Skip Abstract Section
Abstract
This article presents the design and implementation of a novel intrusion detection system, called EchoSensor, which leverages speakers and microphones in smart home devices to capture human gait patterns for individual identification. EchoSensor harnesses the speaker to send inaudible acoustic signals (around 20 kHz) and utilizes the microphone to capture the reflected signals. As the reflected signals have unique variations in the Doppler shift respective to the gaits of different people, EchoSensor is able to profile human gait patterns from the generated spectrograms. To mine the gait information, we first propose a two-stage interference cancellation scheme to remove the background noise and environmental interference, followed by a new method to detect the starting point of walking and estimate the gait cycle time. We then perform the fine-grained analysis of the spectrograms to extract a series of features. In the end, machine learning is employed to construct an identifier for individual recognition. We implement the EchoSensor system and deploy it under different household environments to conduct intrusion detection tasks. Extensive experimental results have demonstrated that EchoSensor can achieve the averaged Intruder Gait Detection Rate (IDR) and True Family Member Gait Detection Rate (TFR) of 92.7% and 91.9%, respectively.
- [1] . 2015. Acoustic gaits: Gait analysis with footstep sounds. IEEE Transactions on Biomedical Engineering 62, 8 (2015), 2001–2011.Google Scholar
Cross Ref
- [2] . 2011. On using gait in forensic biometrics. Journal of Forensic Sciences 56, 4 (2011), 882–889.Google ScholarCross Ref
- [3] . 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 27.Google ScholarDigital Library
- [4] . 2017. BreathPrint: Breathing acoustics-based user authentication. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 278–291.Google ScholarDigital Library
- [5] . 2010. Glottal Source and Vocal-tract Separation. Ph.D. Dissertation.Google Scholar
- [6] . 2007. A survey of biometric gait recognition: Approaches, security, and challenges. In Proceedings of the Annual Norwegian Computer Science Conference. Annual Norwegian Computer Science Conference Norway, 19–21.Google Scholar
- [7] . 2006. Biometric gait authentication using accelerometer sensor. JCP 1, 7 (2006), 51–59.Google Scholar
- [8] . 2015. A software-based sonar ranging sensor for smart phones. IEEE Internet of Things Journal 2, 6 (2015), 479–489.Google ScholarCross Ref
- [9] . 2012. Soundwave: Using the Doppler effect to sense gestures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1911–1914.Google ScholarDigital Library
- [10] . 2006. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 2 (2006), 316–322.Google ScholarDigital Library
- [11] . 2018. Parks Associates Predicts about 27 Percent of U.S. Households to have Security by 2021. Retrieved March 15, 2019 from http://www.securitysystemsnews.com/article/parks-associates-predicts-about-27-percent-us-households-have-security-2021Google Scholar
- [12] . 2007. Using ground reaction forces from gait analysis: Body mass as a weak biometric. In Proceedings of the International Conference on Pervasive Computing. Springer, 251–267.Google ScholarCross Ref
- [13] . 2007. Acoustic Doppler sonar for gait recogination. In Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE, 27–32.Google ScholarDigital Library
- [14] . 1977. An exponential moving-average sequence and point process (EMA1). Journal of Applied Probability 14, 1 (1977), 98–113.Google ScholarCross Ref
- [15] . 2018. Human gait recognition with micro-doppler radar and deep autoencoder. In Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR’18). IEEE, 3347–3352.Google ScholarCross Ref
- [16] . 2022. Experience: Practical problems for acoustic sensing. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 381–390.Google ScholarDigital Library
- [17] . 2016. Side-channel information leakage of encrypted video stream in video surveillance systems. In Proceedings of the IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 1–9.Google ScholarDigital Library
- [18] . 2020. Your privilege gives your privacy away: An analysis of a home security camera service. In Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 387–396.Google ScholarDigital Library
- [19] . 2018. Lippass: Lip reading-based user authentication on smartphones leveraging acoustic signals. In Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 1466–1474.Google ScholarDigital Library
- [20] . 2019. WiFi sensing with channel state information: A survey. ACM Computing Surveys 52, 3 (2019), 46.Google Scholar
- [21] . 2007. Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram. Computer Methods and Programs in Biomedicine 87, 1 (2007), 28–35.Google ScholarDigital Library
- [22] . 2012. Gait-based personal identification system using rotation sensor. Journal of Emerging Trends in Computing and Information Sciences 3, 2 (2012), 395–402.Google Scholar
- [23] . 2014. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14, 2 (2014), 3362–3394.Google ScholarCross Ref
- [24] . 2016. Fingerio: Using active sonar for fine-grained finger tracking. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 1515–1525.Google ScholarDigital Library
- [25] . 2017. Covertband: Activity information leakage using music. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 87.Google ScholarDigital Library
