Pattern discovery and users classification through web usage mining

Bhargav, Anshul; Bhargav, Munish

doi:10.1109/iccicct.2014.6993038

Cited by 14 publications

(13 citation statements)

References 2 publications

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“…To solve the aforementioned problems, current research focuses on the use of machine learning based detection techniques to distinguish Web bots from humans, rather than solely relying on rulebased detection techniques. The first step in generating machine learning models that can be used for the detection of Web bots is the extraction of sessions from Web logs [5,18,22,24,25]. After that, several features are extracted from each session and used to identify whether the visitors are bots or humans.…”

Section: Background and Related Workmentioning

confidence: 99%

“…HTML, text, JavaScript, image, css, etc.) [13,22], the access patterns [3] and the HTTP errors produced [5,27]. These features are used as input to generate machine learning models.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In both cases, a ground truth of human and Web bot sessions is required. In most recent research, the annotation process relies on comparing each visitor's agent name [26] and IP address [5,7,13,22,25] with the agent names and IPs of known web bots according to lists hosted on external servers. Such lists mostly contain identifiers for bots which are benign in nature, like, for example, search engine bots, although some malicious bots can be found there as well.…”

Section: Background and Related Workmentioning

confidence: 99%

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Towards a framework for detecting advanced Web bots

Iliou

Κωστούλας

Tsikrika

et al. 2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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Automated programs (bots) are responsible for a large percentage of website traffic. These bots can either be used for benign purposes, such as Web indexing, Website monitoring (validation of hyperlinks and HTML code), feed fetching Web content and data extraction for commercial use or for malicious ones, including, but not limited to, content scraping, vulnerability scanning, account takeover, distributed denial of service attacks, marketing fraud, carding and spam. To ensure their security, Web servers try to identify bot sessions and apply special rules to them, such as throttling their requests or delivering different content. The methods currently used for the identification of bots are based either purely on rule-based bot detection techniques or a combination of rulebased and machine learning techniques. While current research has developed highly adequate methods for Web bot detection, these methods' adequacy when faced with Web bots that try to remain undetected hasn't been studied. For this reason, we created and evaluated a Web bot detection framework on its ability to detect conspicuous bots separately from its ability to detect advanced Web bots. We assessed the proposed framework performance using real HTTP traffic from a public Web server. Our experimental results show that the proposed framework has significant ability to detect Web bots that do not try to hide their bot identity using HTTP Web logs (balanced accuracy in a false-positive intolerant server > 95%). However, detecting advanced Web bots that present a browser fingerprint and may present a humanlike behaviour as well is considerably more difficult.

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Section: Background and Related Workmentioning

confidence: 99%

“…HTML, text, JavaScript, image, css, etc.) [13,22], the access patterns [3] and the HTTP errors produced [5,27]. These features are used as input to generate machine learning models.…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards a framework for detecting advanced Web bots

Iliou

Κωστούλας

Tsikrika

et al. 2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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“…In general approaches of find the patterns, pattern tree is created and the analysis is done, but in proposed algorithm there is no need of tree creation and the analysis is done based on the website architecture, which will increase the ability of the other pattern matching algorithms and needs only one database scan. Bhargav, Anshul, and MunishBhargav [5] have worked on pattern discovery and users classification through prediction model. The proposed structure is based on three steps.…”

Section: Literature Reviewmentioning

confidence: 99%

Multifactor Naïve Bayes Classification for the Slow Learner Prediction Over Multiclass Student Dataset

Swati¹,

Kaur²

2016

IJCSA

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The high school students must be observed for their slow learning or quick learning abilities to provide them with the best education practices. Such analysis can be perfectly performed over the student performance data. The high school student data has been obtained from the schools from the various regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300 students obtained from one school in the regions has been undergone the test using the proposed model in this paper. The proposed model is based upon the naïve bayes classification model for the data classification using the multi-factor features obtained from the input dataset. The subject groups have been divided into the two primary groups: difficult and normal. The classification algorithm has been applied individually over data grouped in the various subject groups. Both of the early stage classification events have produced the almost similar results, whereas the results obtained from the classification events over the averaging factors and the floating factors told the different story than the early stage classification. The proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input data. The proposed model can be considered as the effective classification model when evaluated from the results described in the earlier sections.

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“…In user classification process, each user is classified according to their characteristics. The classifications are based on country based classification, site entry based classification and access time based classification [2].…”

Section: Related Workmentioning

confidence: 99%

Predicting Users With Similar Behaviour Through Sessions

2017

IJAERD

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Pattern discovery and users classification through web usage mining

Cited by 14 publications

References 2 publications

Towards a framework for detecting advanced Web bots

Towards a framework for detecting advanced Web bots

Multifactor Naïve Bayes Classification for the Slow Learner Prediction Over Multiclass Student Dataset

Predicting Users With Similar Behaviour Through Sessions

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