Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection

Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the...

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Vydáno v:	Neural computing & applications Ročník 35; číslo 24; s. 17883 - 17898
Hlavní autoři:	Jagat, Rikhi Ram, Sisodia, Dilip Singh, Singh, Pradeep
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.08.2023 Springer Nature B.V
Témata:	Artificial Intelligence Automation Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Decision trees Deep learning Image Processing and Computer Vision Machine learning Multilayer perceptrons Multilayers Original Article Probability and Statistics in Computer Science Robots Scalping Semi-supervised learning Software Web portals Deep learning Deep feature extraction Stacked sparse autoencoder Semi-supervised learning Web robot detection Machine learning
ISSN:	0941-0643, 1433-3058
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Abstract	Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the accuracy and efficiency of existing methods depend highly on the labeled web log data, countless web requests are generated daily with the help of web robots. Exhaustive and accurate manual labeling of reconstructed sessions is time-consuming and challenging. Further, effective detection of web robots is more challenging with unlabeled or partially labeled data. To address the aforementioned issues, we reformulated web robot detection as a semi-supervised learning problem. In this paper, we propose a deep learning-based Semi-Supervised Stacked Sparse AutoEncoder (Web-S4AE) for web robot detection. The proposed model uses content-based features and features extracted from web access log data to effectively classify web robots. The experiments were conducted on publicly available web log data from a library and information portal to assess the performance of Web-S4AE. The Web-S4AE model was trained in two phases. The first phase; comprises training the model with unlabeled data to extract the hidden information, and in the second phase, the model is fine-tuned using labeled data. The results suggest that incorporating more unlabeled data can significantly improve the classifier's performance. The Web-S4AE model’s performance was also compared with other models such as the Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP).
AbstractList	Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the accuracy and efficiency of existing methods depend highly on the labeled web log data, countless web requests are generated daily with the help of web robots. Exhaustive and accurate manual labeling of reconstructed sessions is time-consuming and challenging. Further, effective detection of web robots is more challenging with unlabeled or partially labeled data. To address the aforementioned issues, we reformulated web robot detection as a semi-supervised learning problem. In this paper, we propose a deep learning-based Semi-Supervised Stacked Sparse AutoEncoder (Web-S4AE) for web robot detection. The proposed model uses content-based features and features extracted from web access log data to effectively classify web robots. The experiments were conducted on publicly available web log data from a library and information portal to assess the performance of Web-S4AE. The Web-S4AE model was trained in two phases. The first phase; comprises training the model with unlabeled data to extract the hidden information, and in the second phase, the model is fine-tuned using labeled data. The results suggest that incorporating more unlabeled data can significantly improve the classifier's performance. The Web-S4AE model’s performance was also compared with other models such as the Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP).
