Firmware Vulnerabilities Homology Detection Based on Clonal Selection Algorithm for IoT Devices

He, Daojing; Xiaohu, Yu; Li, Tinghui; Chan, Sammy; Guizani, Mohsen

doi:10.1109/jiot.2022.3152364

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Implementing robust firewalls can help to protect IIoT devices from external intrusions and network attacks [68][69][70]. Firmware and application security is another critical area; as bugs or vulnerabilities in the firmware or software can be exploited by attackers to compromise a device, it is essential that IIoT device manufacturers regularly release firmware and application updates in order to fix known vulnerabilities and protect devices from attacks [71][72][73]. Finally, security awareness and staff training are important aspects of ensuring that end users are aware of security threats and best practices for protecting their devices and data.…”

Section: Background On the Iiotmentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

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Section: Background On the Iiotmentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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show abstract

“…SAST is an invaluable way of assessing the security of IoT but not the only one needed as it is meant to be complementary (but necessary) to the more traditional blackbox techniques used. Black-box methods used for vulnerability detection in IoT systems include, among others, fuzzing, taint-analysis, symbolic execution, homology analysis, and penetration testing [29][30][31][32][33]. In the remainder of this section, we discuss some of the latest scientific work conducted on these methods.…”

Section: Assessing the Security Of Iot Softwarementioning

confidence: 99%

“…He et al [33] propose a homology detection method based on a clonal selection algorithm for detecting vulnerabilities in IoT firmwares. The proposed problem indicates that methods utilizing machine learning for detecting vulnerabilities in IoT firmwares need sample data of firmware vulnerabilities.…”

Section: Assessing the Security Of Iot Softwarementioning

confidence: 99%

A Software Vulnerability Management Framework for the Minimization of System Attack Surface and Risk

et al. 2023

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Current Internet of Things (IoT) systems comprise multiple software systems that are deployed to provide users with the required functionalities. System architects create system blueprints and draw specifications for the software artefacts that are needed; subsequently, either custom-made software is developed according to these specifications and/or ready-made COTS/open source software may be identified and customized to realize the overall system goals. All deployed software however may entail vulnerabilities, either due to insecure coding practices or owing to misconfigurations and unexpected interactions. Moreover, software artefacts may implement a much broader set of functionalities than may be strictly necessary for the system at hand, in order to serve a wider range of needs, and failure to appropriately configure the deployed software to include only the required modules results in the further increase of the system attack surface and the associated risk. In this paper, we present a software vulnerability management framework which facilitates (a) the configuration of software to include only the necessary features, (b) the execution of security-related tests and the compilation of platform-wide software vulnerability lists, and (c) the prioritization of vulnerability addressing, considering the impact of each vulnerability, the associated technical debt for its remediation, and the available security budget. The proposed framework can be used as an aid in IoT platform implementation by software architects, developers, and security experts.

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A survey on privacy and security issues in IoT-based environments: Technologies, protection measures and future directions

Sun,

Wan,

et al. 2025

Computers & Security

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Firmware Vulnerabilities Homology Detection Based on Clonal Selection Algorithm for IoT Devices

Cited by 9 publications

References 34 publications

Overview of AI-Models and Tools in Embedded IIoT Applications

Overview of AI-Models and Tools in Embedded IIoT Applications

A Software Vulnerability Management Framework for the Minimization of System Attack Surface and Risk

A survey on privacy and security issues in IoT-based environments: Technologies, protection measures and future directions

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