Many security issues have come to the fore with the increasingly widespread adoption of Internet-of-Things (IoT) devices. The Mirai attack on Dyn DNS service, in which vulnerable IoT devices such as IP cameras, DVRs and routers were infected and used to propagate large-scale DDoS attacks, is one of the more prominent recent examples. IoT botnets, consisting of hundreds-of-thousands of bots, are currently present in-the-wild'' at least and are only expected to grow in the future, with the potential to cause significant network downtimes and financial losses to network companies. We propose, therefore, to build testbeds for evaluating IoT botnets and design suitable mitigation techniques against them. A DETERlab-based IoT botnet testbed is presented in this work. The testbed is built in a secure contained environment and includes ancillary services such as DHCP, DNS as well as botnet infrastructure including CnC and scanListen loading servers. Developing an IoT botnet testbed presented us with some unique challenges which are different from those encountered in non-IoT botnet testbeds and we highlight them in this paper. Further, we point out the important features of our testbed and illustrate some of its capabilities through experimental results.
There have been several research works on the study of botnets through testbeds. @cite_8 have presented a toolkit for Emulab-enabled testbeds (such as DETERlab) called Botnet Evaluation Environment (BEE), designed to provide bots and botnets for experimentation in a scalabale and secure self-contained environment. BEE includes a library of OS bot images to be run on physical and virtual machines as well as a set of services and tools required for botnet evaluation such as DHCP, DNS, IRC, VM monitors and honeypots. @cite_7 has proposed a composable botnet framework called SLINGbot which can be used to construct botnets with different (Command and Control) C2 structures (including potential future botnet C2 structures), simulate botnet traffic, characterize it and develop botnet defense techniques.
@cite_7 @cite_8
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There is an urgent need for a pro-active approach to botnet detection and mitigation that will enable computer network defenders to characterize emerging and future botnet threats and design effective defense techniques before these threats materialize. To this end, we have developed a System for Live Investigation of Next Generation bots (SLINGbot). SLINGbot is an extensible, composable bot framework that enables researchers to construct benign bots for the purposes of generating and characterizing botnet Command and Control (C2) traffic. This enables researchers to simulate current and potential future botnet traffic, characterize it, and design effective defense techniques. In this paper, we describe the SLINGbot system and how it can be used for the pro-active development of botnet defenses. An in-depth understanding of botnet behavior is a precursor to building effective defenses against this serious and growing threat. In this paper we describe our initial steps toward building a flexible and scalable laboratory testbed for experiments with bots and botnets. Our Botnet Evaluation Environment (BEE) is designed to enable individual bots or networks of up to thousands of bots to be tested in a secure, self-contained framework. BEE is being developed as a toolkit for Emulab-enabled network testbeds; a design choice made to obviate the need for building user experiment management functions and to enable access to collections of computing hosts. The focus of our implementation efforts has been on building a library of OS Bot images that can be run on individual systems or on virtual machines. The library currently includes images generated from source code of four well known bots (Agobot, GTbot, Spybot, SDbot) and from binary code for several unknown bots, and a number of Windows OS variants. BEE also includes a set of services that are required for botnets including DHCP, DynDNS, and IRC, as well as other tools that are useful for botnet measurement and evaluation such as VM monitors and honeypots. To demonstrate the utility of BEE, we describe a simple set of tests that characterizes command and control traffic from three different botnet configurations.