Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

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Journal of Data Analysis and Information Processing

ISSN Print: 2327-7211
ISSN Online: 2327-7203
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"Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors"
written by Dilip Singh Sisodia, Shrish Verma, Om Prakash Vyas,
published by Journal of Data Analysis and Information Processing, Vol.3 No.1, 2015

has been cited by the following article(s):

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CrossRef

[1]	MACHINE LEARNING-BASED DEFENCES AGAINST ADVANCED'SESSION-REPLAY'WEB BOTS
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[2]	FRS-SIFS: fuzzy rough set session identification and feature selection in web robot detection
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[3]	Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection
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[4]	Generating Human-Like Motion to Defeat Interaction-Based CAPTCHAs
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[5]	Analysis of the Effect of Feature Selection and Class Balancing Methods with Supervised Algorithms in Web Robot Detection Problem
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[6]	BotsBFUOD: Web Bot Detection using Biometric Features and Unsupervised Outlier Detection
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[7]	DISET: a distance based semi-supervised self-training for automated users' agent activity detection from web access log
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[8]	Web Bot Detection Evasion Using Deep Reinforcement Learning
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[9]	Efficient on-the-fly Web bot detection
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[10]	Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics
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[11]	Web Bot Detection Evasion Using Generative Adversarial Networks
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[12]	An Overview of Web Crawlers Detection Techniques
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[13]	Quantum inspired approach for early classification of time series.
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[14]	Modeling the behavior of web crawlers on a web resource
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[15]	Bot recognition in a web store: an approach based on unsupervised learning
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[16]	An Overview of Web Robots Detection Techniques
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[17]	Identifying legitimate Web users and bots with different traffic profiles–an Information Bottleneck approach
	2020

[18]	АЛГОРИТМ И МЕТОДИКА ОБНАРУЖЕНИЯ И ПРОТИВОДЕЙСТВИЯ ВЕБ-РОБОТАМ
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[19]	Performance Evaluation of Large Data Clustering Techniques on Web Robot Session Data
	Machine Intelligence and Signal Analysis, 2019

[20]	Towards a framework for detecting advanced Web bots
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[21]	Enhancing the security of patients' portals and websites by detecting malicious web crawlers using machine learning techniques
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[22]	Applying Machine Learning to Cyber Security
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[23]	Online Web Bot Detection Using a Sequential Classification Approach
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[24]	Categorization Performance of Unsupervised Learning Techniques for Web Robots Sessions
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[25]	The Impact of Data Re-Sampling on Learning Performance of Class Imbalanced Bankruptcy Prediction Models
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[26]	Comparative performance of interestingness measures to identify redundant and noninformative rules from web usage data
	2018

[27]	Web Access Patterns of Actual Human Visitors and Web Robots: A Correlated Examination
	Handbook of Research on Pattern Engineering System Development for Big Data Analytics, 2018

[28]	A Relative Performance of Dissimilarity Measures for Matching Relational Web Access Patterns Between User Sessions
	Handbook of Research on Pattern Engineering System Development for Big Data Analytics, 2018

[29]	Comparative Performance of Interestingness Measures to Identify Redundant and Non-informative Rules from Web Usage Data
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[30]	A Soft Computing Approach for Benign and Malicious Web Robot Detection
	Expert Systems with Applications, 2017

[31]	A Subtractive Relational Fuzzy C-Medoids Clustering Approach To Cluster Web User Sessions from Web Server Logs
	International Journal of Applied Engineering Research [IJAER], 2017

[32]	Detection of Web te vitors based on fuzzy rough sets
	Soft Computing, 2017

[33]	Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs
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[34]	Изучение поведения средств автоматизированного сбора информации с веб-ресурсов
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[35]	Web Robot Detection Using Fuzzy Rough Set Theory
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[36]	A Study of Different Web-Crawler Behaviour
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[37]	Augmented intuitive dissimilarity metric for clustering of Web user sessions
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[38]	Performance Evaluation of an Augmented Session Dissimilarity Matrix of Web User Sessions Using Relational Fuzzy C-means clustering
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[39]	A Conglomerate Relational Fuzzy Approach for Discovering Web User Session Clusters from Web Server Logs
	International Journal of Engineering and Technology (IJET), 2016

[40]	Quantitative Evaluation of Web user Session Dissimilarity measures using Medoids based Relational Fuzzy clustering
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[41]	Fast prediction of web user browsing behaviours using most interesting patterns
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[42]	Session identification techniques used in web usage mining: A systematic mapping of scholarly literature
	Online Information Review, 2016

