@inproceedings{b4be306f285145d7b20f0e4ff8bf9a66,
title = "Evidence Fusion for Malicious Bot Detection in IoT",
abstract = "Billions of devices in the Internet of Things (IoT) are inter-connected over the internet and communicate with each other or end users. IoT devices communicate through messaging bots. These bots are important in IoT systems to automate and better manage the work flows. IoT devices are usually spread across many applications and are able to capture or generate substantial influx of big data. The integration of IoT with cloud computing to handle and manage big data, requires considerable security measures in order to prevent cyber attackers from adversarial use of such large amount of data. An attacker can simply utilize the messaging bots to perform malicious activities on a number of devices and thus bots pose serious cybersecurity hazards for IoT devices. Hence, it is important to detect the presence of malicious bots in the network. In this paper we propose an evidence theory-based approach for malicious bot detection. Evidence Theory, a.k.a. Dempster Shafer Theory (DST) is a probabilistic reasoning tool and has the unique ability to handle uncertainty, i.e. in the absence of evidence. It can be applied efficiently to identify a bot, especially when the bots have dynamic or polymorphic behavior. The key characteristic of DST is that the detection system may not need any prior information about the malicious signatures and profiles. In this work, we propose to analyze the network flow characteristics to extract key evidence for bot traces. We then quantify these pieces of evidence using apriori algorithm and apply DST to detect the presence of the bots.",
keywords = "Apriori Algorithm, Big data, Botnet, Bots, Cyber Security, Dempster-Shafer Theory, Internet of Things (IoT)",
author = "Moitrayee Chatterjee and Namin, {Akbar Siami} and Prerit Datta",
note = "Funding Information: This work is supported in part from National Science Foundation under the grant number 1821560. Funding Information: ACKNOWLEDGMENT This work is supported in part from National Science Foundation under the grant number 1821560. Publisher Copyright: {\textcopyright} 2018 IEEE.; null ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2019",
month = jan,
day = "22",
doi = "10.1109/BigData.2018.8621895",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4545--4548",
editor = "Yang Song and Bing Liu and Kisung Lee and Naoki Abe and Calton Pu and Mu Qiao and Nesreen Ahmed and Donald Kossmann and Jeffrey Saltz and Jiliang Tang and Jingrui He and Huan Liu and Xiaohua Hu",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
}