Unsupervised Detection of Anomalous Behavior in Wireless Devices based on Auto-Encoders
A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can co...
Uložené v:
| Vydané v: | IEEE/IFIP Network Operations and Management Symposium s. 1 - 7 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.04.2020
|
| Predmet: | |
| ISSN: | 2374-9709 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can collect sensitive data from millions of users and send them to a third-party servers. In this paper, we propose a methodology that leverages the power consumption of wireless devices to build a model that makes them more robust to the presence of malicious apps. The method consists of two stages: (i) Feature Extraction where stacked Restricted Boltzmann Machine (RBM) AutoEncoders (AE) and Principal Component Analysis (PCA) are used to extract features vector based on AE's reconstruction errors. (ii) Classifier where One-Class Support Vector Machine is trained to perform the classification task. The validation of the methodology is performed on a real measurements dataset. The obtained results show a good potential and prove that AEs' reconstruction error can be used as a good discriminating feature. The obtained detection accuracy surpasses previously reported techniques, where it reaches up to ~ 98% in some scenarios. |
|---|---|
| AbstractList | A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can collect sensitive data from millions of users and send them to a third-party servers. In this paper, we propose a methodology that leverages the power consumption of wireless devices to build a model that makes them more robust to the presence of malicious apps. The method consists of two stages: (i) Feature Extraction where stacked Restricted Boltzmann Machine (RBM) AutoEncoders (AE) and Principal Component Analysis (PCA) are used to extract features vector based on AE's reconstruction errors. (ii) Classifier where One-Class Support Vector Machine is trained to perform the classification task. The validation of the methodology is performed on a real measurements dataset. The obtained results show a good potential and prove that AEs' reconstruction error can be used as a good discriminating feature. The obtained detection accuracy surpasses previously reported techniques, where it reaches up to ~ 98% in some scenarios. |
| Author | Naik, K. Naik, N. Goel, N. Albasir, A. Kozlowski, A. J. Al-tekreeti, M. Hu, Q. |
| Author_xml | – sequence: 1 givenname: A. surname: Albasir fullname: Albasir, A. organization: University of Waterloo,Waterloo – sequence: 2 givenname: Q. surname: Hu fullname: Hu, Q. organization: University of Waterloo,Waterloo – sequence: 3 givenname: M. surname: Al-tekreeti fullname: Al-tekreeti, M. organization: University of Waterloo,Waterloo – sequence: 4 givenname: K. surname: Naik fullname: Naik, K. organization: University of Waterloo,Waterloo – sequence: 5 givenname: N. surname: Naik fullname: Naik, N. organization: Defence School of CIS,Ministry of Defence,UK – sequence: 6 givenname: A. J. surname: Kozlowski fullname: Kozlowski, A. J. organization: Cistech Limited,Ottawa – sequence: 7 givenname: N. surname: Goel fullname: Goel, N. organization: Cistech Limited,Ottawa |
