Vulnerabilities in IoT Devices for Smart Home Environment

ıs Costa

ao Paulo Barros

1,2

and Miguel Tavares

1,2,3

Instituto Polit

ecnico de Beja, Beja, Portugal

Center of Technology and Systems (CTS) - UNINOVA, Portugal

Instituto de Engenharia de Sistemas e Computadores - Investigac¸

ao e Desenvolvimento - Lisbon (INESC-ID), Portugal

Keywords:

Internet of Things, Smart Home, IoT, IoT Security, Information Security, IoT Vulnerabilities.

Abstract:

Recently, consumers have seen multiple products being advertised as smart home. These products promise to

make our homes more comfortable, safe, automated, and remotely controlled. To this new reality of process-

ing information it was given the name IoT (Internet of Things). Many news headlines have been published

exposing serious security vulnerabilities in many IoT devices, with some of them being exploited to make

one of the largest DDoS attacks recorded. In this paper we present a method developed with the purpose of

identifying high risk vulnerabilities in smart home IoT devices, giving application examples of actual vulner-

abilities found in two commercially available devices. This method uses several open source tools to identify

vulnerabilities in some of these IoT devices. Besides, we will also present some topics related to the main

threats and vulnerabilities that affect smart home IoT devices.

1 INTRODUCTION

IoT devices are gradually peaking the interest of con-

sumers. Several studies predict an almost exponen-

tial growth on the number of IoT devices, totalling

between 20 and 30 billion by 2020 (calsoft, 2018),

with the smart home IoT market also growing in rev-

enue. According to consumers (Perez, 2018), security

problems are the main factor that is slowing down IoT

growth.

In the past years, IoT devices have been seen

many times as insecure devices. In 2015 Syman-

tec (Barcena and Wueest, 2015) released a report

on which major vulnerabilities on many smart home

IoT devices were identiﬁed. In 2016 the Mirai boot-

net was responsible for one of the biggest DDoS

attacks ever recorded, using more than 600 thou-

sand devices, most of them IP cameras (Anton-

akakis et al., 2017). Two online databases https://

www.exploitee.rs/ and http://www.hardwaresecurity.

org/iot/database/) describe vulnerabilities on more

than 200 devices, where about half of them are smart

home IoT. On DEF CON 2017, one of the largest se-

curity and hacking conferences, 47 new vulnerabil-

ities were revealed on 23 devices from 21 different

manufacturers.

This number of vulnerabilities may lead con-

sumers to think that all IoT devices have serious se-

curity vulnerabilities, which, however, is not entirely

true. Even if a device has vulnerabilities, that does not

mean it is insecure. If a vulnerability can only be ex-

plored with physical access to the device, it represents

a small risk for the average consumer. With these fac-

tors in mind, it is crucial to identify vulnerabilities

that can be explored remotely, especially through the

internet.

This paper presents a method to identify vulner-

abilities in smart home IoT devices. This method

represents a practical approach that uses open source

tools to identify vulnerabilities that can be explored

through TCP/IP networks. The method can even be

applied to devices that aren’t smart home, allowing

manufacturers to identify the most explored vulnera-

bilities in cyberattacks, with a low cost method.

Additionally, the paper also presents a vision of

what IoT and smart home IoT are, by analysing as-

pects of its ecosystem, main vulnerabilities and their

real risk. Through these topics we will identify the

higher risk threats to data protection and to private

home security.

2 RELATED WORK

To the best of our knowledge, there is no method that

allows the identiﬁcation and exploitation of vulnera-

Costa, L., Barros, J. and Tavares, M.

Vulnerabilities in IoT Devices for Smart Home Environment.

