An Active and Dynamic Botnet Detection Approach to Track Hidden Concept Drift

Information and Communications Security - Lecture Notes in Computer Science ◽

10.1007/978-3-319-89500-0_55 ◽

2018 ◽

pp. 646-660

Author(s):

Zhi Wang ◽

Meiqi Tian ◽

Chunfu Jia

Keyword(s):

Concept Drift ◽

Botnet Detection ◽

Detection Approach

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A Method Aware of Concept Drift for Online Botnet Detection

GLOBECOM 2020 - 2020 IEEE Global Communications Conference ◽

10.1109/globecom42002.2020.9347990 ◽

2020 ◽

Author(s):

Bruno Henrique Schwengber ◽

Andressa Vergutz ◽

Nelson G. Prates ◽

Michele Nogueira

Keyword(s):

Concept Drift ◽

Botnet Detection

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IoT Botnet Detection Approach Based on PSI graph and DGCNN classifier

2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP) ◽

10.1109/icicsp.2018.8549713 ◽

2018 ◽

Author(s):

Huy-Trung Nguyen ◽

Quoc-Dung Ngo ◽

Van-Hoang Le

Keyword(s):

Botnet Detection ◽

Detection Approach

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CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream

Lecture Notes in Electrical Engineering - Emerging Research in Electronics, Computer Science and Technology ◽

10.1007/978-981-13-5802-9_54 ◽

2019 ◽

pp. 597-612 ◽

Author(s):

Mohammed Ahmed Ali Abdualrhman ◽

M. C. Padma

Keyword(s):

Data Stream ◽

Concept Drift ◽

Imbalanced Data ◽

Detection Approach ◽

Concept Drift Detection

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BOTNET DETECTION APPROACH BASED ON THE DISTRIBUTED SYSTEMS

International Journal of Computing ◽

10.31891/1727-6209/2020/19/2-190-198 ◽

2020 ◽

Vol 19 (2) ◽

pp. 190-198

Author(s):

Oleg Savenko ◽

◽

Anatoliy Sachenko ◽

Sergii Lysenko ◽

George Markowsky ◽

...

Keyword(s):

Distributed Systems ◽

Botnet Detection ◽

Detection Approach

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An efficient reinforcement learning-based Botnet detection approach

Journal of Network and Computer Applications ◽

10.1016/j.jnca.2019.102479 ◽

2020 ◽

Vol 150 ◽

pp. 102479 ◽

Author(s):

Mohammad Alauthman ◽

Nauman Aslam ◽

Mouhammd Al-kasassbeh ◽

Suleman Khan ◽

Ahmad Al-Qerem ◽

...

Keyword(s):

Reinforcement Learning ◽

Botnet Detection ◽

Detection Approach

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DeDroid: A Mobile Botnet Detection Approach Based on Static Analysis

2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) ◽

10.1109/uic-atc-scalcom-cbdcom-iop.2015.240 ◽

2015 ◽

Author(s):

Ahmad Karim ◽

Rosli Salleh ◽

Syed Adeel Ali Shah

Keyword(s):

Static Analysis ◽

Botnet Detection ◽

Mobile Botnet ◽

Detection Approach

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Botnet Detection Approach Using Graph-Based Machine Learning

IEEE Access ◽

10.1109/access.2021.3094183 ◽

2021 ◽

pp. 1-1

Author(s):

Afnan Alharbi ◽

Khalid Alsubhi

Keyword(s):

Machine Learning ◽

Botnet Detection ◽

Detection Approach

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A botnet detection approach based on the clonal selection algorithm

2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT) ◽

10.1109/dessert.2018.8409171 ◽

2018 ◽

Author(s):

Sergii Lysenko ◽

Kira Bobrovnikova ◽

Oleg Savenko

Keyword(s):

Clonal Selection ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Botnet Detection ◽

Detection Approach

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Ichnaea: Effective P2P botnet detection approach based on analysis of network flows

7'th International Symposium on Telecommunications (IST'2014) ◽

10.1109/istel.2014.7000837 ◽

2014 ◽

Author(s):

Rahimeh Khodadadi ◽

Behzad Akbari

Keyword(s):

Network Flows ◽

Botnet Detection ◽

Detection Approach

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A Closeness Centrality Based P2P Botnet Detection Approach Using Deep Learning

10.1109/icccnt51525.2021.9579547 ◽

2021 ◽

Author(s):

Tirthankar Sengupta ◽

Sanghamitra De ◽

Indrajit Banerjee

Keyword(s):

Deep Learning ◽

Closeness Centrality ◽

Botnet Detection ◽

Detection Approach

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