Detecting cognitive hacking in visual inspection with physiological measurements

Huang, Wenyan; Chen, Xiaoyu; Jin, Ran; Lau, Nathan

doi:10.1016/j.apergo.2019.103022

Cited by 6 publications

(3 citation statements)

References 82 publications

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“…Due to its stealthiness, subliminal priming can be a primary candidate for perception hacking. As shown in the experiment results [27], by subtly presenting words or images that are physically or semantically similar to the judgments preferred by the attackers, attackers can influence the accuracy or false alarm rate of the inspectors.…”

Section: Exploitable Cognitive Vulnerabilitymentioning

confidence: 77%

An Introduction of System-Scientific Approaches to Cognitive Security

Huang¹,

Zhu²

2023

Preprint

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Human cognitive capacities and the needs of human-centric solutions for "Industry 5.0" make humans an indispensable component in Cyber-Physical Systems (CPSs), referred to as Human-Cyber-Physical Systems (HCPSs), where AI-powered technologies are incorporated to assist and augment humans. The close integration between humans and technologies in Section 1.1 and cognitive attacks in Section 1.2.4 poses emerging security challenges, where attacks can exploit vulnerabilities of human cognitive processes, affect their behaviors, and ultimately damage the HCPS.Defending HCPSs against cognitive attacks requires a new security paradigm, which we refer to as "cognitive security" in Section 1.2.5. The vulnerabilities of human cognitive systems and the associated methods of exploitation distinguish cognitive security from "cognitive reliability" and give rise to a distinctive CIA triad, as shown in Sections 1.2.5.1 and 1.2.5.2, respectively. Section 1.2.5.3 introduces cognitive and technical defense methods that deter the kill chain of cognitive attacks and achieve cognitive security. System scientific perspectives in Section 1.3 offer a promising direction to address the new challenges of cognitive security by developing quantitative, modular, multi-scale, and transferable solutions. Fig. 1.1 illustrates the structure of Chapter 1.

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Section: Exploitable Cognitive Vulnerabilitymentioning

confidence: 77%

An Introduction of System-Scientific Approaches to Cognitive Security

Huang¹,

Zhu²

2023

Preprint

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“…Huang et al 38 focused on the detection of cognitive hacking (i.e. a cyberattack that seeks to manipulate the perception of humans by exploiting their psychological vulnerabilities) in visual quality inspections through physiological parameters.…”

Section: Affective Computing In Manufacturingmentioning

confidence: 99%

Applications of affective computing in human-robot interaction: State-of-art and challenges for manufacturing

Gervasi

Barravecchia

Mastrogiacomo

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, that is, capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g. facial expressions, body language, voice, or physiological signals) and to adapt its behavior accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g. healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector.

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“…A mental passphrase is difficult to steal because brain activity is unique for each individual and stable over time [9][10][11]. Some studies have shown that a user's mental passphrase is well-defended against attackers [12][13][14]. Moreover, as hardware technologies have advanced gradually, simplified EEG devices have been developed, such as Dry EEG [15], NeuroSky [16], and Emotiv EEG headsets [17].…”

Section: Introductionmentioning

confidence: 99%

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Bak

Jeong

2023

IEEE Access

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Human brain activities-electroencephalogram (EEG) signals-are likely to provide a secure biometric approach for user identification because they are more sensitive, secretive, and difficult to replicate. Many studies have recently focused on identifying and quantifying important frequency patterns in motor imagery (MI), recorded through EEG. However, there is still a lack of an optimal methodology for recognizing users with EEG-based MI. Therefore, we aimed to propose an EEG-MI methodology that utilizes optimized feature extraction methods and classifiers to improve user-aware accuracy. To accomplish this goal, we extracted four features related to MI and compared the accuracies for recognizing users using a support vector machine (SVM) and Gaussian Naïve Bayes (GNB). We then used the half-total error rate (HTER) to determine whether the results were reliable due to an imbalance problem caused by the differences in the data sizes. Thus, we used a common spatial pattern (CSP) to achieve the highest user identification accuracies of 98.97% and 97.47% using SVM and GNB, respectively. All user recognition accuracies are guaranteed by the HTERs, which are below 0.5. However, CSP has the disadvantage of decreasing accuracy on a small dataset scale. Therefore, we proposed and tested a statistical methodology for estimating a minimum dataset scale to ensure CSP performance. We confirm that the used dataset adequately guarantees CSP performance. This study makes a great contribution to the field of information security by presenting an EEG-MI methodology that improves the identification accuracy in human biometrics based on EEG-MI signals.

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Detecting cognitive hacking in visual inspection with physiological measurements

Cited by 6 publications

References 82 publications

An Introduction of System-Scientific Approaches to Cognitive Security

An Introduction of System-Scientific Approaches to Cognitive Security

Applications of affective computing in human-robot interaction: State-of-art and challenges for manufacturing

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

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