Related papers: Scrapers selectively respect robots.txt directives…

This paper explores the legal implications of violating "robots.txt", a technical standard widely used by webmasters to communicate restrictions on automated access to website content. Although historically regarded as a voluntary…

The success of generative AI relies heavily on training on data scraped through extensive crawling of the Internet, a practice that has raised significant copyright, privacy, and ethical concerns. While few measures are designed to resist a…

Gathering enough data to create sufficiently useful training datasets for generative artificial intelligence requires scraping most public websites. The scraping is conducted using pieces of code (scraping bots) that make copies of website…

In the present time, all know about World Wide Web and work over the Internet daily. In this paper, we introduce the search engines working for keywords that are entered by users to find something. The search engine uses different search…

Large Language Models (LLMs) are increasingly relying on web crawling to stay up to date and accurately answer user queries. These crawlers are expected to honor robots.txt files, which govern automated access. In this study, for the first…

From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large language models (LLMs). However, large-scale web scraping to feed LLMs can affect site…

In the past few years, IRC bots, malicious programs which are remotely controlled by the attacker through IRC servers, have become a major threat to the Internet and users. These bots can be used in different malicious ways such as issuing…

The internet has become the main source of data to train modern text-to-image or vision-language models, yet it is increasingly unclear whether web-scale data collection practices for training AI systems adequately respect data owners'…

The web bots have been blamed for consuming large amount of Internet traffic and undermining the interest of the scraped sites for years. Traditional bot detection studies focus mainly on signature-based solution, but advanced bots usually…

The increasing adoption of econometric and machine-learning approaches by empirical researchers has led to a widespread use of one data collection method: web scraping. Web scraping refers to the use of automated computer programs to access…

Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting heavily relied on automated web crawls, which inherently…

Automated bots now account for roughly half of all web requests, and an increasing number deliberately spoof their identity to either evade detection or to not respect robots.txt. Existing countermeasures are either resource-intensive…

Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps…

A bot is a piece of software that is usually installed on an infected machine without the user's knowledge. A bot is controlled remotely by the attacker under a Command and Control structure. Recent statistics show that bots represent one…

Large language models rely on web-scraped text for training; concurrently, content creators are increasingly blocking AI crawlers to retain control over their data. We analyze crawler restrictions across the top one million most-visited…

In recent years, Twitter has seen a proliferation of automated accounts or bots that send spam, offer clickbait, compromise security using malware, and attempt to skew public opinion. Previous research estimates that around 9% to 17% of…

We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we…

For more than a decade now, academicians and online platform administrators have been studying solutions to the problem of bot detection. Bots are computer algorithms whose use is far from being benign: malicious bots are purposely created…

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to…

Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy…