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posibil HWID ban încearcă metodele lui Nytro

,Te duce cu plugușorul ca de Paște. și Amazon face treaba asta

SSHPry2.0 This is a second release of SSHPry tool, with multiple features added. Control of target's TTY Built-In Keylogger Console-Level phishing Record & Replay previous sessions Demo: Blogpost: http://www.korznikov.com/2017/09/sshpry-v2-spy-control-ssh-connected.html Twitter: @nopernik Howto ./sshpry2.py Files: sshpry2.py - the tool Everything else parts of code used in the tool (does not depends on them) Download SSHPry2.0-master.zip or git clone https://github.com/nopernik/SSHPry2.0.git Source

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Table of Contents How Instagram Bots Work How to Automate a Browser How to Use the Page Object Pattern How to Build an Instagram Bot With InstaPy Essential Features Additional Features in InstaPy 5. Conclusion What do SocialCaptain, Kicksta, Instavast, and many other companies have in common? They all help you reach a greater audience, gain more followers, and get more likes on Instagram while you hardly lift a finger. They do it all through automation, and people pay them a good deal of money for it. But you can do the same thing—for free—using InstaPy! In this tutorial, you’ll learn how to build a bot with Python and InstaPy, a library by Tim Großmann which automates your Instagram activities so that you gain more followers and likes with minimal manual input. Along the way, you’ll learn about browser automation with Selenium and the Page Object Pattern, which together serve as the basis for InstaPy. In this tutorial, you’ll learn: How Instagram bots work How to automate a browser with Selenium How to use the Page Object Pattern for better readability and testability How to build an Instagram bot with InstaPy You’ll begin by learning how Instagram bots work before you build one. How Instagram Bots Work How can an automation script gain you more followers and likes? Before answering this question, think about how an actual person gains more followers and likes. They do it by being consistently active on the platform. They post often, follow other people, and like and leave comments on other people’s posts. Bots work exactly the same way: They follow, like, and comment on a consistent basis according to the criteria you set. The better the criteria you set, the better your results will be. You want to make sure you’re targeting the right groups because the people your bot interacts with on Instagram will be more likely to interact with your content. For example, if you’re selling women’s clothing on Instagram, then you can instruct your bot to like, comment on, and follow mostly women or profiles whose posts include hashtags such as #beauty, #fashion, or #clothes. This makes it more likely that your target audience will notice your profile, follow you back, and start interacting with your posts. How does it work on the technical side, though? You can’t use the Instagram Developer API since it is fairly limited for this purpose. Enter browser automation. It works in the following way: You serve it your credentials. You set the criteria for who to follow, what comments to leave, and which type of posts to like. Your bot opens a browser, types in https://instagram.com on the address bar, logs in with your credentials, and starts doing the things you instructed it to do. Next, you’ll build the initial version of your Instagram bot, which will automatically log in to your profile. Note that you won’t use InstaPy just yet. How to Automate a Browser For this version of your Instagram bot, you’ll be using Selenium, which is the tool that InstaPy uses under the hood. First, install Selenium. During installation, make sure you also install the Firefox WebDriver since the latest version of InstaPy dropped support for Chrome. This also means that you need the Firefox browser installed on your computer. Now, create a Python file and write the following code in it: from time import sleep from selenium import webdriver browser = webdriver.Firefox() browser.get('https://www.instagram.com/') sleep(5) browser.close() Run the code and you’ll see that a Firefox browser opens and directs you to the Instagram login page. Here’s a line-by-line breakdown of the code: Lines 1 and 2 import sleep and webdriver. Line 4 initializes the Firefox driver and sets it to browser. Line 6 types https://www.instagram.com/ on the address bar and hits Enter. Line 8 waits for five seconds so you can see the result. Otherwise, it would close the browser instantly. Line 10 closes the browser. This is the Selenium version of Hello, World. Now you’re ready to add the code that logs in to your Instagram profile. But first, think about how you would log in to your profile manually. You would do the following: Go to https://www.instagram.com/. Click the login link. Enter your credentials. Hit the login button. The first step is already done by the code above. Now change it so that it clicks on the login link on the Instagram home page: from time import sleep from selenium import webdriver browser = webdriver.Firefox() browser.implicitly_wait(5) browser.get('https://www.instagram.com/') login_link = browser.find_element_by_xpath("//a[text()='Log in']") login_link.click() sleep(5) browser.close() Note the highlighted lines: Line 5 sets five seconds of waiting time. If Selenium can’t find an element, then it waits for five seconds to allow everything to load and tries again. Line 9 finds the element <a> whose text is equal to Log in. It does this using XPath, but there are a few other methods you could use. Line 10 clicks on the found element <a> for the login link. Run the script and you’ll see your script in action. It will open the browser, go to Instagram, and click on the login link to go to the login page. On the login page, there are three important elements: The username input The password input The login button Next, change the script so