DOI: 10.5220/0007583306150622

In Proceedings of the 5th International Conference on Information Systems Security and Privacy (ICISSP 2019), pages 615-622

ISBN: 978-989-758-359-9

615

bilities in smart home IoT devices like the one here

presented. There are a few similar ones, but they are

based on automated scanning tools, that have trouble

if they cannot correctly identify the device (Visoot-

tiviseth et al., 2017)(Leite Da Silva, 2017). There are

others that focus on hardware and non TCP/IP net-

works (like Bluetooth and Zigbee) (Gupta, 2017), and

LLN networks that rely on IPv6 not currently adopted

by smart home IoT systems (Martins, 2018).

3 SMART HOME IOT DEVICES

Smart home is one of the many components that com-

poses IoT. According to one deﬁnition, a smart home

”is a house or living environment that contains the

technology to allow home devices and systems to be

controlled automatically” (Bing et al., 2011). Smart

home is a concept that has been around for quite some

time, but IoT made it more attractive.

There is still no consensual deﬁnition for the smart

home in the IoT era (Schiefer, 2015). For this reason

it is important to present the deﬁnition adopted in this

paper: smart home is ”a residence equipped with a

communications network, linking sensors and domes-

tic devices and appliances that can be remotely mon-

itored, accessed or controlled, and which provides

services that respond to the needs of its inhabitants”

(Ghaffarianhoseini et al., 2016).

3.1 From Architecture to Reality - The

Security Perspective

At some point we need to step out from the conceptual

models of architectures and look at the implementa-

tions adopted by the manufacturers. Because there

are no standard practices applied (Al-Qaseemi et al.,

2017), sometimes manufacturers’implementations do

not follow the architectures. This scenario may

change by global agreement of stakeholders in IoT

development, with efforts like the Code of Practice

for Consumer IoT Security (Department for Digital,

Culture, Media and Sport (DCMS), 2018). Since we

are presenting a practical method, we need to deﬁne

how the current smart home IoT devices implement

the theoretical architectures. To this implementation

we will call smart home IoT ecosystem.

This ecosystem was built by studying the design

implemented by several manufacturers and also ac-

cording to the work of experts that conduct penetra-

tion tests on IoT devices (Pesce, 2017)(Gupta, 2017).

This ecosystem is divided into ﬁve components (see

Fig. 1):

• Hardware - Includes all the physical components

of the ecosystem including the ﬁrmware;

• Web Application - It can be used to conﬁgure,

control or access a device. In many cases, it is

hosted in a web server that resides in the device

itself;

• Mobile Application - These applications are

gradually replacing web applications since they

present more features and are more practical for

the user;

• Network and Cloud - It includes all communica-

tion between devices, the web and mobile appli-

cation, and the cloud;

• API - The Application Programming Interface

plays a crucial role in IoT. It makes the integra-

tion and interoperability between devices, appli-

cations, and the cloud possible.

Figure 1: Interaction between components of the smart

home IoT ecosystem.

4 SMART HOME IOT SECURITY

In this section we discuss security challenges, threats,

and current vulnerabilities of smart home IoT devices.

4.1 Security Challenges

IoT systems, especially smart home, have many

things in common with traditional computer net-

works, including some security problems. Within a

home network we may have devices like computers,

smartphones, and tablets that have lots of process-

ing power and are constantly monitored by users. On

the other hand, IoT devices have hardware and design

ICISSP 2019 - 5th International Conference on Information Systems Security and Privacy

616

limitations that prevent traditional security measures

from being easily implement. The following are some

of these limitations(Haritha and Lavanya, 2017):

• IoT devices often use low speed CPUs and are bat-

tery powered. Contemporary cryptography algo-

rithms require fast computation;

• IoT devices usually have memory restriction and

use low bandwidth communications. Current se-

curity schemes were not designed to support these

restrictions;

• In some cases, the installation of security patches

may be impossible. IoT devices use lightweight

operating system that might lack modules to re-

ceive and integrate new codes or libraries;

• The IoT universe is made of different types of de-

vices ranging from computers to RFID cards us-

ing different wireless protocols. It is hard to ﬁnd

a security solution that is capable of accommodat-

ing all these devices.

