COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things
The Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objec...
- Autores:
-
Nespoli, Pantaleone
Useche Pelaez, David
Díaz López, Daniel
Gómez Mármol, Felix
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2019
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1436
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1436
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479720/pdf/sensors-19-01492.pdf
- Palabra clave:
- Internet de las cosas
Aprendizaje automático (Inteligencia artificial)
Seguridad informática
Internet of Things
Sentinel for the IoT
Intrusion detection system
Smart home
Machine learning
Malware detection
Threat intelligence
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by/4.0/
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COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| title |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| spellingShingle |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things Internet de las cosas Aprendizaje automático (Inteligencia artificial) Seguridad informática Internet of Things Sentinel for the IoT Intrusion detection system Smart home Machine learning Malware detection Threat intelligence |
| title_short |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| title_full |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| title_fullStr |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| title_full_unstemmed |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| title_sort |
COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things |
| dc.creator.fl_str_mv |
Nespoli, Pantaleone Useche Pelaez, David Díaz López, Daniel Gómez Mármol, Felix |
| dc.contributor.author.none.fl_str_mv |
Nespoli, Pantaleone Useche Pelaez, David Díaz López, Daniel Gómez Mármol, Felix |
| dc.contributor.researchgroup.spa.fl_str_mv |
CTG-Informática |
| dc.subject.armarc.spa.fl_str_mv |
Internet de las cosas Aprendizaje automático (Inteligencia artificial) Seguridad informática |
| topic |
Internet de las cosas Aprendizaje automático (Inteligencia artificial) Seguridad informática Internet of Things Sentinel for the IoT Intrusion detection system Smart home Machine learning Malware detection Threat intelligence |
| dc.subject.proposal.eng.fl_str_mv |
Internet of Things Sentinel for the IoT Intrusion detection system Smart home Machine learning Malware detection Threat intelligence |
| description |
The Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objects are able to communicate autonomously through already existing network infrastructures, thus generating a more concrete integration between real world and computer-based systems. On the downside, the great benefit carried by the IoT paradigm in our life brings simultaneously severe security issues, since the information exchanged among the objects frequently remains unprotected from malicious attackers. The paper at hand proposes COSMOS (Collaborative, Seamless and Adaptive Sentinel for the Internet of Things), a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using multiple defensive rings, resulting in a more accurate and robust protection. Additionally, we discuss the current deployment of the sentinel on a commodity device (i.e., Raspberry Pi). Exhaustive experiments are conducted on the sentinel, demonstrating that it performs meticulously even in heavily stressing conditions. Each defensive layer is tested, reaching a remarkable performance, thus proving the applicability of COSMOS in a distributed and dynamic scenario such as IoT. With the aim of easing the enjoyment of the proposed entinel, we further developed a friendly and ease-to-use COSMOS App, so that end-users can manage sentinel(s) directly using their own devices (e.g., smartphone). |
