A new method of hybrid time window embedding with transformer-based traffic data classification in IoT-networked environment
The Internet of Things (IoT) appliances often expose sensitive data, either directly or indirectly. They may, for instance, tell whether you are at home right now or what your long or short-term habits are. Therefore, it is crucial to protect such devices against adversaries and has in place an earl...
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| Published in | Pattern analysis and applications : PAA Vol. 24; no. 4; pp. 1441 - 1449 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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London
Springer London
01.11.2021
Springer Nature B.V |
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| Abstract | The Internet of Things (IoT) appliances often expose sensitive data, either directly or indirectly. They may, for instance, tell whether you are at home right now or what your long or short-term habits are. Therefore, it is crucial to protect such devices against adversaries and has in place an early warning system which indicates compromised devices in a quick and efficient manner. In this paper, we propose time window embedding solutions that efficiently process a massive amount of data and have a low-memory-footprint at the same time. On top of the proposed embedding vectors, we use the core anomaly detection unit. It is a classifier that is based on the transformer’s encoder component followed by a feed-forward neural network. We have compared the proposed method with other classical machine-learning algorithms. Therefore, in the paper, we formally evaluate various machine-learning schemes and discuss their effectiveness in the IoT-related context. Our proposal is supported by detailed experiments that have been conducted on the recently published Aposemat IoT-23 dataset. |
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| AbstractList | The Internet of Things (IoT) appliances often expose sensitive data, either directly or indirectly. They may, for instance, tell whether you are at home right now or what your long or short-term habits are. Therefore, it is crucial to protect such devices against adversaries and has in place an early warning system which indicates compromised devices in a quick and efficient manner. In this paper, we propose time window embedding solutions that efficiently process a massive amount of data and have a low-memory-footprint at the same time. On top of the proposed embedding vectors, we use the core anomaly detection unit. It is a classifier that is based on the transformer’s encoder component followed by a feed-forward neural network. We have compared the proposed method with other classical machine-learning algorithms. Therefore, in the paper, we formally evaluate various machine-learning schemes and discuss their effectiveness in the IoT-related context. Our proposal is supported by detailed experiments that have been conducted on the recently published Aposemat IoT-23 dataset. |
| Author | Pawlicki, Marek Choraś, Michał Kozik, Rafał |
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In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 877–881 – reference: ThanhCTZelinkaIA survey on artificial intelligence in malware as next-generation threatsMendel201925273410.13164/mendel.2019.2.027 – reference: XuCShenJDuXZhangFAn intrusion detection system using a deep neural network with gated recurrent unitsIEEE Access20186486974870710.1109/ACCESS.2018.2867564 – reference: Claise B (2004) Cisco systems netflow services export version 9. rfc 3954 (informational) – reference: Yeo M, Koo Y, Yoon Y, Hwang T, Ryu J, Song J, Park C (2018) Flow-based malware detection using convolutional neural network. In: 2018 International Conference on Information Networking (ICOIN), pp. 910–913. https://doi.org/10.1109/ICOIN.2018.8343255 – reference: Caviglione L, Choraś M, Corona I, Janicki A, Mazurczyk W, Pawlicki M, Wasielewska K (2020) Tight arms race: overview of current malware threats and trends in their detection. IEEE Access – reference: Tenable: Blink XT2 sync module multiple vulnerabilities (2019). https://www.tenable.com/security/research/tra-2019-51 – reference: Garcia S (2014) dentifying, modeling and detecting botnet behaviors in the network. Ph.D. thesis, Instituto Superior de