NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

Secure Data Provenance in Home Energy Monitoring Networks

Smart grid empowers home owners to efficiently manage their smart home appliances within a Home Area Network (HAN), by real time monitoring and fine-grained control. However, it offers the possibility for a malicious user to intrude into the HAN and deceive the smart metering system with fraudulent energy usage report. While most of the existing works have focused on how to prevent data tampering in HAN's communication channel, this paper looks into a relatively less studied security aspect namely data provenance. We propose a novel solution based on Shamir's secret sharing and threshold cryptography to guarantee that the reported energy usage is collected from the specific appliance as claimed at a particular location, and that it reflects the real consumption of the energy. A byproduct of the proposed security solution is a guarantee of data integrity. A prototype implementation is presented to demonstrate the feasibility and practicality of the proposed solution