Paper Info
Title
On Data-driven Curation, Learning, and Analysis for Inferring Evolving Internet-of-Things (IoT) Botnets in the Wild
Abstract
The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructures. The highly heterogeneous nature of IoT devices and their widespread deployments has led to the rise of several key security and measurement-based challenges, significantly crippling the process of collecting, analyzing and correlating IoT-centric data. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. The proposed work aims to classify and infer Internet-scale compromised IoT devices by solely observing one-way network traffic, while also uncovering, reporting and thoroughly analyzing “in the wild” IoT botnets. To prepare a relevant dataset, a novel probabilistic model is developed to cleanse unrelated traffic by removing noise samples (i.e., misconfigured network traffic). Subsequently, several shallow and deep learning models are evaluated in an effort to train an effective multi-window convolutional neural network. By leveraging active and passing measurements when generating the training dataset, the neural network aims to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is employed by scrutinizing a set of innovative and efficient network feature sets. Analyzing 3.6 TB of recently captured darknet traffic revealed a momentous 440,000 compromised IoT devices and generated evidence-based artifacts related to 350 IoT botnets. Moreover, by conducting thorough analysis of such inferred campaigns, we reveal their scanning behaviors, packet inter-arrival times, employed rates and geo-distributions. Although several campaigns exhibit significant differences in these aspects, some are more distinguishable; by being limited to specific geo-locations or by executing scans on random ports besides their core targets. While many of the inferred botnets belong to previously documented campaigns such as Hide and Seek, Hajime and Fbot, newly discovered events portray the evolving nature of such IoT threat phenomena by demonstrating growing cryptojacking capabilities or by targeting industrial control services. To motivate empirical (and operational) IoT cyber security initiatives as well as aid in reproducibility of the obtained results, we make the source codes of all the developed methods and techniques available to the research community at large.
Year
DOI
Venue
2020
10.1016/j.cose.2019.101707
Computers & Security
Keywords
Field
DocType
Data science,Cyber forensics,Internet-of-things,IoT Security,Internet measurements
Hierarchical clustering,Data-driven,Convolutional neural network,Computer security,Computer science,Source code,Darknet,Botnet,Network packet,Artificial intelligence,Deep learning
Journal
Volume
ISSN
Citations 
91
0167-4048
1
PageRank 
References 
Authors
0.37
0
9
Name
Order
Citations
PageRank
Morteza Safaei Pour141.81
Antonio Mangino210.37
Kurt Friday310.37
Matthias Rathbun410.37
Elias Bou-Harb520726.40
Farkhund Iqbal623030.06
Sagar Samtani7319.09
Jorge Crichigno816814.41
N. Ghani982.25