Autocorrelation Analysis of Financial Botnet Traffic

Nagarajan, Prathiba; Troia, Fabio Di; Austin, Thomas H.; Stamp, Mark

doi:10.5220/0006685705990606

Cited by 4 publications

(1 citation statement)

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“…Use of models like support vector machine(SVM) and Hidden Markov models(HMM) are increasing to identify malware and botnets. Autocorrelation plots of network traffic was used to identify the patterns in bots [23]. The patterns when applied to actual were able to identify data from bots with very high probability.…”

Section: Botnet Detection Methodsmentioning

confidence: 99%

Management and Security of IoT systems using Microservices

Kammara

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Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Pi's and IoT devices from different vendors. We validate our design by presenting some simple experimental results. v ACKNOWLEDGMENTS I would like to express my deepest gratitude to my project advisor, Dr. Melody Moh, for her excellent guidance, and encouragement throughout the course of this project. My sincere thanks to my committee members, Dr. Robert Chun, and Dr. Thomas Austin for their useful comments, and their valuable time for reviewing my work. My special thanks to my friends and family members who believed in me, who supported me in every step of this endeavor. Thanks to the wonderful open source community out there for making source code available that was used for some parts in this project. Thanks to hacker community and security enthusiasts who provided some pointers to complete the malware testing part of this project. vi

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Section: Botnet Detection Methodsmentioning

confidence: 99%