4.2 Top Vulnerabilities

One of the differences between traditional computer

networks and IoT ones are the threats they face. Many

times, smart home IoT attacks have consequences

that transcend the boundaries of cyberspace and affect

people lives and the safety of their homes (Denning

et al., 2013)(Soltan et al., 2018). But many of these

attacks explore vulnerabilities that are well known by

IT Administrators and security experts. These vulner-

abilities gained an all new ”life” within IoT.

Within this big media coverage that preach an IoT

apocalypse (Newman, 2018)(Hiner, 2018) it is impor-

tant to identify the vulnerabilities that pose the biggest

risk to the safety of smart home IoT and their real im-

pact. Some may affect a big number of devices but

are not very easily exploited (Dorsey, 2018), there-

fore have a low risk. One of the biggest treats to smart

home networks are automated attacks. They have not

a speciﬁc home as a target, but a type of device or a

speciﬁc network service. With these aspects in mind,

the vulnerabilities that are more likely to be exploited

on smart home IoT attacks are the following(OWASP,

b)(Khan and Salah, 2018)(Embedi, 2018):

• Lack of security updates;

• Insecure web application and services authentica-

tion;

• Insecure services exposed to the internet;

• Insecure network communications.

4.3 Penetration Testing

IoT devices are bringing new security challenges to

home network security (see section 4.1). A vulnera-

bility in a smart door lock can lead to a robbery or

to other violent crimes (Denning et al., 2013). So

the need to test home network security can increase

with the growing adoption of smart home IoT devices.

Penetration testing is a well know technique applied

by ethical hackers, that makes use of some of the tools

employed by malicious hackers, in order to simulate

attacks and identify security vulnerabilities.

The method to identify vulnerabilities in smart

home IoT devices that we propose is based on the

PTES Standard (http://www.pentest-standard.org/).

This standard needed to be adapted since it lacks

some speciﬁc steps related to IoT smart home sys-

tems. For example, some of the information gather-

ing techniques suggested by the standard are targeted

to collect information about persons, like a company

CIO. In a smart home IoT ecosystem (see section 3.1)

the human factor is not that important so we need to

gather information from other sources like the devices

ﬁrmware or mobile application source code, to under-

stand how devices interact.

5 IDENTIFYING

VULNERABILITIES

This section presents the proposed method. The re-

spective results are presented in Section 6.

5.1 Introduction

The previous sections provided useful information in

order to understand the ideas behind this method.

Since we are still in the beginning of the IoT era

and manufacturers are still waiting for standardisation

of architectures and technologies (Briodagh, 2018),

there is no universal recipe to identify vulnerabilities

in IoT systems. By having a general understanding

of the ideas, challenges, current architectures, threats,

and vulnerabilities, this method becomes more eas-

ily adapted to different and future smart home IoT

ecosystems.

5.2 Method Structure

The method is divided into different stages and proce-

dures represented by an activity diagram (Omg, 2017)

(see Fig. 2).

Vulnerabilities in IoT Devices for Smart Home Environment

617

Figure 2: Activity diagram of the proposed method.

All the procedures and stages at the same level

can be executed in parallel, but only after all are com-

pleted we can move to the next level. The ones that

cannot be executed for some reason (e.g. the device

does not have a web interface) should be ignored to-

gether with the respective arcs; this would imply ad-

ditional paths in the diagram, yet, for better readabil-

ity, they are not presented. To make interpreting the

method easier, the numbering in the diagram matches

the ones of the following subsections.

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5.3 Install and Conﬁgure an Operating

System (OS)

Correctly conﬁguring a test environment is a key as-

pect in order to ensure that the tools we use work

properly. The easiest way is to use a pre-build operat-

ing system (OS) dedicated to ethical hacking. These

include some of the tools that will be used later in

the method. There are many OS’s available, but it is

import to choose one that is frequently updated. The

ones we tested and therefore recommend are the fol-

lowing:

• Kali Linux (https://www.kali.org)

• Parrot OS (https://www.parrotsec.org).