Author	Singh, Pradeep Sisodia, Dilip Singh Jagat, Rikhi Ram
Author_xml	– sequence: 1 givenname: Rikhi Ram orcidid: 0000-0002-5794-6660 surname: Jagat fullname: Jagat, Rikhi Ram email: rrjagat.phd2019.cse@nitrr.ac.in organization: Department of Computer Science and Engineering, National Institute of Technology Raipur – sequence: 2 givenname: Dilip Singh surname: Sisodia fullname: Sisodia, Dilip Singh organization: Department of Computer Science and Engineering, National Institute of Technology Raipur – sequence: 3 givenname: Pradeep surname: Singh fullname: Singh, Pradeep organization: Department of Computer Science and Engineering, National Institute of Technology Raipur
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Cites_doi	10.1007/s13748-016-0094-0 10.48550/arXiv.1903.01003 10.1007/978-3-030-04834-1_21 10.18517/ijaseit.7.3.1563 10.1007/978-3-319-89743-1_27 10.1109/HPCC/SmartCity/DSS.2018.00252 10.1016/j.comnet.2021.108742 10.3390/electronics10192347 10.1002/nem.2100 10.1080/02664763.2020.1864815 10.1016/j.ejor.2010.06.038 10.1177/1461348419830815 10.7551/mitpress/7503.003.0024 10.1109/ACCESS.2018.2858277 10.1007/s11042-022-14258-0 10.1109/TBME.2021.3110767 10.1007/s10100-018-0531-1 10.1007/s10115-013-0706-y 10.1038/s41598-019-55320-6 10.1109/ICCW.2018.8403759 10.1186/s42400-019-0023-1 10.1016/j.knosys.2020.105875 10.1177/0165551516673293 10.1109/CyberSecurity49315.2020.9138856 10.1016/j.asoc.2012.08.028 10.1038/381607a0 10.4236/jdaip.2015.31001 10.48550/arXiv.1306.6709 10.1016/j.inffus.2017.10.006 10.1007/978-981-16-9605-3_64 10.1016/j.rse.2022.112947 10.1007/s11042-018-6273-1 10.1007/978-3-642-35289-8_26 10.1109/ICOIN48656.2020.9016522 10.1007/s10489-020-01754-9 10.1145/3339252.3339267 10.5555/1756006.1756025 10.1080/02331934.2011.654343
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References	KangZFengCWanXStacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrumJ Low Freq Noise Vib Act Control20203923324510.1177/1461348419830815 SisodiaDSAugmented session similarity based framework for measuring web user concern from web server logsInt J Adv Sci Eng Inf Technol20177100710.18517/ijaseit.7.3.1563 Zhang B, Yu Y, Li J (2018) Network intrusion detection based on stacked sparse autoencoder and binary tree ensemble method. In: 2018 IEEE international conference on communications workshops (ICC Workshops). IEEE, pp 1–6 Iliou C, Kostoulas T, Tsikrika T, et al. (2019) Towards a framework for detecting advanced web bots. In: Proceedings of the 14th international conference on availability, reliability and security. ACM, New York, NY, USA, pp 1–10 KrawczykBLearning from imbalanced data: open challenges and future directionsProg Artif Intell2016522123210.1007/s13748-016-0094-0 Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp 437–478 TrigueroIGarcíaSHerreraFSelf-labeled techniques for semi-supervised learning: taxonomy, software and empirical studyKnowl Inf Syst20154224528410.1007/s10115-013-0706-y SagheerAKotbMUnsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problemsSci Rep201991903810.1038/s41598-019-55320-6 OnakOErenlerTSerinagaogluYA novel data-adaptive regression framework based on multivariate adaptive regression splines for electrocardiographic imagingIEEE Trans Biomed Eng20226996397410.1109/TBME.2021.3110767 Akrout I, Feriani A, Akrout M (2019) Hacking google reCAPTCHA v3 using reinforcement learning. ArXiv 1–5. https://doi.org/10.48550/arXiv.1903.01003 Chen H, He H, Starr A (2020) An overview of web robots detection techniques. In: 2020 international conference on cyber security and protection of digital services (Cyber Security). IEEE, pp 1–6 ErhanDCourvilleABengioYVincentPWhy does unsupervised pre-training help deep learning?J Mach Learn Res20109201208260062310.5555/1756006.17560251242.68219 Chu Z, Gianvecchio S, Wang H (2018) Bot or Human? A behavior-based online bot detection system. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing, pp 432–449 JagatRRSisodiaDSSinghPDISET: a distance based semi-supervised self-training for automated users’ agent activity detection from web access logMultimed Tools Appl202210.1007/s11042-022-14258-0 KuterSBolatKAkyurekZA Machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area productRemote Sens Environ202227211294710.1016/j.rse.2022.112947 MienyeIDSunYImproved heart disease prediction using particle swarm optimization based stacked sparse autoencoderElectronics202110234710.3390/electronics10192347 Bahi M, Batouche M (2018) Drug-target interaction prediction in drug repositioning based on deep semi-supervised learning. In: IFIP advances in information and communication technology. Springer International Publishing, pp 302–313 ReedSLeeHAnguelovDTraining deep neural networks on noisy labels with bootstrappingSci York20141010 StevanovicDVlajicNAnADetection of malicious and non-malicious website visitors using unsupervised neural network learningAppl Soft Comput20131369870810.1016/j.asoc.2012.08.028 SuchackaGIwańskiJIdentifying legitimate web users and bots with different traffic profiles-an information bottleneck approachKnowl-Based Syst202019710587510.1016/j.knosys.2020.105875 Arai T, Okabe Y, Matsumoto Y, Kawamura K (2020) Detection of Bots in CAPTCHA as a Cloud Service Utilizing Machine Learning. In: 2020 international conference on information networking (ICOIN). IEEE, pp 584–589 GnoumaMLadjailiaAEjbaliRZaiedMStacked sparse autoencoder and history of binary motion image for human activity recognitionMultimed Tools Appl2019782157217910.1007/s11042-018-6273-1 AouediOPiamratKBagadtheyDHandling partially labeled network data: a semi-supervised approach using stacked sparse autoencoderComput Netw202220710874210.1016/j.comnet.2021.108742 Martín A, Ashish A, Paul B, et al. (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Accessed 12 Mar 2022 ZhangQYangLTChenZLiPA survey on deep learning for big dataInf Fusion20184214615710.1016/j.inffus.2017.10.006 YanBHanGEffective feature extraction via stacked sparse autoencoder to improve intrusion detection systemIEEE Access20186412384124810.1109/ACCESS.2018.2858277 Parisotto S, Launaro A, Leone N, Schönlieb C-B (2020) Unsupervised clustering of roman pottery profiles from their SSAE representation. arXiv 1–18 OlshausenBAFieldDJEmergence of simple-cell receptive field properties by learning a sparse code for natural imagesNature199638160760910.1038/381607a0 SisodiaDSVermaSVyasOPAgglomerative approach for identification and elimination of web robots from web server logs to extract knowledge about actual visitorsJ Data Anal Inf Process20150311010.4236/jdaip.2015.31001 JagatRRSisodiaDSSinghPSumaVFernandoXDuK-LWangHSemi-supervised self-training approach for web robots activity detection in weblogEvolutionary computing and mobile sustainable networks2022SingaporeSpringer91192410.1007/978-981-16-9605-3_64 Imperva Bad Bot Report 2022. https://www.imperva.com/resources/reports/2022-imperva-bad-bot-report.pdf. Accessed 13 Jun 2022 Andrew N (2011) Sparse autoencoder. CS294A Lectture Notes 72:1–19 Bellet A, Habrard A, Sebban M (2013) A survey on metric learning for feature vectors and structured data. 1–59. https://doi.org/10.48550/arXiv.1306.6709 De SchepperTCameloMFamaeyJLatréSTraffic classification at the radio spectrum level using deep learning models trained with synthetic dataInt J Netw Manag20203012010.1002/nem.2100 NalcaciGÖzmenAWeberGWLong-term