To speak bluntly, when it comes to its visualization capabilities, Tableau, while it appears so promising, astonishingly lacks in its ability to integrate seamlessly with statistical, hypothesis-driven testing. You may be let down constantly if you feel the need to not only visualize but compare your set of observations between groups on hard statistical grounds. Hence, one must admit that there is still a strong value gap between visualization tools like Tableau, and pure statistical software such as Minitab, SPSS, SAS, and, of course, the humble yet tremendously powerful and open source workhorse, R. Tables and corresponding computations, at least at the time of writing this piece, are not able to support statistical testing, such as testing for normality, pairwise comparisons, accounting for interactions between variables, linear regression, logistics regression modeling, and, in general, statistical modeling capabilities. As of now, only basic statistical measures (central tendencies and measures of variation) can be computed. Here’s some example data I’ve been playing with: As I look at the above shipping costs, a question comes to mind: How do I form a hypothesis about whether shipping cost varies by sub-category or not? All I can get from Tableau is perhaps a box plot to visually compare costs. Is that enough? Of course not. What I would like to do is to find the average or mean shipping cost for each category or sub-category and then form a simple yes/no hypothesis. Sadly, Tablau falls silent on this question. Maybe we can try different types of charts or compare data visually/manually across or down a table. Typical Tableau users may find themselves constrained if they...
BlackArch Linux is an Arch Linux-based distribution for penetration testers and security researchers. The repository contains 2336 tools. You can install tools individually or in groups. BlackArch Linux is compatible with existing Arch installs. ChangeLog The following list contains official BlackArch live and netinstall ISO images. You can burn these images to DVDs and flashdrives. The live ISO contains a complete, functional BlackArch Linux system. The netinstall ISO is a lightweight image for bootstrapping machines. If possible, please try to use a mirror near you to download the ISOs. You can find a list of mirrors below. Image Version Torrent Size SHA1sum BlackArch Linux 64 bit Live ISO 2019.09.01 Torrent 16 GB 1c63f42625a0c4c8ff0f9148f6f857c56a851a05 BlackArch Linux 64 bit Netinstall ISO 2019.09.01 Torrent 660 MB eb2a791348626e98db0da4ca8a289ef7a8e7d0b5 Do not use UNetBootIn to write ISO files to flashdrives. UNetBootIn modifies the bootloader configuration, which is bad. You can use this instead (where /dev/sdX is your flashdrive and file.iso is a BlackArch ISO): # Example Image writing$ sudo dd bs=512M if=file.iso of=/dev/sdX Default Login The default login for all ISOs and OVA is: root:blackarch Installing On Top Of ArchLinux BlackArch Linux is compatible with existing/normal Arch installations. It acts as an unofficial user repository. Below you will find instructions on how to install BlackArch in this manner. >>Run https://blackarch.org/strap.sh as root and follow the instructions.curl -O https://blackarch.org/strap.sh>>The SHA1 sum should match: 9f770789df3b7803105e5fbc19212889674cd503 strap.shsha1sum strap.sh>>Set execute bitchmod +x strap.sh>>Run strap.shsudo ./strap.sh You may now install tools from the blackarch repository. >>To list all of the available tools, runsudo pacman -Sgg | grep blackarch | cut -d’ ‘ -f2 | sort -u >> To install all of the tools, runsudo pacman -S...