We also recommend to install the operating sys-

tem in a virtual machine. It is more practical and there

is there is no need for a dedicated computer just for

this purpose. We will call this machine ”HackOS”.

5.4 Install and Conﬁgure a Wireless

Network

Most smart home IoT devices require a wireless net-

work to communicate. By using a speciﬁc network

for the method we are reducing the number of com-

munications to analyse, and not taking the risk of ex-

posing other network devices to security risks. To

make intercepting communications easier we recom-

mend creating a WiFi Access Point in the ”HackOS”

machine.

5.5 Passive Information Gathering

This stage focuses on collecting information about the

smart home IoT device and its ecosystem. In this

stage no communication should be made with the IoT

device or its ecosystem. The point here is to deter-

mine what kind of information is publicly available.

This stage is divided into three procedures:

5.5.1 Proc. 1 – Identify the Device and its

Components

Before starting to collect more information about the

device it’s important to identify the brand, model and

manufacturer. If possible, the device should be dis-

mantled in order to identify any part numbers, logos

or other inscriptions.

5.5.2 Proc. 2 – Collect Documentation and

Information

The more information we collect on a device, the big-

ger the chances are of understanding the device and

how it interacts with its ecosystem. Look for user

manuals, API documentation, open source projects,

and ﬁrmware installationﬁles;

5.5.3 Proc. 3 – Collect Information about the

Mobile Application

Most IoT device have a mobile application used to

control and collect data from them. On the Android

operating system it is possible to obtain an installation

ﬁle directly from an online repository. The following

are the most popular ones:

• APKmirror (https://www.apkmirror.com/);

• Aptoide (https://pt.aptoide.com/);

• APKpure (https://apkpure.com).

5.6 Active Information Gathering

Active information gathering means collecting in-

formation about the smart home IoT device and its

ecosystem by communicating and intercepting their

network trafﬁc. This stage is divided into three proce-

dures presented in the next subsections.

5.6.1 Proc. 1 – Device Identiﬁcation

This procedure is about identifying the devices’ IP

and MAC addresses in the network. If the device has a

WiFi network for conﬁguration we should try to con-

nect to it before making any conﬁgurations. Some

devices expose different ports or services or have a

different behaviour before they are conﬁgured.

To identify the IP and MAC address of the device

we recommend using the tool Netdiscover (https://

github.com/alexxy/netdiscover).

5.6.2 Proc. 2 – Network Trafﬁc Capture

By capturing the communications sent and received

by the device and its ecosystem we can extract valu-

able information later on. Since some IoT devices

communicate in a different way based on their state,

it is important to capture the devices network trafﬁc

in different states (Pesce, 2017):

• Booting up, without conﬁguration and in stand-

by;

• Communicating with the mobile and web applica-

tions;

• During a ﬁrmware update;

• Without internet connection.

To capture the network trafﬁc we recommend us-

ing the tool Wireshark (https://www.wireshark.org/).

Vulnerabilities in IoT Devices for Smart Home Environment

619

5.6.3 Proc. 3 – Port and Service Identiﬁcation

The ﬁnal procedure of this stage allows us to enumer-

ate the network ports that are open on the device and

what services they expose. Some devices have dozens

of open ports (Loi et al., 2017) that expose services

like SSH or Telnet.

The recommended tools for this procedure are

Nmap (https://nmap.org/) and Zenmap (https://

nmap.org/zenmap/).

5.7 Trafﬁc Analysis

This section is about analysing the network captures

made in the previous section. The goal of this analy-

sis is to determine how the different ecosystem com-

ponents interact with each other, and what kind of in-

formation is sent and received. The following are the

key aspects to identify at this stage:

• Proc. 1 – Endpoint Identiﬁcation: Identify IP ad-

dress, domain and location of all endpoints;

• Proc. 2 – Ports and Protocols: What network ports

and protocols were used;

• Proc. 3 – Data Sent Without Encryption;

• Proc. 4 – Files Intercepted;

• Proc. 5 – Information Sent to the Internet;

In order to analyse the network captures we rec-

ommend using the tool Wireshark.

