Detecting Vulnerability on IoT Device Firmware: A Survey

Feng, Xiaotao; Zhu, Xiaogang; Han, Qing‐Long; Wei, Zhou; Wen, Sheng; Xiang, Yang

doi:10.1109/jas.2022.105860

Cited by 47 publications

(10 citation statements)

References 69 publications

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“…Neshenko et al addressed IoT vulnerabilities that are constantly evolving and explained a comprehensive approach in order to categorize state-of-the-art surveys in [12]. In addition, the works in [13,14] focused only on detecting IoT firmware vulnerability, whereas [15] described the common IoT communication protocols and how they implemented specific security mechanisms to make a comparison of the considered IoT technologies. In contrast, a paper described the IoT vulnerabilities and reviewed state-of-the-art articles but without seeking deeper into ML and DL techniques that are used to improve IoT security [16].…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security

Hulayyil,

Li,

2023

Electronics

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Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in IoT environments, and a review of recent research trends is presented.

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Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security

Hulayyil,

Li,

2023

Electronics

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“…The main challenges of discovering vulnerabilities in IoT smart devices include the lack of computing resources of IoT devices, the diversity of device types and protocols, the difficulty in obtaining and emulating firmware, and the limited feedback from devices [7,8]. Fuzzing is a software vulnerability discovery method widely used in academic and industrial fields [22].…”

Section: Iot Smart Device Fuzzingmentioning

confidence: 99%

“…Fuzzing has become one of the most successful techniques for finding software security vulnerabilities [7]. Fuzzing generates many test cases, repeatedly tests the target software, and monitors the abnormal conditions of the program [7,8]. Security vulnerabilities in IoT smart devices usually exist in device firmware [9].…”

Section: Introductionmentioning

confidence: 99%

“…The benchmark programs for evaluating these fuzzers are software in a general IT environment, and the evaluated fuzzers are general fuzzers under coverage guidance. However, in the IoT scenario, the benchmark program should be the firmware images of the IoT smart devices, and the architectures of these are pretty different (such as x86, MIPS, and ARM) [8]. At the same time, these firmware images usually provide various network communication services [22].…”

Section: Introductionmentioning

confidence: 99%

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IoTFuzzBench: A Pragmatic Benchmarking Framework for Evaluating IoT Black-Box Protocol Fuzzers

et al. 2023

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High scalability and low operating cost make black-box protocol fuzzing a vital tool for discovering vulnerabilities in the firmware of IoT smart devices. However, it is still challenging to compare black-box protocol fuzzers due to the lack of unified benchmark firmware images, complete fuzzing mutation seeds, comprehensive performance metrics, and a standardized evaluation framework. In this paper, we design and implement IoTFuzzBench, a scalable, modular, metric-driven automation framework for evaluating black-box protocol fuzzers for IoT smart devices comprehensively and quantitatively. Specifically, IoTFuzzBench has so far included 14 real-world benchmark firmware images, 30 verified real-world benchmark vulnerabilities, complete fuzzing seeds for each vulnerability, 7 popular fuzzers, and 5 categories of complementary performance metrics. We deployed IoTFuzzBench and evaluated 7 popular black-box protocol fuzzers on all benchmark firmware images and benchmark vulnerabilities. The experimental results show that IoTFuzzBench can not only provide fast, reliable, and reproducible experiments, but also effectively evaluate the ability of each fuzzer to find vulnerabilities and the differential performance on different performance metrics. The fuzzers found a total of 13 vulnerabilities out of 30. None of these fuzzers can outperform the others on all metrics. This result demonstrates the importance of comprehensive metrics. We hope our findings ease the burden of fuzzing evaluation in IoT scenarios, advancing more pragmatic and reproducible fuzzer benchmarking efforts.

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“… Ebbers (2022) analyzed firmware upgrades on IoT devices using data mining and mapping techniques. Feng et al (2023) examined challenges and solutions for firmware security analysis in IoT devices. Hassija et al (2019) discussed security challenges and emerging technologies to enhance trust levels in IoT applications.…”

Section: Related Workmentioning

confidence: 99%

Framework to perform taint analysis and security assessment of IoT devices in smart cities

Bhardwaj,

Vishnoi,

Bharany

et al. 2023

PeerJ Computer Science

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The Internet of Things has a bootloader and applications responsible for initializing the device’s hardware and loading the operating system or firmware. Ensuring the security of the bootloader is crucial to protect against malicious firmware or software being loaded onto the device. One way to increase the security of the bootloader is to use digital signature verification to ensure that only authorized firmware can be loaded onto the device. Additionally, implementing secure boot processes, such as a chain of trust, can prevent unauthorized access to the device’s firmware and protect against tampering during the boot process. This research is based on the firmware bootloader and application dataflow taint analysis and security assessment of IoT devices as the most critical step in ensuring the security and integrity of these devices. This process helps identify vulnerabilities and potential attack vectors that attackers could exploit and provides a foundation for developing effective remediation strategies.

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Detecting Vulnerability on IoT Device Firmware: A Survey

Cited by 47 publications

References 69 publications

Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security

Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security

IoTFuzzBench: A Pragmatic Benchmarking Framework for Evaluating IoT Black-Box Protocol Fuzzers

Framework to perform taint analysis and security assessment of IoT devices in smart cities

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