load forecasting: models based on MARS, ANN and LR methodsCent Eur J Oper Res20192710331049399593510.1007/s10100-018-0531-107100445 TaylanPYerlikaya-ÖzkurtFBilgiç UçakBWeberG-WA new outlier detection method based on convex optimization: application to diagnosis of Parkinson’s diseaseJ Appl Stat20214824212440433074810.1080/02664763.2020.186481507484662 WanSLiYSunKPathMarker: protecting web contents against inside crawlersCybersecurity20192910.1186/s42400-019-0023-1 Lagopoulos A, Tsoumakas G (2019) Web robot detection-Server Logs Cabri A, Suchacka G, Rovetta S, Masulli F (2018) Online web bot detection using a sequential classification approach. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on Smart City; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS). IEEE, pp 1536–1540 WeberG-WDefterliOAlparslan GökSZKropatEModeling, inference and optimization of regulatory networks based on time series dataEur J Oper Res2011211114277032610.1016/j.ejor.2010.06.0381221.93024 LagopoulosATsoumakasGContent-aware web robot detectionAppl Intell2020504017402810.1007/s10489-020-01754-9 Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems 19. The MIT Press, pp 153–160 WeberG-WÇavuşoğluZÖzmenAPredicting default probabilities in emerging markets by new conic generalized partial linear models and their optimizationOptimization201261443457290351410.1080/02331934.2011.6543431245.91080 SisodiaDSKhandalVSinghalRFast prediction of web user browsing behaviours using most interesting patternsJ Inf Sci201844749010.1177/0165551516673293 8668_CR15 T De Schepper (8668_CR12) 2020; 30 8668_CR36 8668_CR19 8668_CR18 A Sagheer (8668_CR13) 2019; 9 S Wan (8668_CR24) 2019; 2 G-W Weber (8668_CR39) 2011; 211 Q Zhang (8668_CR7) 2018; 42 8668_CR8 G Suchacka (8668_CR26) 2020; 197 RR Jagat (8668_CR32) 2022 O Onak (8668_CR41) 2022; 69 8668_CR30 8668_CR1 8668_CR3 G-W Weber (8668_CR37) 2012; 61 8668_CR2 8668_CR31 DS Sisodia (8668_CR33) 2017; 7 8668_CR4 G Nalcaci (8668_CR27) 2019; 27 8668_CR25 ID Mienye (8668_CR10) 2021; 10 A Lagopoulos (8668_CR22) 2020; 50 P Taylan (8668_CR42) 2021; 48 D Erhan (8668_CR14) 2010; 9 8668_CR28 M Gnouma (8668_CR9) 2019; 78 B Yan (8668_CR17) 2018; 6 S Kuter (8668_CR40) 2022; 272 DS Sisodia (8668_CR34) 2018; 44 O Aouedi (8668_CR11) 2022; 207 D Stevanovic (8668_CR23) 2013; 13 RR Jagat (8668_CR38) 2022 BA Olshausen (8668_CR29) 1996; 381 I Triguero (8668_CR6) 2015; 42 Z Kang (8668_CR16) 2020; 39 DS Sisodia (8668_CR21) 2015; 03 8668_CR44 S Reed (8668_CR5) 2014; 10 8668_CR20 B Krawczyk (8668_CR35) 2016; 5 8668_CR43
References_xml	– reference: Bahi M, Batouche M (2018) Drug-target interaction prediction in drug repositioning based on deep semi-supervised learning. In: IFIP advances in information and communication technology. Springer International Publishing, pp 302–313 – reference: Chu Z, Gianvecchio S, Wang H (2018) Bot or Human? A behavior-based online bot detection system. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing, pp 432–449 – reference: Arai T, Okabe Y, Matsumoto Y, Kawamura K (2020) Detection of Bots in CAPTCHA as a Cloud Service Utilizing Machine Learning. In: 2020 international conference on information networking (ICOIN). IEEE, pp 584–589 – reference: YanBHanGEffective feature extraction via stacked sparse autoencoder to improve intrusion detection systemIEEE Access20186412384124810.1109/ACCESS.2018.2858277 – reference: JagatRRSisodiaDSSinghPSumaVFernandoXDuK-LWangHSemi-supervised self-training approach for web robots activity detection in weblogEvolutionary computing and mobile sustainable networks2022SingaporeSpringer91192410.1007/978-981-16-9605-3_64 – reference: SagheerAKotbMUnsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problemsSci