Improving Smart Home Security; Integrating Logical Sensing into Smart Home
Abstract:
The paper explains various security issues in the existing home automation systems and proposes the use of logic based security algorithms to improve home security. The work classifies natural access points to a home as primary and secondary access points depending on their use. Logic based sensing is implemented by identifying normal user behavior at these access points and requesting user verification when necessary. User position is also considered when various access points changed states. Moreover, the algorithm also verifies the legitimacy of a fire alarm by measuring the change in temperature, humidity and carbon monoxide levels, thus defending against manipulative attackers. The experiment conducted in this paper used a combination of sensors, microcontrollers, Raspberry Pi and ZigBee communication to identify user behavior at various access points and implement the logical sensing algorithm. In the experiment the proposed logical sensing algorithm was successfully implemented for a month in a studio apartment. During the course of the experiment the algorithm was able to detect all the state changes of the primary and secondary access points and also successfully verified user identity 55 times generating 14 warnings and 5 alarms.
Existing System:
Despite smart home security being critical there are some vulnerabilities in the existing systems [3] [4]. Over the years researchers demonstrated various security issues associated with the devices and technology used in modern smart homes. The wireless sensor networks deployed in modern smart homes for device to device communication is vulnerable to various Routing [5] and Wormhole attacks [6]. Popular communication technologies like ZigBee and 802.15.4 used in smart homes are susceptible to Replay attacks [7]. All these factors contributed to the rapid rise in home burglaries over the past decade [8] [9] and demonstrates the importance of Home Security in the modern world. Our previous works in smart home security [10] [11] explains the changing role of modern home security systems and defines the role of a modern home automation system as, one capable of identifying, alerting and preventing intrusion attempts in a home at the same time preserving evidence of the intrusion or attempted intrusion so that the perpetuator or perpetuators can be identified and prosecuted.
Proposed System:
In this paper, we analyzed various access points in a home to identify different improbable scenarios within a smart home during its operation. Access points are inherent in the structure of a home, which can be used for entering and exiting a home. In a typical home these natural access points are front door, back door, balcony doors and windows. Even though window is not a normal access point it can be used as one; most likely by an intruder depending on the situation. Physical access to a home is only possible through these access points unless serious structural alterations are made to a home. These serious structural alterations cannot be made without drawing In this paper, we analyzed various access points in a home to identify different improbable scenarios within a smart home during its operation. Access points are inherent in the structure of a home, which can be used for entering and exiting a home. In a typical home these natural access points are front door, back door, balcony doors and windows. Even though window is not a normal access point it can be used as one; most likely by an intruder depending on the situation. Physical access to a home is only possible through these access points unless serious structural alterations are made to a home. These serious structural alterations cannot be made without drawing home, the behavior of a legitimate user at these access points can be broken down in to a set of possible events which can be predicted.
Conclusion:
The paper detects user actions at primary and secondary access points in a home using different sensors. These detected user actions and behaviors are compared with normal user behavior at various access points to identify intrusions or intrusion attempts. In the experiment, our proposed algorithm was able to successfully identify all 305 state changes of the main access point and reduce them to 190 user behaviors while the secondary access point changed state 56 times. The alarm was triggered five times when the user failed to confirm his identity. Six of the fourteen warnings generated were regarding secondary access points while the other eight were relating to primary access point when the home became empty. In addition to identifying intrusions in home, the algorithm also warns user about imminent and live potential security vulnerabilities by identifying the status of various access points, user position and behaviors.
References:
[1] C. Suh and Y.-B. Ko, “Design and implementation of intelligent home control systems based on active sensor networks,” IEEE Transactions on Consumer Electronics, vol. 54, no. 3, pp. 1177–1184, 2008.
[2] B. Fouladi, S. Ghanoun, “Security Evaluation of the Z-Wave Wireless Protocol,” Black hat USA, Aug. 2013.
[3] Wenye Wang, Zhuo Lu, “Cyber security in the Smart Grid: Survey and challenges,” Computer Networks, Volume 57, Issue 5, Pages 1344-1371, April 2013.
[4] N. Komninos, E. Philippou and A. Pitsillides, “Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures,” in IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1933-1954, Fourthquarter 2014.
[5] C. Karlof, D. Wagner, “Secure routing in wireless sensor networks: attacks and countermeasures”, Ad Hoc Networks, vol. 1, pp. 293–315, 2003.
[6] Y. Hu, A. Perrig, D. Johnson, “Wormhole attacks in wireless networks”, IEEE Journal on Selected Areas in Communications, vol. 24, no. 2, pp. 370–380, Feb. 2006.
[7] Y. Mo and B. Sinopoli, “Secure control against replay attacks,” 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, pp. 911-918, 2009.
[8] D. Deadman, “Forecasting residential burglary,” International Journal of Forecasting, vol. 19, no. 4, pp. 567–578, 2003.
[9] UNODC, “International Burglary, Car Theft and Housebreaking Statistics,” United Nations Office on Drugs and Crime (UNODC), Technical Report, 2015.