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2019 |
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2019 |
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2021-05-18T22:39:28Z 2021-10-01T17:22:45Z |
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2021-05-18T22:39:28Z 2021-10-01T17:22:45Z |
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Artículo de revista |
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1424-8220, 2019 |
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https://repositorio.escuelaing.edu.co/handle/001/1436 |
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doi:10.3390/s19071492 |
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479720/pdf/sensors-19-01492.pdf |
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1424-8220, 2019 doi:10.3390/s19071492 |
| url |
https://repositorio.escuelaing.edu.co/handle/001/1436 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479720/pdf/sensors-19-01492.pdf |
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eng |
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eng |
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Volume 19, Number 1492, 2019 |
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29 |
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1492 |
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Sensors |
| dc.relation.references.spa.fl_str_mv |
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Available online: https://www.gartner. com/newsroom/id/3412017 (accessed on 11 August 2018). Charmonman, S.; Mongkhonvanit, P. Special consideration for Big Data in IoE or Internet of Everything. In Proceedings of the 13th International Conference on ICT and Knowledge Engineering (ICT Knowledge Engineering 2015), Bangkok, Thailand, 18–20 November 2015; pp. 147–150. Conti, M.; Dehghantanha, A.; Franke, K.; Watson, S. Internet of Things security and forensics: Challenges and opportunities. Future Gener. Comput. Syst. 2018, 78, 544–546. [CrossRef] Tweneboah-Koduah, S.; Skouby, K.E.; Tadayoni, R. Cyber Security Threats to IoT Applications and Service Domains. Wirel. Person. Commun. 2017, 95, 169–185. [CrossRef] Ling, Z.; Luo, J.; Xu, Y.; Gao, C.; Wu, K.; Fu, X. Security Vulnerabilities of Internet of Things: A Case Study of the Smart Plug System. IEEE Internet Things J. 2017, 4, 1899–1909. [CrossRef] Antonakakis, M.; April, T.; Bailey, M.; Bernhard, M.; Bursztein, E.; Cochran, J.; Durumeric, Z.; Halderman, J.A.; Invernizzi, L.; Kallitsis, M.; et al. Understanding the Mirai Botnet. In Proceedings of the 26th USENIX Conference on Security Symposium (SEC17), Vancouver, BC, Canada, 16–18 August 2017; pp. 1093–1110 Hwang, Y.H. IoT Security & Privacy: Threats and Challenges. In Proceedings of the 1st ACM Workshop on IoT Privacy, Trust, and Security (IoTPTS15), Singapore, 14 April 2015 Díaz López, D.; Blanco Uribe, M.; Santiago Cely, C.; Vega Torres, A.; Moreno Guataquira, N.; Morón Castro, S.; Nespoli, P.; Gómez Mármol, F. Shielding IoT against cyber-attacks: An event-based approach using SIEM. Wirel. Commun. Mob. Comput. 2018, 2018, 3029638. [CrossRef] Nespoli, P.; Zago, M.; Huertas Celdrán, A.; Gil Pérez, M.; Gómez Mármol, F.; García Clemente, F.J. A Dynamic Continuous Authentication Framework in IoT-Enabled Environments. In Proceedings of the Fifth International Conference on Internet of Things: Systems, Management and Security (IoTSMS 2018), Valencia, Spain, 15–18 October 2018; pp. 131–138. Lin, H.; Bergmann, N.W. IoT Privacy and Security Challenges for Smart Home Environments. Information 2016, 7, 44. [CrossRef] Kambourakis, G.; Gomez Marmol, F.; Wang, G. Security and Privacy in Wireless and Mobile Networks. Future Internet 2018, 10, 