Secure Data Sharing and Analysis in Cloud-Based Energy Management Systems

Abstract

Abstract. Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies.

Retracted: A novel optimized Bat Extreme Learning intrusion detection system for smart Internet of Things networks

“…Traditional security system consists of static border barriers (e.g., firewalls and intrusion detection system [IDS]), universal utilization of end‐point resistances (e.g., against infection), and programming patches from sellers. Be that as it may, these systems cannot deal with IoT arrangements because of the heterogeneity in gadgets and of their utilization cases and gadget/seller requirements 4–7 . This implies that the customary methodologies may not scale the IoT gadget since these techniques can obstruct the outside assaults yet frequently neglect to keep the assaults from the inner gadgets or its applications 8–10 .…”

[Retracted] Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

“…Although the massive data is stored centrally, it is convenient for data analysis and processing, but the loss and damage of big data caused by improper security management will cause devastating disaster. Due to the development of new technology and new business, the infringement of privacy right is not limited to physical and compulsory invasion, but is derived in a subtler way through various data, and the data security and privacy risks caused by this will be more serious [ 18 ].…”

A Supervised Intrusion Detection System for Smart Home IoT Devices

“…The insufficient security measures and lack of dedicated anomaly detection systems for these heterogeneous networks make them vulnerable to a range of attacks such as data leakage, spoofing, disruption of service (DoS/DDoS), energy bleeding, insecure gateways, etc. These can lead to disastrous effects; causing damage to hardware, disrupting the system availability, causing system blackouts, and even physically harm individuals [5], [6]. Therefore, it is clear that the scale of impact of the attacks performed on IoT networks can vary significantly.…”