A scanner app with 100 million downloads starts to deliver malware An Android Google play app, available since 2010, has recently started installing malware. In recent years, cybersecurity — or cyber security, depending on your preferred usage — has become a frequent topic. Escalating cases of fraud, cybercrime, and data breaches have ensured that terms relating to cybersecurity, whether it be phishing or account compromise, have entered the consumer space and are no longer just known by professionals in the industry. Google, as the provider of one of the most popular search engines in the world, can provide an interesting resource to find out what areas of cybersecurity we are interested in, how threats are evolving — alongside our knowledge of them — and which vulnerabilities and attacks have gained the most widespread attention. This week, incident response platform Redscan published the results of research (.PDF) into Google cybersecurity-related search trends and their popularity based on Google Trends data from 2004 – 2019. The most-searched-for public figure in the industry is Robert Herjavec, investor and CEO of IT security firm Herjavec Group. Searches for Herjavec take place four times as often as those for Kevin Mitnick, dubbed the “world’s most famous hacker” and now an active security consultant. In addition to Herjavec and Mitnick, John McAfee, Bruce Schneier, and Troy Hunt are in the top five most searched-for security professionals. The cybersecurity companies that most commonly feature on general Google searches are Norton, Avast, AVG, Kaspersky, and ESET. When it comes to enterprise-related queries, Symantec, Fortinet, Akamai, Mimecast, and FireEye are the most popular, according to Redscan. If you...
As email continues to be not only an important means of communication but also an official record of information and a tool for managing tasks, schedules, and collaborations, making sense of everything moving in and out of our inboxes will only get more difficult. The good news is there’s a method to the madness of staying on top of your email, and Microsoft researchers are drawing on this behavior to create tools to support users. Two teams working in the space will be presenting papers at this year’s ACM International Conference on Web Search and Data Mining February 11–15 in Melbourne, Australia. “Identifying the emails you need to pay attention to is a challenging task,” says Partner Researcher and Research Manager Ryen White of Microsoft Research, who manages a team of about a dozen scientists and engineers and typically receives 100 to 200 emails a day. “Right now, we end up doing a lot of that on our own.” According to the McKinsey Global Institute, professionals spend 28 percent of their time on email, so thoughtful support tools have the potential to make a tangible difference. “We’re trying to bring in machine learning to make sense of a huge amount of data to make you more productive and efficient in your work,” says Senior Researcher and Research Manager Ahmed Hassan Awadallah. “Efficiency could come from a better ability to handle email, getting back to people faster, not missing things you would have missed otherwise. If we’re able to save some of that time so you could use it for your actual work function, that would be great.” Email deferral:...
While many cyber security professionals have been looking at (and even investing in) the potential benefits of utilizing artificial intelligence (AI) technology within many different business functions, earlier this week, the Israel National Cyber Directorate (INCD) issued a warning of a new type of cyber-attack that leverages AI to impersonate senior enterprise executives. The method instructs company employees to perform transactions including money transfers and other malicious activity on the network. There are recent reports of this type of cyber-attack received at the operational center of the INCD. While business email compromise (BEC) types of fraud oftentimes use social engineering methods for a more effective attack, this new method escalates the attack type by using AI-based software, which makes voice phishing calls to senior executives. The attacking software learns to mimic the voice of a person defined for it and makes a conversation with an employee on behalf of the CEO. It was also reported that today there are programs that, after listening to 20 minutes to a particular voice, can speak everything that the user types in that learned voice. The Potential AI Voice Threat Implications Head of Information Security & Data Protection Officer for Matrix Medical Network, Dr. Rebecca Wynn, cautions, “It is absolutely a threat to watch and very dangerous.” She explains that staff must be trained about receiving instructions from their managers or senior leaders that are out of the normal requests/processes and have a process in place to verify those requests without being sanctioned. “Experts have certainly been warning for the past two or three years about the dangerous side of artificial intelligence, namely...
What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. Figure 1: Source [1] In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant captions (“a soccer player is kicking a soccer ball”) while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. Figure 2: Source [2] ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience. The primary purpose of this blog post is to develop an understanding of how Convolutional Neural Networks work on images. If you are new to neural networks in general, I would recommend reading this short tutorial on Multi Layer Perceptrons to get an idea about how they work, before proceeding. Multi Layer Perceptrons are referred to as “Fully Connected Layers” in this post. The LeNet Architecture (1990s) LeNet was one of the very first convolutional neural networks which helped propel the field of Deep Learning. This pioneering work by Yann LeCun was named LeNet5 after many previous successful iterations since the year 1988 [3]. At that time the LeNet architecture was used mainly for character recognition tasks such as reading zip codes, digits, etc. Below, we will develop an intuition of how...
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