18. [CrossRef] Miettinen, M.; Marchal, S.; Hafeez, I.; Asokan, N.; Sadeghi, A.R.; Tarkoma, S. IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT. In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS17), Atlanta, GA, USA, 5–8 June 2017; pp. 2177–2184 Ning, H.; Hong, L.; Yang, L.T. Cyberentity Security in the Internet of Things. Computer 2013, 46, 46–53. [CrossRef] Sforzin, A.; Gómez Mármol, F.; Conti, M.; Bohli, J.M. RPiDS: Raspberry Pi IDS A Fruitful Intrusion Detection System for IoT. In Proceedings of the IEEE Conference on Advanced and Trusted Computing, Toulouse, France, 18–21 July 2016; pp. 440–448 Vasilomanolakis, E.; Karuppayah, S.; Mühlhäuser, M.; Fischer, M. Taxonomy and Survey of Collaborative Intrusion Detection. ACM Comput. Surv. 2015, 47, 1–33. [CrossRef] Useche Peláez, D.; Díaz López, D.; Nespoli, P.; Gómez Mármol, F. TRIS: A Three-Rings IoT Sentinel to protect against cyber-threats. In Proceedings of the Fifth International Conference on Internet of Things: Systems, Management and Security (IoTSMS 2018), Valencia, Spain, 15–18 October 2018; pp. 123–130. Nespoli, P.; Papamartzivanos, D.; Mármol, F.G.; Kambourakis, G. Optimal Countermeasures Selection Against Cyber Attacks: A Comprehensive Survey on Reaction Frameworks. IEEE Commun. Surv. Tutor. 2018, 20, 1361–1396. [CrossRef] Papamartzivanos, D.; Gómez Mármol, F.; Kambourakis, G. Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems. IEEE Access 2019, 7, 13546–13560. [CrossRef] Snort. Network Intrusion Detection and Prevention System. Available online: https://www.snort.org/ (accessed on 26 March 2019) Pathan, A.S.K. The State of the Art in Intrusion Prevention and Detection; Taylor & Francis: Milton Park, Abingdon, UK, 2014. Kismet. Wireless Sniffer and Network Intrusion Detection System. Available online: https://www. kismetwireless.net (accessed on 26 March 2019). OpenVAS. Open Vulnerability Assessment System. Available online: http://www.openvas.org (accessed on 26 March 2019). Varsalone, J.; McFadden, M. Defense against the Black Arts: How Hackers Do What They Do and How to Protect against It; Taylor & Francis: Milton Park, Abingdon, UK, 2011. YARA. The Pattern Matching Swiss Knife for Malware Researchers. Available online: http://yara. readthedocs.io (accessed on 26 March 2019). Latifi, S. Information Technology: New Generations: 13th International Conference on Information Technology; Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Switzerland, 2016. Weka. Data Mining with Open Source Machine Learning Software. Available online: https://cs.waikato.ac. nz/ml/weka (accessed on 26-03-2019). Kaluža, B. Instant Weka How-to; Packt Publishing: Birmingham, UK, 2013. Koodous. Collaborative Platform for Android Malware Research. Available online: https://koodous.com (accessed on 26 March 2019) APKMirror. Free APK Downloads. Available online: https://www.apkmirror.com/ (accessed on 26 March 2019) Arp, D.; Spreitzenbarth, M.; Huebner, M.; Gascon, H.; Rieck, K. Drebin: Effective and Explainable Detection of Android Malware in Your Pocket. In Proceedings of the 21th Annual Network and Distributed System Security Symposium (NDSS14), San Diego, CA, USA, 23–26 February 2014; pp. 23–26. VirusTotal. Free On-Line File Analyzer. Available online: https://www.virustotal.com (accessed on 26 March 2019). Ciampa, M. CompTIA Security+ Guide to Network Security Fundamentals; Cengage Learning: Boston, MA, USA, 2017. Radare. Portable Reversing Framework. Available online: https://rada.re/r (accessed on 26 March 2019). Dunham, K.; Hartman, S.; Quintans, M.; Morales, J.A.; Strazzere, T. Android Malware and Analysis; Information Security Books; CRC Press: Boca Raton, FL, USA, 2014. Drake, J.J.; Lanier, Z.; Mulliner, C.; Fora, P.O.; Ridley, S.A.; Wicherski, G. Android