Ingenier’ıa de Software Tandil Departamento de Computacio’n y Sistemas – reference: Andrysiak T, Saganowski Ł, Choraś M, Kozik R (2014) Network traffic prediction and anomaly detection based on arfima model. In: International Joint Conference SOCO’14-CISIS’14-ICEUTE’14, pp. 545–554. Springer – reference: F-Secure: the f-secure attack landscape report H1-2020 (2020). https://www.f-secure.com/content/dam/press/de/media-library/reports/F-Secure-attack-landscape-h12020.pdf – reference: PawlickaAJaroszewska-ChorasDChorasMPawlickiMGuidelines for stego/malware detection tools: achieving gdpr complianceIEEE Technol Soc Mag2020394607010.1109/MTS.2020.3031848 – reference: Choraś M, Pawlicki M (2020) Intrusion detection approach based on optimised artificial neural network. Neurocomputing – reference: Zhang H, Dai S, Li Y, Zhang W (2018) Real-time distributed-random-forest-based network intrusion detection system using apache spark. In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), pp. 1–7 – reference: Komisarek M, Choraś M, Kozik R, Pawlicki M (2020) Real-time stream processing tool for detecting suspicious network patterns using machine learning. In: Proceedings of the 15th International Conference on Availability, Reliability and Security, pp. 1–7 – reference: Liu X, Tang Z, Yang B (2019) Predicting network attacks with cnn by constructing images from netflow data. In: 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), pp. 61–66 – reference: Flanagan K, Fallon E, Awad A, Connolly P (2017) Self-configuring netflow anomaly detection using cluster density analysis. In: 2017 19th International Conference on Advanced Communication Technology (ICACT), pp. 421–427 – reference: Fu R, Zhang Z, Li L (2016) Using lstm and gru neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328 – reference: Hardegen C, Pfülb B, Rieger S, Gepperth A (2020) Predicting network flow characteristics using deep learning and real-world network traffic. IEEE Transactions on Network and Service Management pp. 1–1 – reference: YangCLiuJKristianiELiuMYouIPauGNetflow monitoring and cyberattack detection using deep learning with cephIEEE Access202087842785010.1109/ACCESS.2019.2963716 – reference: NaseerSSaleemYKhalidSBashirMKHanJIqbalMMHanKEnhanced network anomaly detection based on deep neural networksIEEE Access20186482314824610.1109/ACCESS.2018.2863036 – reference: BitDefender: Ring video doorbell pro under the scope (2019). https://www.bitdefender.com/files/News/CaseStudies/study/294/Bitdefender-WhitePaper-RDoor-CREA3949-en-EN-GenericUse.pdf – volume: 6 start-page: 48231 year: 2018 ident: 980_CR14 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2863036 – ident: 980_CR20 doi: 10.1109/ICOIN.2018.8343255 – volume: 6 start-page: 48697 year: 2018 ident: 980_CR18 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2867564 – ident: 980_CR1 doi: 10.1007/978-3-319-07995-0_54 – volume: 25 start-page: 27 year: 2019 ident: 980_CR17 publication-title: Mendel doi: 10.13164/mendel.2019.2.027 – volume: 39 start-page: 60 issue: 4 year: 2020 ident: 980_CR15 publication-title: IEEE Technol Soc Mag doi: 10.1109/MTS.2020.3031848 – ident: 980_CR7 – ident: 980_CR8 doi: 10.23919/ICACT.2017.7890124 – ident: 980_CR22 doi: 10.1109/PCCC.2018.8711068 – ident: 980_CR9 doi: 10.1109/YAC.2016.7804912 – volume: 8 start-page: 7842 year: 2020 ident: 980_CR19 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2963716 – ident: 980_CR11 doi: 10.23919/CNSM46954.2019.9012716 – ident: 980_CR6 doi: 10.17487/rfc3954 – ident: 980_CR16 – ident: 980_CR2 – ident: 980_CR10 – ident: 980_CR21 doi: 10.1109/NOMS.2018.8406212 – ident: 980_CR4 doi: 10.1109/TCSET49122.2020.235562 – ident: 980_CR3 doi: 10.1109/ACCESS.2020.3048319 – ident: 980_CR12 doi: 10.1145/3407023.3409189 – ident: 980_CR5 doi: 10.1016/j.neucom.2020.07.138 – ident: 980_CR13 doi: 10.1109/BigDataSecurity-HPSC-IDS.2019.00022 |
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| SubjectTerms | Algorithms Anomalies Coders Computer Science Early warning systems Embedding Internet of Things Machine learning Neural networks Original Article Pattern Recognition Traffic information Transformers Windows (intervals) |
| Title | A new method of hybrid time window embedding with transformer-based traffic data classification in IoT-networked environment |
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