day malware detection system with associated machine-learning based framework.…”

Section: Purposementioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

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The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used ‘as-is’ or adapted for specific purposes, has not been thoroughly investigated. This article addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and ‘grey’ publications. We find that data mining methodologies are primarily applied ‘as-is’. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

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Section: Purposementioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…These works are complemented by comprehensive study of Barbará et al (2001) who constructed experimental testbed for intrusion detection with data mining methods. Detection model combining data fusion and mining and respective components for Botnets identification was developed by Kiayias et al (2009) Authors proposed integrated IoT Big Data Analytics framework. This research is complemented by interdisciplinary study of Zhong et al (2017) where IoT and wireless technologies are used to create RFID-enabled environment producing analysis of KPIs to improve logistics.…”

Section: Purpose 4 -To Incorporate Context-awareness Aspectsmentioning

confidence: 99%

“…Main Adaptation Purpose Publications 1) To implement fully scaled, integrated data mining solution Sun et al (2015), Hu et al (2010), Wang (2015), Du et al (2017), Ç inicioglu et al (2011), Doreswamy (2008), Güder et al (2014), Simoff and Galloway (2008), Deng et al (2011), Xu and Qiu (2008), Shin and Jeong (2005), Chee et al (2016), Yu et al (2009), Ding and Daniel (2007), Liu et al (2018), Shao et al (2008) 2) To implement complex systems and integrated business applications with data mining model/solution as component or tool Mobasher (2007), Singh et al (2014), Alazab et al (2011), Kisilevich et al (2013), Haruechaiyasak et al (2004), Luna et al (2017), Khan et al (2013), Ortega et al (2015), Lau et al (2018), Ahmed et al (2011), Capozzoli et al (2017), Kabir (2016), Kiayias et al (2009), Kamrani et al (2001), Büchner and Mulvenna (1998), Shahbaz et al (2010), Lee et al (2001), Lee et al (2000), Barbará et al (2001), Lenz et al (2018) 3) To implement data mining as part of integrated/combined specialized infrastructure,data environments and types (eg. IoT, cloud, mobile networks) Manuscript to be reviewed…”

Section: Appendicesmentioning

confidence: 99%

Peer Review #1 of "Adaptations of data mining methodologies: a systematic literature review (v0.1)"

2020

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The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA, has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used 'as-is' or adapted for specific purposes, has not been thoroughly investigated. This paper addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and 'grey' publications. We find that data mining methodologies are primarily applied 'as-is'. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

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From detection to reaction - A holistic approach to cyber defense

Klein

Tölle

Martini

2011

2011 Defense Science Research Conference and Expo (DSR)

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The cyber defense context includes various activities that are often investigated and discussed individually. The process from the detection of a threat to its eventual treatment clearly resembles other decision-making paradigms. By aligning the cyber defense process to the well-known OODA loop, in which the activities in one phase of the loop are prerequisites for those in other phases, situational awareness with respect to information assurance can be established

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A Combined Fusion and Data Mining Framework for the Detection of Botnets

Cited by 4 publications

References 12 publications

Adaptations of data mining methodologies: a systematic literature review

Adaptations of data mining methodologies: a systematic literature review

Peer Review #1 of "Adaptations of data mining methodologies: a systematic literature review (v0.1)"

From detection to reaction - A holistic approach to cyber defense

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