[43]	A Discounted Fuzzy Relational Clustering of Web Users' Using Intuitive Augmented Sessions Dissimilarity Metric
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[44]	Detection of Web site visitors based on fuzzy rough sets
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[45]	Session identification techniques used in web usage mining
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[46]	Методы обнаружения автоматизированного сбора информации с веб-ресурсов
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[47]	Кибернетика и программирование

[1]	Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2 Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-3-031-18458-1_15

[2]	Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection Neural Computing and Applications, 2023 DOI:10.1007/s00521-023-08668-w

[3]	DISET: a distance based semi-supervised self-training for automated users’ agent activity detection from web access log Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-022-14258-0

[4]	Web Bot Detection Evasion Using Deep Reinforcement Learning Proceedings of the 17th International Conference on Availability, Reliability and Security, 2022 DOI:10.1145/3538969.3538994

[5]	Web Bot Detection Evasion Using Deep Reinforcement Learning Proceedings of the 17th International Conference on Availability, Reliability and Security, 2022 DOI:10.1145/3538969.3538994

[6]	Web Bot Detection Evasion Using Deep Reinforcement Learning Proceedings of the 17th International Conference on Availability, Reliability and Security, 2022 DOI:10.1145/3538969.3538994

[7]	Representing a method to identify and contrast with the fraud which is created by robots for developing websites’ traffic ranking Signal and Data Processing, 2022 DOI:10.52547/jsdp.18.4.69

[8]	Web Bot Detection Evasion Using Deep Reinforcement Learning Proceedings of the 17th International Conference on Availability, Reliability and Security, 2022 DOI:10.1145/3538969.3538994

[9]	DISET: a distance based semi-supervised self-training for automated users’ agent activity detection from web access log Multimedia Tools and Applications, 2022 DOI:10.1007/s11042-022-14258-0

[10]	Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics Digital Threats: Research and Practice, 2021 DOI:10.1145/3447815

[11]	Web Bot Detection Evasion Using Generative Adversarial Networks 2021 IEEE International Conference on Cyber Security and Resilience (CSR), 2021 DOI:10.1109/CSR51186.2021.9527915

[12]	Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics Digital Threats: Research and Practice, 2021 DOI:10.1145/3447815

[13]	Modeling the behavior of web crawlers on a web resource Journal of Physics: Conference Series, 2020 DOI:10.1088/1742-6596/1679/3/032043

[14]	An Overview of Web Robots Detection Techniques 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), 2020 DOI:10.1109/CyberSecurity49315.2020.9138856

[15]	Enhancing the security of patients’ portals and websites by detecting malicious web crawlers using machine learning techniques International Journal of Medical Informatics, 2019 DOI:10.1016/j.ijmedinf.2019.103976

[16]	Towards a framework for detecting advanced Web bots Proceedings of the 14th International Conference on Availability, Reliability and Security, 2019 DOI:10.1145/3339252.3339267

[17]	Towards a framework for detecting advanced Web bots Proceedings of the 14th International Conference on Availability, Reliability and Security, 2019 DOI:10.1145/3339252.3339267

[18]	Categorization Performance of Unsupervised Learning Techniques for Web Robots Sessions 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 2018 DOI:10.1109/ICIRCA.2018.8597200

[19]	Online Web Bot Detection Using a Sequential Classification Approach 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), 2018 DOI:10.1109/HPCC/SmartCity/DSS.2018.00252

[20]	Handbook of Research on Pattern Engineering System Development for Big Data Analytics Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2018 DOI:10.4018/978-1-5225-3870-7.ch012

[21]	Handbook of Research on Pattern Engineering System Development for Big Data Analytics Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2018 DOI:10.4018/978-1-5225-3870-7.ch010

[22]	Detection of Web site visitors based on fuzzy rough sets Soft Computing, 2017 DOI:10.1007/s00500-016-2476-4

[23]	A soft computing approach for benign and malicious web robot detection Expert Systems with Applications, 2017 DOI:10.1016/j.eswa.2017.06.004

[24]	A study of different web-crawler behaviour 2017 20th Conference of Open Innovations Association (FRUCT), 2017 DOI:10.23919/FRUCT.2017.8071322

[25]	A soft computing approach for benign and malicious web robot detection Expert Systems with Applications, 2017 DOI:10.1016/j.eswa.2017.06.004

[26]	A soft computing approach for benign and malicious web robot detection Expert Systems with Applications, 2017 DOI:10.1016/j.eswa.2017.06.004

[27]	A soft computing approach for benign and malicious web robot detection Expert Systems with Applications, 2017 DOI:10.1016/j.eswa.2017.06.004

[28]	Session identification techniques used in web usage mining Online Information Review, 2016 DOI:10.1108/OIR-08-2015-0274

[29]	Augmented intuitive dissimilarity metric for clustering of Web user sessions Journal of Information Science, 2016 DOI:10.1177/0165551516648259

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