Researchers used electromagnetic signals to classify malware infecting IoT devices

guest · Jan 5, 2022

January 5, 2022

A team of academics (Duy-Phuc Pham, Damien Marion, Matthieu Mastio and Annelie Heuser) from the Research Institute of Computer Science and Random Systems (IRISA) have devised a new approach that analyzes electromagnetic field emanations from the Internet of Things (IoT) devices to detect highly evasive malware.

The team of experts presented their technique at the Annual Computer Security Applications Conference (ACSAC) that took place in December.
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The researchers proposed a novel approach of using side channel information to identify malware targeting IoT systems. The technique could allow analysts to determine malware type and identity, even when the malicious code is heavily obfuscated to prevent static or symbolic binary analysis.

“In this paper, we concentrate on the ElectroMagnetic (EM) field of an embedded device as a source for malware analysis, which offers several advantages. In fact, EM emanation that is measured from the device is practically undetectable by the malware. Therefore, malware evasion techniques cannot be straightforwardly applied unlike for dynamic software monitoring.” reads a research paper published by the experts.
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Experts pointed out that the approach does not require modifications on the target devices.

The team analyzed power side-channel signals using Convolution Neural Networks (CNN) to detect malicious activities on IoT devices.
The test conducted by the researchers demonstrated that they were able to predict three generic malware types (and one benign class) with an accuracy of 99.82%.
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Obfuscation Revealed: Leveraging Electromagnetic Signals for Obfuscated Malware Classification

The Internet of Things (IoT) is constituted of devices that are exponentially growing in number and in complexity. They use numerous customized firmware and hardware, without taking into consideration security issues, which make them a target for cybercriminals, especially malware authors.

We will present a novel approach of using side channel information to identify the kinds of threats that are targeting the device. Using our approach, a malware analyst is able to obtain precise knowledge about malware type and identity, even in the presence of obfuscation techniques which may prevent static or symbolic binary analysis.
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guest · Jan 10, 2022

Raspberry Pi can detect malware attack using electromagnetic waves with astonishing accuracy
January 10, 2022

A Raspberry Pi has been trained to detect malware that is actively engaging its victims. The Pi Malware Detection system relies on electromagnetic waves emanating from the potential victim's computer to detect malicious activity. Researchers claim the system achieved accuracy as high as 99.82% during testing.

A team of researchers from the Research Institute of Computer Science and Random Systems (IRISA) has jointly come up with a unique solution to sniff out malware. The Malware Detection System does not intercept any software or investigate data packets flowing in and out of any computer. Instead, the Raspberry Pi estimates, with a surprisingly high degree of accuracy, ongoing malware activity, by analyzing specific electromagnetic (EM) waves.
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To develop the malware detection system, researchers hooked up a Raspberry Pi to an oscilloscope (Picoscope 6407) and an H-field probe to detect EM field changes. According to Tom's Hardware, they trained the Single Board Computer (SBC), “with both safe and malicious data sets to help define the parameters of a potential threat”.
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Users do not need to install any software on potential targets. Hence, this system remains completely immune to the countermeasures many types of malware deploy to evade detection or cripple anti-malware systems.
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Researchers used electromagnetic signals to classify malware infecting IoT devices

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