5.8 Remote Access Vulnerabilities

Some IoT devices have a remote access protocol

available, like Telnet or SSH. In fact many devices

that formed the Mirai bootnet (Antonakakis et al.,

2017) were infected because they had their Telnet ser-

vice exposed to the internet with default credentials.

The most common attacks to these kind of services

are Brute-Force or Dictionary-Attacks.

To test if the remote access service is vul-

nerable to any of the attacks mentioned or oth-

ers, we recommend using the tools Medusa (https:

//github.com/jmk-foofus/medusa), Ncrack (https://

github.com/nmap/ncrack) or Hydra (https://github.

com/vanhauser-thc/thc-hydra).

5.9 Firmware Vulnerabilities

Exploring an IoT device ﬁrmware is one the most

common ways to identify its vulnerabilities. Since

some of these devices feature a striped down version

of a Linux OS, their ﬁle structure and services are

very similar to the desktop version. One of the most

common vulnerabilities found is the exposure of sen-

sitive information (OWASP, b) like login credentials.

We propose a two step approach in order to identify

sensitive information in the ﬁrmware.

5.9.1 Step 1 – Firmware Image Analysis and File

Extraction

The objective of this procedure is to extract the

ﬁrmware ﬁle system from the ﬁrmware image, typi-

cally a BIN ﬁle. Some vendors have these ﬁles avail-

able for download from their websites, or we can

try to get it by capturing the network trafﬁc during

a ﬁrmware update (some devices use plain HTTP on

update). There are other methods that involve extract-

ing the ﬁrmware directly from the device storage, but

they require physical access to the device.

To extract the ﬁrmware ﬁles from the image

we recommend using binwalk (https://github.com/

ReFirmLabs/binwalk).

5.9.2 Step 2 – Firmware Analysis

After getting access to the ﬁles in the ﬁrmware we

can start to look for information that can help us iden-

tify some vulnerabilities and understand the device

behaviour. The information to look for is the follow-

ing (Gupta, 2017)(OWASP, a):

• Login Credentials - Usernames and passwords

left on the code;

• Backdoors - Most commonly Telnet or SSH ser-

vices;

• URL’s - Firmware or source code repositories,

unauthenticated cloud or API connectios, and

other;

• Criptographic Keys - Symmetric keys in the

source code or in a ﬁle;

• Cypher Algorithms - Information about encryp-

tion algorithms can help to decrypt communica-

tions;

• Authentication Mechanisms - Details about the

API, web application, or other authentication pro-

cesses.

To ease the process of going through all

the ﬁrmware ﬁles we recommend using a tool

called Firmwalker (https://github.com/craigz28/

ﬁrmwalker). This tool searches for speciﬁc words in

ﬁles (like ”password” or ”admin”), extensions, ﬁle

types and others. After running the tools it produces

a report of all the ﬁles that matched the search

criteria and even other information like IP and email

addresses.

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620

5.10 Mobile Application Vulnerabilities

Some mobile applications also expose sensitive infor-

mation that may help explore other vulnerabilities on

the ecosystem. Besides this, they can also have vul-

nerabilities of their own. This stage will only focus

Android applications.

In order to perform a full vulnerability analysis in

mobile applications there is a large amount of spe-

ciﬁc knowledge about the operating system and mo-

bile application development that is needed. But even

without that knowledge we can use tools to identify

vulnerabilities. This stage is divided into two steps

and offers a simple way to identify vulnerabilities.

5.10.1 Step 1 – Source Code Extraction

Many times, Android applications source code can be

extracted from the APK installation ﬁle. This ﬁle can

be obtained from one of the repositories mentioned

in Proc. 3 of stage 5.5. The ﬁles are then reverse

engineered into Java classes that can be analysed.

In order to extract the source code we recom-

mend using Jadx (https://github.com/skylot/jadx) or

QARK (https://github.com/linkedin/qark). The for-

mer can only be used to extract the source code, but

the latter includes other features covered in the next

step.

