HoMonit

Zhang, Wei; Meng, Yan; Liu, Yugeng; Zhang, Xiaokuan; Zhang, Yinqian; Zhu, Haojin

doi:10.1145/3243734.3243820

Cited by 152 publications

(40 citation statements)

References 26 publications

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“…Acar et al inferred user activities by creating traffic profiles and relying on traffic rate variations to detect device state changes [1]. Zhang et al extracted fingerprints for several event types in order to detect misbehaving smart home applications [41]. Trimananda et al were able to infer events related to Zigbee devices by identifying packet-level signatures from the Wi-Fi and Ethernet traffic that the smartphone and smart hub generated in order to communicate with cloud servers [35].…”

Section: Related Workmentioning

confidence: 99%

“…To add to the challenge, there is a lack of robust security analysis tools for Zigbee networks, commercial Zigbee devices use closed-source software, and certain specification documents have not been released to the public. Previous studies that incorporate centralized Zigbee networks largely demonstrate information leakage from encrypted traffic [1,17,35,41], such as the identification of triggered events, without exploring how an attacker may use this information to disrupt the operation of centralized Zigbee networks. While several command injection attacks have been demonstrated against centralized Zigbee networks [8,21,49], these attacks require knowledge of the network key.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Zigator

Akestoridis

Harishankar

Michael

et al. 2020

Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks

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As the popularity of Internet-connected devices for residential use increases, it is important to ensure that they meet appropriate security goals, given that they interact with the physical world through sensors and actuators. Zigbee is a wireless communication protocol that is commonly used in smart home environments, which builds on top of the IEEE 802.15.4 standard. In this work we present a security analysis tool, called Zigator, that enables in-depth study of Zigbee networks. In particular, we study the security consequences of the design choice to disable MAC-layer security in centralized Zigbee networks. We show that valuable information can be gained from passive inspection of Zigbee traffic, including the identification of certain encrypted NWK commands, which we then use to develop selective jamming and spoofing attacks. An attacker may launch these attacks in order to force the end user to factory reset targeted devices and eventually expose the network key. We validated our attacks by setting up a testbed, using open-source tools, that incorporates commercial Zigbee devices. Finally, we publicly release the software tools that we developed and the Zigbee packets that we captured, to contribute back to the research community. CCS CONCEPTS • Security and privacy → Mobile and wireless security; • Networks → Mobile and wireless security; Home networks; Mobile ad hoc networks; Sensor networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Zigator

Akestoridis

Harishankar

Michael

et al. 2020

Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks

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“…The purposed system used a microcontroller to provide the necessary data to the algorithm in order to detect the occurrence of fire and the sensors used in the purposed model include CO, temperature and smog sensor [30]. Muhammad Salihin et al presented a mechanism to detect fire using smoke sensors and also provided a monitoring system so that in case of a fire the system could provide the monitoring mechanism to display wirelessly to help firefighters to pinpoint the location of fire, helping in the rescue process [31,32].…”

Section: Related Workmentioning

confidence: 99%

Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

et al. 2021

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Fire monitoring systems have usually been based on a single sensor such as smoke or flame. These single sensor systems have been unable to distinguish between true and false presence of fire, such as a smoke from a cigarette which might cause the fire alarm to go off. Consuming energy all day long and being dependent on one sensor that might end with false alert is not efficient and environmentally friendly. We need a system that is efficient not only in sensing fire accurately, but we also need a solution which is smart. In order to improve upon the results of existing single sensor systems, our system uses a combination of three sensors to increase the efficiency. The result from the sensor is then analyzed by a specified rule-set using an AI-based fuzzy logic algorithm; defined in the purposed research, our system detects the presence of fire. Our system is designed to make smart decisions based on the situation; it provides feature updated alerts and hardware controls such as enabling a mechanism to start ventilation if the fire is causing suffocation, and also providing water support to minimize the damage. The purposed system keeps updating the management about the current severity of the environment by continually sensing any change in the environment during fire. The purposed system proved to provide accurate results in the entire 15 test performed around different intensities of a fire situation. The simulation work for the SMDD is done using MATLAB and the result of the experiments is satisfactory.

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“…Wireless traffic analysis systems and Intrusion Detection Systems have a long and rich history [2,4,6,11,12,16,23,25,26,29,33,35,39,48,55,58]. Generally speaking, wireless traffic analysis systems and Intrusion Detection Systems (IDS) continually monitor computers or networks, collecting data (e.g., system calls, network communication), extracting quantifiable features from this data, and applying a variety of techniques to analyze the data in search of signs of anomalies or compromise.…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

Recurring verification of interaction authenticity within bluetooth networks

Peters

Pierson

Sen

et al. 2021

Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks

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Although user authentication has been well explored, device-todevice authentication -specifically in Bluetooth networks -has not seen the same attention. We propose Verification of Interaction Authenticity (VIA) -a recurring authentication scheme based on evaluating characteristics of the communications (interactions) between devices. We adapt techniques from wireless traffic analysis and intrusion-detection systems to develop behavioral models that capture typical, authentic device interactions (behavior); these models enable recurring verification of device behavior. To evaluate our approach we produced a new dataset consisting of more than 300 Bluetooth network traces collected from 20 Bluetooth-enabled smart-health and smart-home devices. In our evaluation, we found that devices can be correctly verified at a variety of granularities, achieving an F1-score of 0.86 or better in most cases. CCS CONCEPTS• Security and privacy → Mobile and wireless security; Intrusion detection systems; Multi-factor authentication.

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HoMonit

Cited by 152 publications

References 26 publications

Zigator

Zigator

Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

Recurring verification of interaction authenticity within bluetooth networks

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