Hacker’s Handbook; EBL-Schweitzer; Wiley: Hoboken, NJ, USA, 2014. OSSIM. Alienvault Open-Source SIEM. Available online: https://www.alienvault.com/products/ossim (accessed on 26 March 2019). Savas, O.; Deng, J. Big Data Analytics in Cybersecurity; Data Analytics Applications; CRC Press: Boca Raton, FL, USA, 2017 Akula, M.; Mahajan, A. Security Automation with Ansible 2: Leverage Ansible 2 to Automate Complex Security Tasks Like Application Security, Network Security, and Malware Analysis; Packt Publishing: Birmingham, UK, 2017 Dash, S.K.; Suarez-Tangil, G.; Khan, S.; Tam, K.; Ahmadi, M.; Kinder, J.; Cavallaro, L. DroidScribe: Classifying Android Malware Based on Runtime Behavior. In Proceedings of the IEEE Security and Privacy Workshops (SPW16), San Jose, CA, USA, 22–26 May 2016; pp. 252–261. Nespoli, P. WISS: Wireless IDS for IoT with SIEM integration. Master’s Thesis, University of Naples Federico II, Naples, Italy, 2017 Heriyanto, T.; Allen, L.; Ali, S. Kali Linux: Assuring Security by Penetration Testing; Packt Publishing: Birmingham, UK, 2014. Aho, A.V.; Corasick, M.J. Efficient String Matching: An Aid to Bibliographic Search. Commun. ACM 1975, 18, 333–340. [CrossRef] Yara Rules. Yara Rules Official Repository. Available online: https://github.com/Yara-Rules (accessed on 26 March 2019). Ronen, R.; Radu, M.; Feuerstein, C.; Yom-Tov, E.; Ahmadi, M. Microsoft Malware Classification Challenge. arXiv 2018, arXiv:1802.10135. Offensive Computing. Free Malware Download. Available online: http://www.offensivecomputing.net/ (accessed on 26 March 2019) Virus Sign. Malware Research and Data Center. Available online: http://www.virussign.com/ (accessed on 26 March 2019). Zelter. Malware Sample Sources. Available online: https://zeltser.com/malware-sample-sources/ (accessed on 26 March 2019). Ning, H.; Liu, H. Cyber-Physical-Social Based Security Architecture for Future Internet of Things. Adv. Internet Things 2012, 2, 1–7. [CrossRef] Dorri, A.; Kanhere, S.; Jurdak, R. Blockchain in internet of things: Challenges and Solutions. arXiv 2016, arXiv:1608.05187. Tor Project. Anonymity online. Available online: https://www.torproject.org/ (accessed on 26 March 2019). Riahi, A.; Challal, Y.; Natalizio, E.; Chtourou, Z.; Bouabdallah, A. A Systemic Approach for IoT Security. In Proceedings of the IEEE International Conference on Distributed Computing in Sensor Systems, Cambridge, MA, USA, 21–23 May 2013; pp. 351–355. Babar, S.; Stango, A.; Prasad, N.; Sen, J.; Prasad, R. Proposed embedded security framework for Internet of Things (IoT). In Proceedings of the 2nd IEEE International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), Chennai, India, 28 February–3 March 2011; pp. 1–5 Rahman, A.F.A.; Daud, M.; Mohamad, M.Z. Securing Sensor to Cloud Ecosystem using Internet of Things (IoT) Security Framework. In Proceedings of the International Conference on Internet of things and Cloud Computing—ICC ’16, Cambridge, UK, 22–23 March 2016; pp. 1–5. Abie, H.; Balasingham, I. Risk-based Adaptive Security for Smart IoT in eHealth. In Proceedings of the 7th International Conference on Body Area Networks (BodyNets12), Oslo, Norway, 24–26 September 2012; pp. 269–275. Cheng, S.M.; Chen, P.Y.; Lin, C.C.; Hsiao, H.C. Traffic-Aware Patching for Cyber Security in Mobile IoT. IEEE Commun. Mag. 2017, 55, 29–35. [CrossRef] Roux, J.; Alata, E.; Auriol, G.; Nicomette, V.; Kaâniche, M. Toward an Intrusion Detection Approach for IoT based on Radio Communications Profiling. In Proceedings of the 13th European Dependable Computing Conference, Geneva, Switzerland, 4–8 September 2017; pp. 147–150. Hodo, E.; Bellekens, X.; Hamilton, A.; Dubouilh, P.L.; Iorkyase, E.; Tachtatzis, C.; Atkinson, R. Threat analysis of IoT networks using artificial neural network intrusion detection system. In Proceedings of the 2016 International Symposium on Networks, Computers and Communications (ISNCC16), Hammamet, Tunisia, 11–13 May 2016; pp. 1–6. Meidan, Y.; Bohadana, M.; Shabtai, A.; Ochoa, M.; Tippenhauer, N.O.; Guarnizo, J.D.; Elovici, Y. Detection of Unauthorized IoT Devices Using Machine Learning Techniques. arXiv 2017, arXiv:1709.04647. Hasan, M.A.M.; Nasser, M.; Ahmad, S.; Molla, K.I. Feature selection for intrusion detection using random forest. J. Inf. Secur. 