Loading…
A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices
Sensors (e.g., light, gyroscope, accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to othe...
Saved in:
Published in: | arXiv.org 2019-10 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | |
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Sikder, Amit Kumar Aksu, Hidayet Uluagac, A Selcuk |
description | Sensors (e.g., light, gyroscope, accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead. |
format | article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2308723400</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2308723400</sourcerecordid><originalsourceid>FETCH-proquest_journals_23087234003</originalsourceid><addsrcrecordid>eNqNyrEOgjAUQNHGxESi_MNLnJvUFoTVoMTFCXZS8aGgtPpaxM-XwQ9wusM9MxZIpTY8jaRcsNC5Tgght4mMYxWw0w4yazx-PNejJoScdI-jpTs0lmCPHmvfmisUaJwlftYOL1DeCLV3YA0UvSY_uXdbo1uxeaMfDsNfl2ydH8rsyJ9kXwM6X3V2IDOtSiqRJlJFQqj_1BdL9j1D</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2308723400</pqid></control><display><type>article</type><title>A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices</title><source>Publicly Available Content Database</source><creator>Sikder, Amit Kumar ; Aksu, Hidayet ; Uluagac, A Selcuk</creator><creatorcontrib>Sikder, Amit Kumar ; Aksu, Hidayet ; Uluagac, A Selcuk</creatorcontrib><description>Sensors (e.g., light, gyroscope, accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Accelerometers ; Application programming interface ; Intrusion detection systems ; Machine learning ; Malware ; Management systems ; Markov chains ; Performance evaluation ; Sensors ; Smart sensors ; Smartphones ; Smartwatches</subject><ispartof>arXiv.org, 2019-10</ispartof><rights>2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2308723400?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25753,37012,44590</link.rule.ids></links><search><creatorcontrib>Sikder, Amit Kumar</creatorcontrib><creatorcontrib>Aksu, Hidayet</creatorcontrib><creatorcontrib>Uluagac, A Selcuk</creatorcontrib><title>A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices</title><title>arXiv.org</title><description>Sensors (e.g., light, gyroscope, accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.</description><subject>Accelerometers</subject><subject>Application programming interface</subject><subject>Intrusion detection systems</subject><subject>Machine learning</subject><subject>Malware</subject><subject>Management systems</subject><subject>Markov chains</subject><subject>Performance evaluation</subject><subject>Sensors</subject><subject>Smart sensors</subject><subject>Smartphones</subject><subject>Smartwatches</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNyrEOgjAUQNHGxESi_MNLnJvUFoTVoMTFCXZS8aGgtPpaxM-XwQ9wusM9MxZIpTY8jaRcsNC5Tgght4mMYxWw0w4yazx-PNejJoScdI-jpTs0lmCPHmvfmisUaJwlftYOL1DeCLV3YA0UvSY_uXdbo1uxeaMfDsNfl2ydH8rsyJ9kXwM6X3V2IDOtSiqRJlJFQqj_1BdL9j1D</recordid><startdate>20191022</startdate><enddate>20191022</enddate><creator>Sikder, Amit Kumar</creator><creator>Aksu, Hidayet</creator><creator>Uluagac, A Selcuk</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20191022</creationdate><title>A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices</title><author>Sikder, Amit Kumar ; Aksu, Hidayet ; Uluagac, A Selcuk</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_23087234003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accelerometers</topic><topic>Application programming interface</topic><topic>Intrusion detection systems</topic><topic>Machine learning</topic><topic>Malware</topic><topic>Management systems</topic><topic>Markov chains</topic><topic>Performance evaluation</topic><topic>Sensors</topic><topic>Smart sensors</topic><topic>Smartphones</topic><topic>Smartwatches</topic><toplevel>online_resources</toplevel><creatorcontrib>Sikder, Amit Kumar</creatorcontrib><creatorcontrib>Aksu, Hidayet</creatorcontrib><creatorcontrib>Uluagac, A Selcuk</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sikder, Amit Kumar</au><au>Aksu, Hidayet</au><au>Uluagac, A Selcuk</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices</atitle><jtitle>arXiv.org</jtitle><date>2019-10-22</date><risdate>2019</risdate><eissn>2331-8422</eissn><abstract>Sensors (e.g., light, gyroscope, accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2019-10 |
issn | 2331-8422 |
language | eng |
recordid | cdi_proquest_journals_2308723400 |
source | Publicly Available Content Database |
subjects | Accelerometers Application programming interface Intrusion detection systems Machine learning Malware Management systems Markov chains Performance evaluation Sensors Smart sensors Smartphones Smartwatches |
title | A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T06%3A02%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=A%20Context-aware%20Framework%20for%20Detecting%20Sensor-based%20Threats%20on%20Smart%20Devices&rft.jtitle=arXiv.org&rft.au=Sikder,%20Amit%20Kumar&rft.date=2019-10-22&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2308723400%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_23087234003%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2308723400&rft_id=info:pmid/&rfr_iscdi=true |