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Application of learning algorithms in smart home IoT system security

  • *Corresponding author: Jian Mao

    *Corresponding author: Jian Mao 
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  • With the rapid development of Internet of Things (IoT) technologies, smart home systems are getting more and more popular in our daily life. Besides providing convenient functionality and tangible benefits, smart home systems also expose users to security risks. To enhance the functionality and the security, machine learning algorithms play an important role in a smart home ecosystem, e.g., ensuring biotechnology-based authentication and authorization, anomalous detection, etc. On the other side, attackers also treat learning algorithms as a tool, as well as a target, to exploit the security vulnerabilities in smart home systems. In this paper, we unify the system architectures suggested by the mainstream service providers, e.g., Samsung, Google, Apple, etc. Based on our proposed overall smart home system model, we investigate the application of learning algorithms in smart home IoT system security. Our study includes two angles. First, we discussed the functionality and security enhancing methods based on learning mechanisms; second, we described the security threats exposed by employing learning techniques. We also explored the potential solutions that may address the aforementioned security problems.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.


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  • Figure 1.  Unified Smart Home System Architecture

    Figure 2.  Learning-based Attack Vectors in Smart Home Systems

    Table 1.  A brief summary of learning application in smart home

    Layer Application Description Func. Sec. References
    Control Layer Image/Speech Recognition Identifying specific images to meet user requirements; Verifying users identities through face/voice [11], [28]
    Incident Recognition Using real-time data to predict sudden events on health issues [36], [60]
    Energy saving Predicting energy consumption;
    Managing energy utilisation
    [44], [50]
    User preference Providing home services for users based on predicted user preference [10], [39]
    Anomalous Detection Detecting abnormal behaviors;
    Defending DDoS attack;
    Device failure detection
    [7], [29]
    Processing Layer Malware Detection Detecting malicious software;
    Providing recommended solutions
    [23], [32], [40]
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    Table 2.  A taxonomy of learning-related attack in smart home

    Angles Description Attack Vectors References
    Exploiting vulnerabilities of Learning Automatic vehicle interference Tampering with the image transmitted to the automatic vehicle image recognition algorithm [37]
    Controlling voice control system Designing an ultrasound that contain voice control commands, but humans could not hear [55]
    Intrusion detection systems evasion Disguising traffic pattern of the malicious data [14]
    Using Learning-Based Techniques Attack cryptographic algorithm Learning-based analysis of power traces to find secret key information [20], [25]
    PUF attack Learning-based modeling methods; Combining side-channel information with machine learning modeling techniques [27], [31],
    [42], [43]
    Stealing information from cache Building cache pattern classifier to extract information [58]
    Recovering printed text Analyzing voice of printer via machine learning [5]
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