NDLI: S2A: secure smart household appliances

Please wait, while we are loading the content...

Secure and efficient proof of storage with deduplication

Stalking online: on user privacy in social networks

On practical specification and enforcement of obligations

Relationship-based access control: its expression and enforcement through hybrid logic

Risk-based security decisions under uncertainty

Privacy streamliner: a two-stage approach to improving algorithm efficiency

Efficient run-time solving of RBAC user authorization queries: pushing the envelope

An information theoretic privacy and utility measure for data sanitization mechanisms

Practical oblivious storage

Measuring query privacy in location-based services

Deriving implementation-level policies for usage control enforcement

Bounding trust in reputation systems with incomplete information

Quantitative access control with partially-observable Markov decision processes

Privacy analysis using ontologies

A model-theoretic approach to data anonymity and inference control

SWIPE: eager erasure of sensitive data in large scale systems software

SENTINEL: securing database from logic flaws in web applications

Revisiting link privacy in social networks

Discovering access-control misconfigurations: new approaches and evaluation methodologies

The privacy in the time of the internet: secrecy vs transparency

Role engineering: from theory to practice

S2A: secure smart household appliances

Towards end-to-end secure content storage and delivery with public cloud

Leakage-free redactable signatures

Comparison-based encryption for fine-grained access control in clouds

Cookie-based privacy issues on google services

Protecting health information on mobile devices

SecDS: a secure EPC discovery service system in EPCglobal network

Detecting repackaged smartphone applications in third-party android marketplaces

Refinement-based design of a group-centric secure information sharing model

Identifying native applications with high assurance

S2A: secure smart household appliances

Content Provider	ACM Digital Library
Author	Chen, Yuxin Luo, Bo
Abstract	Security protection is an integral component for smart homes; however, smart appliances security has received little attention in the research community. Household appliances become very vulnerable if we introduce smart functions without proper security protection. In particular, smart access functions enable users to operate devices remotely. Meanwhile, smart devices are are also designed to support residential demand response, i.e. postpone non-urgent tasks to non-peak hours. However, remote adversaries could utilize such functions to manipulate smart appliances' operations without physically touching them. Such interferences, if not properly handled, could damage the smart devices, disturb owners' life or even harm the households' physical security. In this paper, we present S2A, a security protection solution to be embedded in smart appliances. First, a SUP model is developed to quantify penalties from device security, usability and electricity price. We employ multi-criteria reinforcement learning to integrate the three factors to determine an optimal operation strategy. Next, to leverage the risk of forged control commands or pricing data, we present a realtime assessment mechanism based on Bayesian inference. Risk indices are further integrated into the SUP model to serve as weighting factors of corresponding decision criteria. Evaluation shows that S2A ensures appliances security while providing good usability and economical efficiency.
Starting Page	217
Ending Page	228
Page Count	12
File Format	PDF
ISBN	9781450310918
DOI	10.1145/2133601.2133628
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-02-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Machine learning for security Intrusion detection Smart household devices Smart grids
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

Securing virtual execution environments through machine learning-based intrusion detection

A secure and efficient scheme for machine-to-machine communications in smart grid

Can machine learning be secure?

Intrusion detection in advanced metering infrastructure based on consumption pattern

Host-Based Intrusion Detection for Advanced Mobile Devices

Outside the Closed World: On Using Machine Learning for Network Intrusion Detection

Hierarchical secure virtualization model for cloud

A Review of Rule Learning-Based Intrusion Detection Systems and Their Prospects in Smart Grids

S2A: secure smart household appliances

Similar Documents

Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

Securing virtual execution environments through machine learning-based intrusion detection

A secure and efficient scheme for machine-to-machine communications in smart grid

Can machine learning be secure?

Intrusion detection in advanced metering infrastructure based on consumption pattern

Host-Based Intrusion Detection for Advanced Mobile Devices

Outside the Closed World: On Using Machine Learning for Network Intrusion Detection

Hierarchical secure virtualization model for cloud

A Review of Rule Learning-Based Intrusion Detection Systems and Their Prospects in Smart Grids

S2A: secure smart household appliances