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SIoTFuzzer: Fuzzing Web Interface in IoT Firmware via Stateful Message Generation

Authors :
Hangwei Zhang
Kai Lu
Xu Zhou
Qidi Yin
Pengfei Wang
Tai Yue
Source :
Applied Sciences, Vol 11, Iss 7, p 3120 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Cyber attacks against the web management interface of Internet of Things (IoT) devices often have serious consequences. Current research uses fuzzing technologies to test the web interfaces of IoT devices. These IoT fuzzers generate messages (a test case sent from the client to the server to test its functionality) without considering their dependency, which is unlikely to bypass the early check of the server. These invalid test cases significantly reduce the efficiency of fuzzing. To overcome this problem, we propose a stateful message generation (SMG) mechanism for IoT web fuzzing. SMG addresses two problems in IoT fuzzing. First, we retrieve the message dependency by using web front-end analysis and status analysis. These dependent messages, which can easily bypass the server check, are used as a valid seed. Second, we adopt a multi-message seed format to preserve the dependency of the messages when mutating the seed to get a valid test case, so that the test case can bypass the state check of the server to make a valid test. Message dependency preservation is implemented by our proposed parameter mutation and structural mutation methods. We implement SMG in our IoT fuzzer, SIoTFuzzer, which applies IoT firmware on the latest Linux-based simulation tool, FirmAE. We test nine IoT devices including a router and an IP camera and adopt a vulnerability detection mechanism. Our evaluation results show that (1) SIoTFuzzer is capable of finding real-world vulnerabilities in IoT devices; (2) our SMG is effective as it enables Boofuzz (a popular protocol fuzzer) to find command injection and cross-site scripting (XSS) vulnerabilities; and (3) compared to FirmFuzz, SIoTFuzzer found all the vulnerabilities in our benchmarks, while FirmFuzz found only four—the efficiency of our tool increased by 20.57% on average.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.71f499c52642a6b78786671e89c1b7
Document Type :
article
Full Text :
https://doi.org/10.3390/app11073120