Content-aware web robot detection

Lagopoulos, Athanasios; Tsoumakas, Grigorios

doi:10.1007/s10489-020-01754-9

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…Regarding the web logs, several measurable features are extracted from the pages that the visitors accessed, along with the access frequency and access patterns [11], [22]; the semantics of the content of each page have also been considered [12]. Features extracted from web logs are used to train machine learning models to classify the new visitors as bots or humans, mainly through the use of classification [11], [16] or clustering algorithms [22], [23].…”

Section: A Web Bot Detectionmentioning

confidence: 99%

Web Bot Detection Evasion Using Generative Adversarial Networks

Iliou

Κωστούλας

Tsikrika

et al. 2021

2021 IEEE International Conference on Cyber Security and Resilience (CSR)

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Web bots are programs that can be used to browse the web and perform automated actions. These actions can be benign, such as web indexing and website monitoring, or malicious, such as content scraping and scalping. To detect bots, web servers consider bots' fingerprint and behaviour, with research showing that techniques that examine the visitor's mouse movements can be very effective. In this work, we showcase that web bots can leverage the latest advances in machine learning to evade detection based on their mouse movements and touchscreen trajectories (for the case of mobile web bots). More specifically, the proposed web bots utilise Generative Adversarial Networks (GANs) to generate images of trajectories similar to those of humans, which can then be used by bots to evade detection. We show that, even if the web server is aware of the attack method, web bots can generate behaviours that can evade detection.

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Section: A Web Bot Detectionmentioning

confidence: 99%

Web Bot Detection Evasion Using Generative Adversarial Networks

Iliou

Κωστούλας

Tsikrika

et al. 2021

2021 IEEE International Conference on Cyber Security and Resilience (CSR)

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“…The actuating part is in direct contact with the workpiece, and its structural design is particularly important; the driving part mainly provides the driving force for the actuating part, so that the actuator can realize the corresponding gripping action [17][18][19]. The transmission system of the gripping mechanism is mainly in the form of mechanical transmission, hydraulic transmission, pneumatic and electric transmission, etc., [17][18][19][20][21][22][23][24][25]; the control system is to control the robot according to specific procedures and requirements so that the machine can complete the specified gripping action and tasks [22,23]; the auxiliary system contains various detection devices, sensing devices, identification devices, etc., [24,25]. Together, they serve the actuator to ensure the smooth implementation of the gripping work.…”

Section: Introductionmentioning

confidence: 99%

A Review of End-Effector Research Based on Compliance Control

Dai

Xiang

et al. 2022

Machines

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The end-effector is a key device for direct contact and operation between the operator and the workpiece, and its mechanical structure will directly affect the quality of the machine and expand its application. The theoretical research and technical implementation of the end-effector for compliance control are facing a lot of urgent challenges to be solved, therefore, the research results of active compliance control of robot end-effectors have a very broad application prospect. This paper describes the design and research results of different end-effectors under impedance-based control, hybrid force/position control, and intelligent flexible control methods, respectively. Under each control method, the structural characteristics and the optimized control scheme under different drives are introduced. Finally, key techniques for achieving compliance control are derived by summarizing, which broadens the engineering applications and provides methods and ideas for future research.

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“…for completed user visits (Web sessions). Primarily, supervised learning has been used, e.g., decision tress, support vector machines, neural networks, ensemble methods (Iliou et al 2019;Lagopoulos and Tsoumakas 2020;Lysenko et al 2020;Rahman and Tomar 2021;Rovetta et al 2017;Ustebay et al 2019). Unsupervised learning has also been investigated (Alam et al 2014;Suchacka and Iwański 2020;Rovetta et al 2020;Zabihi et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Digital Twins for Lighting Analysis: Literature Review, Challenges, and Research Opportunities

Hassan¹,

Angelaki²,

Alfaro³

et al. 2022

ECMS 2022 Proceedings Edited by Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat

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Light modelling, simulation, and photometric calculations are by now common tasks in the lighting design process. These practices contribute to the definition and comparison of suitable layout arrangements and help predict the impact of lighting devices. Those tasks demand the use of tools to support the simulation of different scenarios, the analyses of their pros and cons according to different criteria (e.g., health and safety, perception, aesthetics, energy consumption, and costs), and decision-making. Digital twins have emerged as relevant technologies to simulate and visualize different ``what-if'' scenarios associated with physical entities and processes. In this paper, we investigate the state-of-the-art research concerning the use of digital twins for supporting lighting analysis in the urban/outdoor context. We also present and discuss challenges and research opportunities related to the design, implementation, and validation of digital twins in this domain.

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Content-aware web robot detection

Cited by 11 publications

References 25 publications

Web Bot Detection Evasion Using Generative Adversarial Networks

Web Bot Detection Evasion Using Generative Adversarial Networks

A Review of End-Effector Research Based on Compliance Control

Digital Twins for Lighting Analysis: Literature Review, Challenges, and Research Opportunities

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