Web Scraping is a popular methodology to extract data from websites. This is often done to derive insights for Sentiment Analysis, Predicting User preferences, Cross-Selling products, etc. Some of the real-life examples of web scraping include – extracting data for pricing analysis, user ratings for movie sentiment analysis, corporate admin tasks to read and classify log files in an HTML, search bots trying to make sense of a results page. While web scraping activity does not provide intelligence of its own, as we have seen above the data extracted can be useful in multiple ways. A more common use case would be a start-up eCommerce website trying to set a price on its products based on market research on competitors.
Web scraping involves writing a software robot that can automatically collect data from various webpages. Simple bots might get the job done, but more sophisticated bots use AI to find the appropriate data on a page and copy it to the appropriate data field to be processed by an analytics application. AI web scraping-based use cases include e-commerce, labor research, supply chain analytics, enterprise data capture and market research, said Sarah Petrova, co-founder at Techtestreport. These kinds of applications rely heavily on data and the syndication of data from different parties. Commercial applications use web scraping to do sentiment analysis about new product launches, curate structured data sets about companies and products, simplify business process integration and predictively gather data.
This article comes from Joon Im, a student in Business Science University. Joon has completed both the 201 (Advanced Machine Learning with H2O) and 102 (Shiny Web Applications) courses. Joon shows off his progress in this Web Scraping Tutorial with rvest. I recently completed the Part 2 of the Shiny Web Applications Course, DS4B 102-R and decided to make my own price prediction app. The app works by predicting prices on potential new bike models based on current existing data.
There is a universal rule for buying stuff, "paying less money is better than paying more money". For personal reasons I needed to buy a used car but I was not in a hurry, I had time to think about it and find the best deal. When checking the local used car shops, they showed me cars in the range of 7.000 to 10.000 euro. This is a lot of money so I thought I should use my data science skills to find the best deal. First thing we need for a machine learning project is data.
Security expert Bob Diachenko, along with Comparitech, has discovered more than 267 million Facebook user IDs, phone numbers and names in an unsecured database. The huge trove of data is likely the result of an illegal scraping operation or Facebook API abuse by a group of hackers in Vietnam. The exposed data could be used by threat actors to conduct large-scale SMS spam and phishing campaigns. "A database containing more than 267 million Facebook user IDs, phone numbers, and names was left exposed on the web for anyone to access without a password or any other authentication." "Comparitech partnered with security researcher Bob Diachenko to uncover the Elasticsearch cluster.
"The Clock" is a 2010 art installation by Christian Marclay. It is an experimental film that features over 12,000 individual shots of clocks from movies and television, edited in such a way that the film itself functions as a clock. In this talk, we'll use modern machine learning models and video web scraping to recreate the concept behind "The Clock". We'll use Kubernetes to orchestrate building a modern video scraper, capable of getting around the walls of YouTube and Instagram to grab user content. We'll then use existing machine learning models to infer when clocks occur in videos, to create our own montage with the found internet video.
If you have a model that has acceptable results but isn't amazing, take a look at your data! Taking the time to clean and preprocess your data the right way can make your model a star. In order to look at scraping and preprocessing in more detail, let's look at some of the work that went into "You Are What You Tweet: Detecting Depression in Social Media via Twitter Usage." That way, we can really examine the process of scraping Tweets and then cleaning and preprocessing them. We'll also do a little exploratory visualization, which is an awesome way to get a better sense of what your data looks like!
-- Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc. This paper focuses on design and experiment of a new approach for botnet detection from streaming web server logs, motivated by its wide applicability, real-time protection capability, ease of use and better security of sensitive data. Our algorithm is inspired by a Principal Component Analysis (PCA) to capture correlation in data, and we are first to recognize and adapt Lanczos method to improve the time complexity of PCA-based botnet detection from cubic to sub-cubic, which enables us to more accurately and sensitively detect botnets with sliding time windows rather than fixed time windows. We contribute a generalized online correlation matrix update formula, and a new termination condition for Lanczos iteration for our purpose based on error bound and non-decreasing eigenvalues of symmetric matrices. On our dataset of an ecommerce website logs, experiments show the time cost of Lanczos method with different time windows are consistently only 20% to 25% of PCA. I. INTRODUCTION A bot is a software application that runs automated scripts over the Internet  to perform malicious tasks like DOS attack, website statistics skew, click fraud, price/information scraping, spam/rumor distribution, etc. Traditional single bots usually execute their tasks at a rate much higher than average human users to achieve their goal within a time limit. A botnet, as the name suggests, is a group of bots that work in a coordinated fashion. In contrast to single bots, a botnet, especially those large-scale botnets, they might request resources at a humanlike speed, but altogether they place a heavy burden on the servers and collect large amount of information. Because bots in a botnet behave humanlike, they are much harder to detect, and have become a key platform for many Internet attacks.
We all know Microsoft has recently launched a massive'bug fix bundle' where it released patches for around 50 vulnerabilities including the patch for Cortana's Lock Screen Bypass Vulnerability. However, not many know about'all' of these vulnerabilities for which Microsoft released fixes. It was also strange that it released patches together for 50 different bugs. Seems like the team has been silently working out how to solve various issues reported to them over the past months. Now, an independent security researcher has unveiled one such issue.