Rep201991903810.1038/s41598-019-55320-6 – reference: JagatRRSisodiaDSSinghPDISET: a distance based semi-supervised self-training for automated users’ agent activity detection from web access logMultimed Tools Appl202210.1007/s11042-022-14258-0 – reference: ZhangQYangLTChenZLiPA survey on deep learning for big dataInf Fusion20184214615710.1016/j.inffus.2017.10.006 – reference: Parisotto S, Launaro A, Leone N, Schönlieb C-B (2020) Unsupervised clustering of roman pottery profiles from their SSAE representation. arXiv 1–18 – reference: OlshausenBAFieldDJEmergence of simple-cell receptive field properties by learning a sparse code for natural imagesNature199638160760910.1038/381607a0 – reference: Zhang B, Yu Y, Li J (2018) Network intrusion detection based on stacked sparse autoencoder and binary tree ensemble method. In: 2018 IEEE international conference on communications workshops (ICC Workshops). IEEE, pp 1–6 – reference: OnakOErenlerTSerinagaogluYA novel data-adaptive regression framework based on multivariate adaptive regression splines for electrocardiographic imagingIEEE Trans Biomed Eng20226996397410.1109/TBME.2021.3110767 – reference: KuterSBolatKAkyurekZA Machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area productRemote Sens Environ202227211294710.1016/j.rse.2022.112947 – reference: Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp 437–478 – reference: SisodiaDSAugmented session similarity based framework for measuring web user concern from web server logsInt J Adv Sci Eng Inf Technol20177100710.18517/ijaseit.7.3.1563 – reference: ErhanDCourvilleABengioYVincentPWhy does unsupervised pre-training help deep learning?J Mach Learn Res20109201208260062310.5555/1756006.17560251242.68219 – reference: Andrew N (2011) Sparse autoencoder. CS294A Lectture Notes 72:1–19 – reference: SuchackaGIwańskiJIdentifying legitimate web users and bots with different traffic profiles-an information bottleneck approachKnowl-Based Syst202019710587510.1016/j.knosys.2020.105875 – reference: KangZFengCWanXStacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrumJ Low Freq Noise Vib Act Control20203923324510.1177/1461348419830815 – reference: WanSLiYSunKPathMarker: protecting web contents against inside crawlersCybersecurity20192910.1186/s42400-019-0023-1 – reference: SisodiaDSKhandalVSinghalRFast prediction of web user browsing behaviours using most interesting patternsJ Inf Sci201844749010.1177/0165551516673293 – reference: MienyeIDSunYImproved heart disease prediction using particle swarm optimization based stacked sparse autoencoderElectronics202110234710.3390/electronics10192347 – reference: WeberG-WÇavuşoğluZÖzmenAPredicting default probabilities in emerging markets by new conic generalized partial linear models and their optimizationOptimization201261443457290351410.1080/02331934.2011.6543431245.91080 – reference: Iliou C, Kostoulas T, Tsikrika T, et al. (2019) Towards a framework for detecting advanced web bots. In: Proceedings of the 14th international conference on availability, reliability and security. ACM, New York, NY, USA, pp 1–10 – reference: Imperva Bad Bot Report 2022. https://www.imperva.com/resources/reports/2022-imperva-bad-bot-report.pdf. Accessed 13 Jun 2022 – reference: Martín A, Ashish A, Paul B, et al. (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Accessed 12 Mar 2022 – reference: SisodiaDSVermaSVyasOPAgglomerative approach for identification and elimination of web robots from web server logs to extract knowledge about actual visitorsJ Data Anal Inf Process20150311010.4236/jdaip.2015.31001 – reference: Cabri A, Suchacka G, Rovetta S, Masulli F (2018) Online web bot detection using a sequential classification approach. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on Smart City; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS). IEEE, pp 1536–1540 – reference: Akrout I, Feriani A, Akrout M (2019) Hacking google reCAPTCHA v3 using reinforcement learning. ArXiv 1–5. https://doi.org/10.48550/arXiv.1903.01003 – reference: TrigueroIGarcíaSHerreraFSelf-labeled techniques for semi-supervised learning: taxonomy, software and empirical studyKnowl Inf Syst20154224528410.1007/s10115-013-0706-y – reference: AouediOPiamratKBagadtheyDHandling partially labeled network data: a semi-supervised approach using stacked sparse autoencoderComput Netw202220710874210.1016/j.comnet.2021.108742 – reference: TaylanPYerlikaya-ÖzkurtFBilgiç UçakBWeberG-WA new outlier detection method based on convex optimization: application to diagnosis of Parkinson’s diseaseJ Appl Stat20214824212440433074810.1080/02664763.2020.186481507484662 – reference: Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems 19. The MIT Press, pp 153–160 – reference: NalcaciGÖzmenAWeberGWLong-term load forecasting: models based on MARS, ANN and LR methodsCent Eur J Oper Res20192710331049399593510.1007/s10100-018-0531-107100445 – reference: WeberG-WDefterliOAlparslan GökSZKropatEModeling, inference and optimization of regulatory networks based on time series dataEur J Oper Res2011211114277032610.1016/j.ejor.2010.06.0381221.93024 – reference: GnoumaMLadjailiaAEjbaliRZaiedMStacked sparse autoencoder and history of binary motion image for human activity recognitionMultimed Tools Appl2019782157217910.1007/s11042-018-6273-1 – reference: StevanovicDVlajicNAnADetection of malicious and non-malicious website visitors using unsupervised neural network learningAppl Soft Comput20131369870810.1016/j.asoc.2012.08.028 – reference: ReedSLeeHAnguelovDTraining deep neural networks on noisy labels with bootstrappingSci York20141010 – reference: Chen H, He H, Starr A (2020) An overview of web robots detection techniques. In: 2020 international conference on cyber security and protection of digital services (Cyber Security). IEEE, pp 1–6 – reference: LagopoulosATsoumakasGContent-aware web robot detectionAppl Intell2020504017402810.1007/s10489-020-01754-9 – reference: KrawczykBLearning from imbalanced data: open challenges and future directionsProg Artif Intell2016522123210.1007/s13748-016-0094-0 – reference: Bellet A, Habrard A, Sebban M (2013) A survey on metric learning for feature vectors and structured data. 1–59. https://doi.org/10.48550/arXiv.1306.6709 – reference: De SchepperTCameloMFamaeyJLatréSTraffic classification at the radio spectrum level using deep learning models trained with synthetic dataInt J Netw Manag20203012010.1002/nem.2100 – reference: Lagopoulos A, Tsoumakas G (2019) Web robot detection-Server Logs – ident: 8668_CR18 – volume: 5 start-page: 221 year: 2016 ident: 8668_CR35 publication-title: Prog Artif Intell doi: 10.1007/s13748-016-0094-0 – ident: 8668_CR43 – ident: 8668_CR3 doi: 10.48550/arXiv.1903.01003 – ident: 8668_CR20 doi: 10.1007/978-3-030-04834-1_21 – volume: 7 start-page: 1007 year: 2017 ident: 8668_CR33 publication-title: Int J Adv Sci Eng Inf Technol doi: 10.18517/ijaseit.7.3.1563 – ident: 8668_CR15 doi: 10.1007/978-3-319-89743-1_27 – volume: 10 start-page: 10 year: 2014 ident: 8668_CR5 publication-title: Sci York – ident: 8668_CR25 doi: 10.1109/HPCC/SmartCity/DSS.2018.00252 – volume: 207 start-page: 108742 year: 2022 ident: 8668_CR11 publication-title: Comput Netw doi: 10.1016/j.comnet.2021.108742 – volume: 10 start-page: 2347 year: 2021 ident: 8668_CR10 publication-title: Electronics doi: 10.3390/electronics10192347 – volume: 30 start-page: 1 year: 2020 ident: 8668_CR12 publication-title: Int J Netw Manag doi: 10.1002/nem.2100 – volume: 48 start-page: 2421 year: 2021 ident: 8668_CR42 publication-title: J Appl Stat doi: 10.1080/02664763.2020.1864815 – volume: 211 start-page: 1 year: 2011 ident: 8668_CR39 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2010.06.038 – ident: 8668_CR30 – volume: 39 start-page: 233 year: 2020 ident: 8668_CR16 publication-title: J Low Freq Noise Vib Act Control doi: 10.1177/1461348419830815 – ident: 8668_CR36 doi: 10.7551/mitpress/7503.003.0024 – volume: 6 start-page: 41238 year: 2018 ident: 8668_CR17 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2858277 – year: 2022 ident: 8668_CR38 publication-title: Multimed Tools Appl doi: 10.1007/s11042-022-14258-0 – volume: 69 start-page: 963 year: 2022 ident: 8668_CR41 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2021.3110767 – volume: 27 start-page: 1033 year: 2019 ident: 8668_CR27 publication-title: Cent Eur J Oper Res doi: 10.1007/s10100-018-0531-1 – volume: 42 start-page: 245 year: 2015 ident: 8668_CR6 publication-title: Knowl Inf Syst doi: 10.1007/s10115-013-0706-y – volume: 9 start-page: 19038 year: 2019 ident: 8668_CR13 publication-title: Sci Rep doi: 10.1038/s41598-019-55320-6 – ident: 8668_CR8 doi: 10.1109/ICCW.2018.8403759 – volume: 2 start-page: 9 year: 2019 ident: 8668_CR24 publication-title: Cybersecurity doi: 10.1186/s42400-019-0023-1 – volume: 197 start-page: 105875 year: 2020 ident: 8668_CR26 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2020.105875 – volume: 44 start-page: 74 year: 2018 ident: 8668_CR34 publication-title: J Inf Sci doi: 10.1177/0165551516673293 – ident: 8668_CR4 doi: 10.1109/CyberSecurity49315.2020.9138856 – volume: 13 start-page: 698 year: 2013 ident: 8668_CR23 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2012.08.028 – volume: 381 start-page: 607 year: 1996 ident: 8668_CR29 publication-title: Nature doi: 10.1038/381607a0 – volume: 03 start-page: 1 year: 2015 ident: 8668_CR21 publication-title: J Data Anal Inf Process doi: 10.4236/jdaip.2015.31001 – ident: 8668_CR28 doi: 10.48550/arXiv.1306.6709 – volume: 42 start-page: 146 year: 2018 ident: 8668_CR7 publication-title: Inf Fusion doi: 10.1016/j.inffus.2017.10.006 – start-page: 911 volume-title: Evolutionary computing and mobile sustainable networks year: 2022 ident: 8668_CR32 doi: 10.1007/978-981-16-9605-3_64 – volume: 272 start-page: 112947 year: 2022 ident: 8668_CR40 publication-title: Remote Sens Environ doi: 10.1016/j.rse.2022.112947 – volume: 78 start-page: 2157 year: 2019 ident: 8668_CR9 publication-title: Multimed Tools Appl doi: 10.1007/s11042-018-6273-1 – ident: 8668_CR31 – ident: 8668_CR44 doi: 10.1007/978-3-642-35289-8_26 – ident: 8668_CR2 doi: 10.1109/ICOIN48656.2020.9016522 – volume: 50 start-page: 4017 year: 2020 ident: 8668_CR22 publication-title: Appl Intell doi: 10.1007/s10489-020-01754-9 – ident: 8668_CR1 – ident: 8668_CR19 doi: 10.1145/3339252.3339267 – volume: 9 start-page: 201 year: 2010 ident: 8668_CR14 publication-title: J Mach Learn Res doi: 10.5555/1756006.1756025 – volume: 61 start-page: 443 year: 2012 ident: 8668_CR37 publication-title: Optimization doi: 10.1080/02331934.2011.654343
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SubjectTerms	Artificial Intelligence Automation Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Decision trees Deep learning Image Processing and Computer Vision Machine learning Multilayer perceptrons Multilayers Original Article Probability and Statistics in Computer Science Robots Scalping Semi-supervised learning Software Web portals
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Title	Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection
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