5.10.2 Step 2 – Source Code Analysis

The process of analysing the source code of mobile

applications has two purposes: (1) to better under-

stand the interaction of the different ecosystems com-

ponents and (2) to ﬁnd vulnerabilities within the mo-

bile application.

This process is very similar to the one used in step

2 of the previous stage. In fact we recommend run-

ning the tool Firmwalker in the source code of the

mobile application, since it may have the same kind

of information leakage vulnerability. Another tool we

recommend using is QARK since it runs a static code

analysis algorithms in order to ﬁnd vulnerabilities in

the mobile application.

5.11 Web Application Vulnerabilities

Web applications are still present in some IoT devices.

Their vulnerabilities can be identiﬁed with the same

tools used for common websites or non IoT web ap-

plications. IoT devices web applications exposed to

the internet are a serious threat to IoT security if they

are not properly secured.

Since the biggest threat to smart home IoT secu-

rity are automated attacks (see section 4.2), we can

use one of these automated scanning tools to identify

some of the web application vulnerabilities like the

popular OWASP ZAP (https://github.com/zaproxy/

zaproxy).

6 METHOD IMPLEMENTATION

RESULTS

In order to test the method we applied it to two de-

vices, a smart light bulb, the Yeelight Led Color (De-

vice 1) and, the DVR Dahua DHI-XVR5108H-4KL-

8P (Device 2). To the ﬁrst device we applied most

of the method stages excluding the ones related to

the device ﬁrmware analysis and web application (the

device does not have one). For the second one we

only applied the passive information gathering and

ﬁrmware vulnerability stages.

Device 1 has a bare-metal OS and the ﬁrmware

source ﬁles could not be extracted. Nevertheless we

were able to obtain the ﬁrmware image through the

active information gathering stage.

We also performed an attack to the device

using a python library (https://github.com/rytilahti/

python-miio identiﬁed in the passive information

gathering stage. This library allowed us to control the

device trough its WiFi conﬁguration network, with-

out any authentication. This attack explores two of

the vulnerabilities identiﬁed. The ﬁrst one is the lack

of authentication on the conﬁguration process, since

no password is requested for the WiFi conﬁguration

network. The second one is an unauthorised control

vulnerability, since the device exposed an authentica-

tion token that allows its control. The attack is limited

to a 30 minute time window, since after that period the

network is turned off.

We were also able to ﬁnd credentials in the mobile

application source code, that appear to be for a mesh

network authentication as well as some potential vul-

nerabilities in the mobile application reported by the

QARK tool.

On Device 2, we only had access to its ﬁrmware

(downloaded from the manufacturer website). This

device was used to test if the method could be ap-

plied without having physical access to the device.

We found critical information leakage vulnerabilities

in the device ﬁrmware. The device had default admin-

istrator login credentials in plain text on a conﬁgura-

tion ﬁle, possibly for the device web application. We

ran a search in the Shodan database and found more

then 14000 IP addresses that expose this device web

application to the internet.

Vulnerabilities in IoT Devices for Smart Home Environment

621

7 CONCLUSION AND FUTURE

WORK

We presented a practical method supported by open

source tools that can identify high risk vulnerabilities

present in smart home IoT devices. By following this

method manufacturers and advanced users can test if

their devices are vulnerable to the most common vul-

nerabilities that are being exploited in cyberattacks.

Applying this method we were able to identify vul-

nerabilities in two test devices. By exploring some

of those vulnerabilities, we were able to control one

of the devices without authentication. We were also

able to identify vulnerabilities without physical ac-

cess to a device. These vulnerabilities can potentially

be exploited through the internet since thousands of

devices were identiﬁed in the Shodan database.

For future work we would like to develop speciﬁc

tools that could help automate the trafﬁc analysis sec-

tion of the method. It would also be interesting to ap-

ply the method to different IoT ecosystems to further

test its applicability or even to introduce new stages

or steps in the proposed method.

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