A Comprehensive Survey of Attacks without Physical Access Targeting Hardware Vulnerabilities in IoT/IIoT Devices, and Their Detection Mechanisms

PolychronouNikolaos-Foivos,; ThevenonPierre-Henri,; PuysMaxime,; BeroulleVincent,

doi:10.1145/3471936

Cited by 19 publications

(9 citation statements)

References 71 publications

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“…Hardware attacks such as VGAs are among the most important modern attacks on IoT/IIoT devices [ 20 ]. Features such as the predicted behavior of the device can be changed, or even secret information such as encryption keys can be changed intercepted [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

“…Software attacks targeting hardware vulnerabilities was a term used by Polychronou et al [ 20 ] to describe a specific class of malicious attack vectors targeting IoT/IIoT devices (SATHV). These techniques are aimed at both the hardware flaws in system microarchitecture and the side-channel leakages they cause in the system, and they do not require physical access to the device.…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Jiang

2022

Computational Intelligence and Neuroscience

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A majority of modern IoT/IIoT digital systems rely on cryptographic implementations to provide satisfactory levels of security. Hardware attacks such as side-channel analysis attacks or fault injection attacks can significantly degrade and even eliminate the desired level of security of the infrastructure in question. One of the most dangerous attacks of this type is voltage glitch attacks (VGAs), which can change the intended behavior of a system. By effectively manipulating the voltage at a specific time, an error can be injected that can change the intentional conduct and bypass system security features or even extract confidential information such as encryption keys by analyzing incorrect outputs of the firmware. This study proposes an innovative VGAs detection system based on advanced machine learning. Specifically, an innovative semisupervised learning methodology is used that utilizes a hybrid combination of algorithms. Specifically, a heuristic clustering method is used based on a linear fragmentation of group classes. In contrast, the ELM methodology is used as an algorithm for retrieving hidden variables through convex optimization.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Jiang

2022

Computational Intelligence and Neuroscience

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“…They also proposed a standard hardware testing platform for IoT systems and devices to be used during the hardware design and system software development. Polychronou et al [20] reviewed the various fault injection attack vectors that do not require physical contact with the hardware components of an IoT device. The review presented novel attack methods based on the hardware vulnerabilities at the microarchitectural level in IoT devices.…”

Section: Related Workmentioning

confidence: 99%

“…The literature surveys presented in [1,19,20,25] analyzed the various hardware attack vectors to identify existing hardware vulnerabilities in components such as the microcontroller, clock generator, and voltage supply in the IoT system or device. The purpose of these surveys was to inform IoT hardware designers of the threats posed by fault injection attacks.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Fault Injection Attacks on IoT Systems

2022

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The field of the Internet of Things (IoT) is growing at a breakneck pace and its applications are becoming increasingly sophisticated with time. Fault injection attacks on IoT systems are aimed at altering software behavior by introducing faults into the hardware devices of the system. Attackers introduce glitches into hardware components, such as the clock generator, microcontroller, and voltage source, which can affect software functioning, causing it to misbehave. The methods proposed in the literature to handle fault injection attacks on IoT systems vary from hardware-based attack detection using system-level properties to analyzing the IoT software for vulnerabilities against fault injection attacks. This paper provides a systematic review of the various techniques proposed in the literature to counter fault injection attacks at both the system level and the software level to identify their limitations and propose solutions to address them. Hybrid attack detection methods at the software level are proposed to enhance the security of IoT systems against fault injection attacks. Solutions to the identified limitations are suggested using machine learning, dynamic code instrumentation tools, hardware emulation platforms, and concepts from the software testing domain. Future research possibilities, such as the use of software fault injection tools and supervised machine learning for attack detection at the software level, are investigated.

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“…IoT applications have expanded world-wide, offering the users many services to engage in their personal lives to make it easier. Even though IoT applications are very powerful and give high connectivity to the users and their data over regular apps and systems, they are still considered vulnerable to a wide diversity of attacks and threats [1]. These vulnerabilities are caused by the architecture type of the IoT ecosystem that includes heterogeneous layers of communication.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

et al. 2022

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The fast expansion of the Internet of Things (IoT) in the technology and communication industries necessitates a continuously updated cyber-security mechanism to keep protecting the systems' users from any possible attack that might target their data and privacy. Botnets pose a severe risk to the IoT, they use malicious nodes in order to compromise other nodes inside the network to launch several types of attacks causing service disruption. Examples of these attacks are Denial of Service (DoS), Distributed Denial of Service (DDoS), Service Scan, and OS Fingerprint. DoS and DDoS attacks are the most severe attacks in IoT launched from Botnets. Where the Botnet commands previously compromised single or multiple nodes in the network to launch network traffic towards a specific node or service. This leads to computational, power, or network bandwidth draining, which causes specific services to shutdown or behave unexpectedly. In this paper, we aim to verify the detection approach reliability when it encounters an attack that it was not trained on before. Therefore, we evaluate the performance of Convolutional Neural Networks (CNN) classifier in order to detect the malicious attack traffic especially the attacks that never reported before in the network i.e. Zero-Day attacks. Different regularization techniques i.e. L1 and L2 have been used to address the problem of overfitting and to control the complexity of the classifier. The experimental results show that using the regularization methods gives a higher performance in all the evaluation metrics compared to the standard CNN model. In addition, the enhanced CNN technique improves the capability of IDSs in detection of unseen intrusion events.

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A Comprehensive Survey of Attacks without Physical Access Targeting Hardware Vulnerabilities in IoT/IIoT Devices, and Their Detection Mechanisms

Cited by 19 publications

References 71 publications

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

A Systematic Review of Fault Injection Attacks on IoT Systems

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

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