HVAC: Evading Classifier-based Defenses in Hidden Voice Attacks

Wu, Yi; Xu, Xiangyu; Walker, Payton; Liu, Jian; Saxena, Nitesh; Chen, Yingying; Yu, Jiadi

doi:10.1145/3433210.3437523

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…As previous shown in Table 1 the comparison of the related studies and in Table 10 the comparison of the proposed model and the benchmark study. Wu et al [ 9 ] used the fusion-based technique for the hidden voice attacks. The study has achieved the success rate of 0.941.…”

Section: Further Discussionmentioning

confidence: 99%

“…As a result, some studies have been made on the safety measure for HVAC systems. Wu et al [ 9 ] focused on hidden voice attacks and how such security breaches can affect many organizations and users. They proposed a fusion-based method to combine normal and obfuscated samples, since a hybrid hidden voice attacks commands and effectively fools defensive classifiers.…”

Section: Review Of Literaturementioning

confidence: 99%

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A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

Khan

Aslam

AlShedayed

et al. 2022

Sensors

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The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0.

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Section: Further Discussionmentioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

Khan

Aslam

AlShedayed

et al. 2022

Sensors

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“…Voice-activated devices have become increasingly prevalent daily, with billions of digital voice assistants worldwide and a significant market share held by popular platforms such as Apple Siri, Google Assistant, and Amazon Alexa [1][2]. Researchers have made progress in developing defense mechanisms and detection techniques to counter ultrasonic attacks and other security risks associated with voiceactivated devices [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Continued vigilance and innovation are essential to ensuring user safety and privacy [14].…”

Section: Introductionmentioning

confidence: 99%

“…Surveys, analyses, and investigations into specific aspects of voice assistant security have also contributed significantly to the field [8,10,11,13,16,20,29,37,39,50,69,72,83]. These studies have provided comprehensive insights into the attack surface, vulnerabilities, and potential mitigations, allowing for a deeper understanding of the security challenges posed by voice-controlled systems.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the Near-Ultrasonic Covert Channels method utilizes softwaredefined radio techniques to create covert channels for transmitting data [69]. Similarly, the HVAC attack method evades classifier-based defenses in hidden voice attacks [13]. Ultrasonic attacks are not limited to voice-activated devices; they also target other systems, such as vehicular sensors [61][62]64] and autonomous vehicles [62].…”

Section: Introductionmentioning

confidence: 99%

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2023

IJNSA

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The practical implications of issuing inaudible voice commands. The research mapped each attack vector to a tactic or technique from the MITRE ATT&CK matrix, covering enterprise, mobile, and Industrial Control System (ICS) frameworks. The experiment involved generating and surveying fifty near-ultrasonic audios to assess the attacks' effectiveness. Unprocessed commands achieved a 100% success rate, while processed commands achieved an 86% acknowledgment rate and a 58% overall executed (successful) rate. The research systematically stimulated previously unaddressed attack surfaces, aiming for comprehensive detection and attack design. Each ATT&CK identifier was paired with a tested defensive method, providing attack and defense tactics. The research findings revealed that the attack method employed Single Upper Sideband Amplitude Modulation (SUSBAM) to generate near-ultrasonic audio from audible sources. By eliminating the lower sideband, the design achieved a 6 kHz minimum from 16-22 kHz while remaining inaudible after transformation. The research also investigated the one-to-many attack surface, exploring scenarios where a single device triggers multiple actions or devices. Furthermore, the study demonstrated the reversibility or demodulation of the inaudible signal, suggesting potential alerting methods and the possibility of embedding secret messages like audio steganography. A critical methodological advance included tapping into the postprocessed audio signal when the server demodulates the signal for comparison to both the audible and inaudible input signals to improve the actionable success rates.

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Exploring emergent syllables in end-to-end automatic speech recognizers through model explainability technique

Vitale,

Cutugno,

Origlia

et al. 2024

Neural Comput & Applic

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Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs’ internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems.

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HVAC: Evading Classifier-based Defenses in Hidden Voice Attacks

Cited by 3 publications

References 16 publications

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

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Exploring emergent syllables in end-to-end automatic speech recognizers through model explainability technique

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