- [26] . 2014. The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognition 47, 1 (2014), 228–237.Google ScholarDigital Library
- [27] . 2010. College Physics: Reasoning and Relationships. Brooks/Cole.Google Scholar
- [28] . 2006. Automatic recognition by gait. Proceedings of the IEEE 94, 11 (2006), 2013–2024.Google ScholarCross Ref
- [29] . 1968. Homomorphic analysis of speech. IEEE Transactions on Audio and Electroacoustics 16, 2 (1968), 221–226.Google ScholarCross Ref
- [30] . 2000. The smart floor: A mechanism for natural user identification and tracking. In Proceedings of the CHI’00 Extended Abstracts on Human Factors in Computing Systems. ACM, 275–276.Google ScholarDigital Library
- [31] . 2015. Indoor person identification through footstep induced structural vibration. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications. ACM, 81–86.Google ScholarDigital Library
- [32] . 2013. Smartphone-based user verification leveraging gait recognition for mobile healthcare systems. In Proceedings of the 2013 IEEE International Conference on Sensing, Communications and Networking (SECON). IEEE, 149–157.Google ScholarCross Ref
- [33] . 2018. How Google Home and the Google Assistant Helped you Get More Done in 2017. Retrieved March 15, 2019 from https://www.blog.google/products/assistant/how-google-home-and-google-assistant-helped-you-get-more-done-in-2017Google Scholar
- [34] . 1998. Communication in the presence of noise. Proceedings of the IEEE 86, 2 (1998), 447–457.Google ScholarCross Ref
- [35] . 2018. Vskin: Sensing touch gestures on surfaces of mobile devices using acoustic signals. In Proceedings of the ACM Annual International Conference on Mobile Computing and Networking (MobiCom’18). 591–605.Google ScholarDigital Library
- [36] . 2009. PEM-ID: Identifying people by gait-matching using cameras and wearable accelerometers. In Proceedings of the 2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC). IEEE, 1–8.Google ScholarCross Ref
- [37] . 1997. Study compares older and younger pedestrian walking speeds. Road Management and Engineering Journal (1997). https://web.archive.org/web/20090703084118 http://www.usroads.com/journals/p/rej/9710/re971001.htm. Access date March 15, 2019.Google Scholar
- [38] . 2019. Privacy and security in Internet-connected cameras. In Proceedings of the 2019 IEEE International Congress on Internet of Things (ICIOT’19). IEEE, 173–180.Google ScholarCross Ref
- [39] . 2008. Feature-based human motion parameter estimation with radar. IET Radar, Sonar and Navigation 2, 2 (2008), 135–145.Google ScholarCross Ref
- [40] . 2002. Sonar for Practising Engineers. John Wiley and Sons.Google Scholar
- [41] . 2016. Gait recognition using wifi signals. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 363–373.Google ScholarDigital Library
- [42] . 2018. Gait-based human identification using acoustic sensor and deep neural network. Future Generation Computer Systems 86 (2018), 1228–1237.Google ScholarDigital Library
- [43] . 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2, 1-3 (1987), 37–52.Google ScholarCross Ref
- [44] . 2017. Device-free WiFi human sensing: From pattern-based to model-based approaches. IEEE Communications Magazine 55, 10 (2017), 91–97.Google ScholarCross Ref
- [45] . 2019. Acousticid: Gait-based human identification using acoustic signal. Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies 3, 3 (2019), 1–25.Google ScholarDigital Library
- [46] . 2016. WiWho: Wifi-based person identification in smart spaces. In Proceedings of the 15th International Conference on Information Processing in Sensor Networks. IEEE.Google ScholarCross Ref
- [47] . 2016. Wifi-id: Human identification using wifi signal. In Proceedings of the 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 75–82.Google ScholarCross Ref
- [48] . 2017. Hearing your voice is not enough: An articulatory gesture-based liveness detection for voice authentication. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 57–71.Google ScholarDigital Library
- [49] . 2015. Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Transactions on Cybernetics 45, 9 (2015), 1864–1875.Google ScholarCross Ref
- [50] . 2007. Acoustic micro-Doppler radar for human gait imaging. The Journal of the Acoustical Society of America 121, 3 (2007), EL110–EL113.Google ScholarCross Ref
- [51] . 2019. mid: Tracking and identifying people with millimeter wave radar. In Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS’19). IEEE, 33–40.Google ScholarCross Ref
Index Terms
- EchoSensor: Fine-grained Ultrasonic Sensing for Smart Home Intrusion Detection
Recommendations
Rule generalisation in intrusion detection systems using SNORT
Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks. An IDS's responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this ...
Network Intrusion Detection: Automated and Manual Methods Prone to Attack and Evasion
In this article, the authors describe common intrusion detection techniques, NIDS evasion methods, and how NIDSs detect intrusions. Additionally, we introduce new evasion methods, present test results for confirming attack outcomes based on server ...
Syntax vs. semantics: competing approaches to dynamic network intrusion detection
Malicious network traffic, including widespread worm activity, is a growing threat to internet-connected networks and hosts. In this paper, we consider both syntax and semantics based approaches for dynamic network intrusion detection. The semantics-...
Comments