Identification of Botnet Attacks Using Hybrid Machine Learning Models

[1]  Mansour Sheikhan,et al.  Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach , 2017, Comput. Commun..

[2]  Ali A. Ghorbani,et al.  Botnet detection based on traffic behavior analysis and flow intervals , 2013, Comput. Secur..

[3]  J. K. Kalita,et al.  Botnet in DDoS Attacks: Trends and Challenges , 2015, IEEE Communications Surveys & Tutorials.

[4]  Tsutomu Matsumoto,et al.  IoTPOT: A Novel Honeypot for Revealing Current IoT Threats , 2016, J. Inf. Process..

[5]  Jasni Mohamad Zain,et al.  A static approach towards mobile botnet detection , 2016, 2016 3rd International Conference on Electronic Design (ICED).

[6]  Nick Feamster,et al.  Machine Learning DDoS Detection for Consumer Internet of Things Devices , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[7]  Sidi-Mohammed Senouci,et al.  A lightweight anomaly detection technique for low-resource IoT devices: A game-theoretic methodology , 2016, 2016 IEEE International Conference on Communications (ICC).

[8]  Sven Nomm,et al.  Dimensionality Reduction for Machine Learning Based IoT Botnet Detection , 2018, 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[9]  Jose Romero-Mariona,et al.  IoDDoS - The Internet of Distributed Denial of Sevice Attacks - A Case Study of the Mirai Malware and IoT-Based Botnets , 2017, IoTBDS.

[10]  Thiemo Voigt,et al.  SVELTE: Real-time intrusion detection in the Internet of Things , 2013, Ad Hoc Networks.

[11]  Jens Myrup Pedersen,et al.  An efficient flow-based botnet detection using supervised machine learning , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[12]  Gürkan Gür,et al.  Software-Defined Edge Defense Against IoT-Based DDoS , 2017, 2017 IEEE International Conference on Computer and Information Technology (CIT).

[13]  Elisa Bertino,et al.  Kalis — A System for Knowledge-Driven Adaptable Intrusion Detection for the Internet of Things , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[14]  Elisa Bertino,et al.  Botnets and Internet of Things Security , 2017, Computer.

[15]  Amritanshu Pandey,et al.  Identification of Phishing Attack in Websites Using Random Forest-SVM Hybrid Model , 2018, ISDA.

[16]  Ali A. Ghorbani,et al.  Toward developing a systematic approach to generate benchmark datasets for intrusion detection , 2012, Comput. Secur..

[17]  Yu Chen,et al.  Ultra-lightweight deep packet anomaly detection for Internet of Things devices , 2015, 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC).

[18]  Burak Kantarci,et al.  Anomaly detection and privacy preservation in cloud-centric Internet of Things , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[19]  M. A. Faizal,et al.  Machine Learning for HTTP Botnet Detection Using Classifier Algorithms , 2018 .

[20]  Ali A. Ghorbani,et al.  IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS 1 Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods , 2022 .

[21]  István Szabó,et al.  On the Validation of Traffic Classification Algorithms , 2008, PAM.

[22]  Lei Wu,et al.  Honeypot detection in advanced botnet attacks , 2010, Int. J. Inf. Comput. Secur..