2016, 7, 129. [CrossRef] Pa, Y.M.P.; Suzuki, S.; Yoshioka, K.; Matsumoto, T.; Kasama, T.; Rossow, C. IoTPOT: A Novel Honeypot for Revealing Current IoT Threats. J. Inf. Process. 2016, 24, 522–533. [CrossRef] Sivaraman, V.; Gharakheili, H.H.; Vishwanath, A.; Boreli, R.; Mehani, O. Network-level security and privacy control for smart-home IoT devices. In Proceedings of the IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob15), Abu Dhabi, UAE, 19–21 October 2015; pp. 163–167. |
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Nespoli, Pantaleoneca87776112d767308172d768ee634197600Useche Pelaez, Davidb8efef4ab4506991db1d8cf56d3255a2600Díaz López, Daniel981c11b6a7e1ecc09161caae0919b617600Gómez Mármol, Felix7e6beda1f22aea812be10b3ca0732e19600CTG-Informática2021-05-18T22:39:28Z2021-10-01T17:22:45Z2021-05-18T22:39:28Z2021-10-01T17:22:45Z20191424-8220, 2019https://repositorio.escuelaing.edu.co/handle/001/1436doi:10.3390/s19071492https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479720/pdf/sensors-19-01492.pdfThe Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objects are able to communicate autonomously through already existing network infrastructures, thus generating a more concrete integration between real world and computer-based systems. On the downside, the great benefit carried by the IoT paradigm in our life brings simultaneously severe security issues, since the information exchanged among the objects frequently remains unprotected from malicious attackers. The paper at hand proposes COSMOS (Collaborative, Seamless and Adaptive Sentinel for the Internet of Things), a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using multiple defensive rings, resulting in a more accurate and robust protection. Additionally, we discuss the current deployment of the sentinel on a commodity device (i.e., Raspberry Pi). Exhaustive experiments are conducted on the sentinel, demonstrating that it performs meticulously even in heavily stressing conditions. Each defensive layer is tested, reaching a remarkable performance, thus proving the applicability of COSMOS in a distributed and dynamic scenario such as IoT. With the aim of easing the enjoyment of the proposed entinel, we further developed a friendly and ease-to-use COSMOS App, so that end-users can manage sentinel(s) directly using their own devices (e.g., smartphone).El Internet de las cosas (IoT) se estableció durante la última década como una tecnología con potencialidades y aplicabilidad considerables. Su paradigma de todo lo conectado juntos penetraron en el mundo real, con dispositivos inteligentes ubicados en varios dispositivos diarios. Dichos objetos inteligentes pueden comunicarse de forma autónoma a través de una red ya existente. infraestructuras, generando así una integración más concreta entre el mundo real y el informático sistemas. En el lado negativo, el gran beneficio que trae el paradigma de IoT en nuestra vida trae simultáneamente graves problemas de seguridad, ya que la información intercambiada entre los objetos con frecuencia permanece desprotegido de atacantes malintencionados. El artículo que nos ocupa propone COSMOS (Colaborativo, Centinela adaptable y transparente para Internet de las