| BookMark | eNotUMtKAzEUjaJgW_sFguQHpt7kxkmyrLU-oNqFli5LJnMHI21SJtOCf--IXR04LzhnyC5iisTYrYCJEGDv3pdvH0prNBMJEia2JxXiGRtbbYSWRijbi-dsIFGrwmqwV2yY8zeA0oAwYOtVzIc9tceQqeaP1JHvQoo8NXwa085t0yHzB_pyx5BaHiJfh5a2lHPvPQZPmVfuL9lHpocuFfPoU01tvmaXjdtmGp9wxFZP88_ZS7FYPr_OposiSMCuIIOGSiuh0ZVT5LGuhZVGGYOlU_e-EZWqwPerFNQevDUNIWJppKgVlhZH7Oa_NxDRZt-GnWt_Nqcf8BdV8VQs |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/NOMS47738.2020.9110433 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) (UW System Shared) IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9781728149738 1728149738 |
| EISSN | 2374-9709 |
| EndPage | 7 |
| ExternalDocumentID | 9110433 |
| Genre | orig-research |
| GroupedDBID | 29I 6IE 6IH 6IK 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP M43 OCL RIE RIL RIO |
| ID | FETCH-LOGICAL-i203t-e838e6920f7ba4ec3dd192848836a45cf1b4b0c10440dc0c98fe3336821d43693 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000716920500156&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 03:02:00 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-e838e6920f7ba4ec3dd192848836a45cf1b4b0c10440dc0c98fe3336821d43693 |
| PageCount | 7 |
| ParticipantIDs | ieee_primary_9110433 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-April |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: 2020-April |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE/IFIP Network Operations and Management Symposium |
| PublicationTitleAbbrev | NOMS |
| PublicationYear | 2020 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0047030 |
| Score | 2.100315 |
| Snippet | A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Communication system security Denoising AutoEncoder Feature extraction Malware Malware Detection Power Consumption Information Power demand Support vector machines Wireless communication Wireless Devices Wireless sensor networks |
| Title | Unsupervised Detection of Anomalous Behavior in Wireless Devices based on Auto-Encoders |
| URI | https://ieeexplore.ieee.org/document/9110433 |
| WOSCitedRecordID | wos000716920500156&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwED61FQMsPFrEWx4YSZvEru2MCFoxQKkEFd2qxD5LlUpS5cHvx05DAYmFzYp8iuTz3Wcn33cHcM2UohjwoRcb6wamtY05lMZTyuJ5jCIOTe3pRzGZyPk8mrbgZquFQcSafIZ9N6z_5etMVe5T2cAGpqu31Ya2EHyj1frKuszt3EYBHPjRYPL89MKEoI69Ffr9xvJXC5UaQcb7_3v3AfS-pXhkugWZQ2hhegR7P6oIduFtlhbV2sV8gZrcY1mzq1KSGWIv9-_xyl7uSVMHMSfLlDjG68pmODu3zhPEQZkm1uS2KjNvlDqde170YDYevd49eE2_BG8Z-rT0UFKJPAp9I5KYoaJa2_ObtCFKecyGygQJS3wVuC7TWvkqkgYppVyGgWaUR_QYOmmW4gmQQGsutbTwbjHcKJHYnMmNMhLt-YSG6hS6bokW601JjEWzOmd_Pz6HXeeFDeHlAjplXuEl7KiPclnkV7UfPwFXSp8R |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mFNQXPzbx2zz4aLe2ydr0UXRj4lYHbri30SUXGMx29MO_36SrU8EX30LJUcjl7pe0v98dwC0TgqLjdaxIaTcwKXXMIVeWEBrPI_QjV5WeHvhhyKfTYFSDu40WBhFL8hm2zLD8ly8TUZhPZW0dmKbe1hZsm85ZlVrrK-8ys3crDbBjB-3wZfjKfJ8a_pZrtyrbX01USgzpHfzv7YfQ_BbjkdEGZo6ghvEx7P-oI9iAt0mcFSsT9RlK8oh5ya-KSaKIvt6_R0t9vSdVJcSULGJiOK9LneP03DJTEANmkmiT-yJPrG5slO5p1oRJrzt-6FtVxwRr4do0t5BTjl7g2sqfRwwFlVKf4LgOUupFrCOUM2dzWzimz7QUtgi4Qkqpx11HMuoF9ATqcRLjKRBHSo9LrgFeo7gS_lxnTU8JxVGfUKgrzqBhlmi2WhfFmFWrc_734xvY7Y-Hg9ngKXy-gD3jkTX95RLqeVrgFeyIj3yRpdelTz8B0X-iWg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE%2FIFIP+Network+Operations+and+Management+Symposium&rft.atitle=Unsupervised+Detection+of+Anomalous+Behavior+in+Wireless+Devices+based+on+Auto-Encoders&rft.au=Albasir%2C+A.&rft.au=Hu%2C+Q.&rft.au=Al-tekreeti%2C+M.&rft.au=Naik%2C+K.&rft.date=2020-04-01&rft.pub=IEEE&rft.eissn=2374-9709&rft.spage=1&rft.epage=7&rft_id=info:doi/10.1109%2FNOMS47738.2020.9110433&rft.externalDocID=9110433 |