cosas), un centinela novedoso para proteger entornos de amenazas cibernéticas. Nuestro centinela protege los dispositivos de IoT mediante múltiples anillos defensivos, resultando en una protección más precisa y robusta. Además, discutimos la implementación actual del centinela en un dispositivo básico (es decir, Raspberry Pi). Se realizan experimentos exhaustivos en el centinela, demostrando que funciona meticulosamente incluso en condiciones de mucho estrés. Cada capa defensiva es probada, alcanzando un desempeño notable, demostrando así la aplicabilidad de COSMOS en un escenario distribuido y dinámico como IoT. Con el objetivo de facilitar el disfrute del centinela propuesto, desarrollamos una aplicación COSMOS amigable y fácil de usar, para que los usuarios finales pueden administrar centinelas directamente utilizando sus propios dispositivos (por ejemplo, teléfonos inteligentes).Received: 18 February 2019; Accepted: 23 March 2019; Published: 27 March 2019Department of Information & Communication Engineering, University of Murcia, Calle Campus Universitario, 30100 Murcia, Spain; felixgm@um.es Department of System Engineering, Colombian School of Engineering Julio Garavito, AK 45 (Autonorte), Bogotá 205-59, Colombia; david.useche@mail.escuelaing.edu.co (D.U.P.); daniel.diaz@escuelaing.edu.co (D.D.L.)Correspondence: pantaleone.nespoli@um.es29 páginasapplication/pdfengc 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of ThingsArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85SuizaVolume 19, Number 1492, 2019291492119N/ASensorsWang, T.; Zhang, G.; Liu, A.; Bhuiyan, M.Z.A.; Jin, Q. A Secure IoT Service Architecture with an Efficient Balance Dynamics Based on Cloud and Edge Computing. IEEE Internet Things J. 2018. [CrossRef]Yu, W.; Liang, F.; He, X.; Hatcher, W.G.; Lu, C.; Lin, J.; Yang, X. 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In Proceedings of the IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob15), Abu Dhabi, UAE, 19–21 October 2015; pp. 163–167.Internet de las cosasAprendizaje automático (Inteligencia artificial)Seguridad informáticaInternet of ThingsSentinel for the IoTIntrusion detection systemSmart homeMachine learningMalware detectionThreat intelligenceLICENSElicense.txttext/plain1881https://repositorio.escuelaing.edu.co/bitstream/001/1436/1/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD51open accessORIGINALCOSMOS_ Collaborative, Seamless and Adaptive Sentinel for the Internet of Things.pdfapplication/pdf2677705https://repositorio.escuelaing.edu.co/bitstream/001/1436/2/COSMOS_%20Collaborative%2c%20Seamless%20and%20Adaptive%20Sentinel%20for%20the%20Internet%20of%20Things.pdf5b8b29a10df1e504f46574d876ea60c6MD52metadata only accessTEXTCOSMOS_ Collaborative, Seamless and Adaptive Sentinel for the Internet of Things.pdf.txtCOSMOS_ Collaborative, Seamless and Adaptive Sentinel for the Internet of Things.pdf.txtExtracted texttext/plain102497https://repositorio.escuelaing.edu.co/bitstream/001/1436/3/COSMOS_%20Collaborative%2c%20Seamless%20and%20Adaptive%20Sentinel%20for%20the%20Internet%20of%20Things.pdf.txtac65f5714ee595287a76f22c9b25c6b4MD53open accessTHUMBNAILCOSMOS_ Collaborative, Seamless and Adaptive Sentinel for the Internet of Things.pdf.jpgCOSMOS_ Collaborative, Seamless and Adaptive Sentinel for the Internet of Things.pdf.jpgGenerated Thumbnailimage/jpeg14975https://repositorio.escuelaing.edu.co/bitstream/001/1436/4/COSMOS_%20Collaborative%2c%20Seamless%20and%20Adaptive%20Sentinel%20for%20the%20Internet%20of%20Things.pdf.jpg52ab3eb6fb6a3a6aeafe9d0714a6fddaMD54open access001/1436oai:repositorio.escuelaing.edu.co:001/14362022-08-09 19:21:20.985metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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 |