that it finds those elements, enters your credentials, and clicks on the login button: from time import sleep from selenium import webdriver browser = webdriver.Firefox() browser.implicitly_wait(5) browser.get('https://www.instagram.com/') login_link = browser.find_element_by_xpath("//a[text()='Log in']") login_link.click() sleep(2) username_input = browser.find_element_by_css_selector("input[name='username']") password_input = browser.find_element_by_css_selector("input[name='password']") username_input.send_keys("<your username>") password_input.send_keys("<your password>") login_button = browser.find_element_by_xpath("//button[@type='submit']") login_button.click() sleep(5) browser.close() Here’s a breakdown of the changes: Line 12 sleeps for two seconds to allow the page to load. Lines 14 and 15 find username and password inputs by CSS. You could use any other method that you prefer. Lines 17 and 18 type your username and password in their respective inputs. Don’t forget to fill in <your username> and <your password>! Line 20 finds the login button by XPath. Line 21 clicks on the login button. Run the script and you’ll be automatically logged in to to your Instagram profile. You’re off to a good start with your Instagram bot. If you were to continue writing this script, then the rest would look very similar. You would find the posts that you like by scrolling down your feed, find the like button by CSS, click on it, find the comments section, leave a comment, and continue. The good news is that all of those steps can be handled by InstaPy. But before you jump into using Instapy, there is one other thing that you should know about to better understand how InstaPy works: the Page Object Pattern. How to Use the Page Object Pattern Now that you’ve written the login code, how would you write a test for it? It would look something like the following: def test_login_page(browser): browser.get('https://www.instagram.com/accounts/login/') username_input = browser.find_element_by_css_selector("input[name='username']") password_input = browser.find_element_by_css_selector("input[name='password']") username_input.send_keys("<your username>") password_input.send_keys("<your password>") login_button = browser.find_element_by_xpath("//button[@type='submit']") login_button.click() errors = browser.find_elements_by_css_selector('#error_message') assert len(errors) == 0 Can you see what’s wrong with this code? It doesn’t follow the DRY principle. That is, the code is duplicated in both the application and the test code. Duplicating code is especially bad in this context because Selenium code is dependent on UI elements, and UI elements tend to change. When they do change, you want to update your code in one place. That’s where the Page Object Pattern comes in. With this pattern, you create page object classes for the most important pages or fragments that provide interfaces that are straightforward to program to and that hide the underlying widgetry in the window. With this in mind, you can rewrite the code above and create a HomePage class and a LoginPage class: from time import sleep class LoginPage: def __init__(self, browser): self.browser = browser def login(self, username, password): username_input = self.browser.find_element_by_css_selector("input[name='username']") password_input = self.browser.find_element_by_css_selector("input[name='password']") username_input.send_keys(username) password_input.send_keys(password) login_button = browser.find_element_by_xpath("//button[@type='submit']") login_button.click() sleep(5) class HomePage: def __init__(self, browser): self.browser = browser self.browser.get('https://www.instagram.com/') def go_to_login_page(self): self.browser.find_element_by_xpath("//a[text()='Log in']").click() sleep(2) return LoginPage(self.browser) The code is the same except that the home page and the login page are represented as classes. The classes encapsulate the mechanics required to find and manipulate the data in the UI. That is, there are methods and accessors that allow the software to do anything a human can. One other thing to note is that when you navigate to another page using a page object, it returns a page object for the new page. Note the returned value of go_to_log_in_page(). If you had another class called FeedPage, then login() of the LoginPage class would return an instance of that: return FeedPage(). Here’s how you can put the Page Object Pattern to use: from selenium import webdriver browser = webdriver.Firefox() browser.implicitly_wait(5) home_page = HomePage(browser) login_page = home_page.go_to_login_page() login_page.login("<your username>", "<your password>") browser.close() It looks much better, and the test above can now be rewritten to look like this: def test_login_page(browser): home_page = HomePage(browser) login_page = home_page.go_to_login_page() login_page.login("<your username>", "<your password>") errors = browser.find_elements_by_css_selector('#error_message') assert len(errors) == 0 With these changes, you won’t have to touch your tests if something changes in the UI. For more information on the Page Object Pattern, refer to the official documentation and to Martin Fowler’s article. Now that you’re familiar with both Selenium and the Page Object Pattern, you’ll feel right at home with InstaPy. You’ll build a basic bot with it next. How to Build an Instagram Bot With InstaPy In this section, you’ll use InstaPy to build an Instagram bot that will automatically like, follow, and comment on different posts. First, you’ll need to install InstaPy: $ python3 -m pip install instapy This will install instapy in your system. Essential Features Now you can rewrite the code above with InstaPy so that you can compare the two options. First, create another Python file and put the following code in it: from instapy import InstaPy InstaPy(username="<your_username>", password="<your_password>").login() Replace the username and password with yours, run the script, and voilà! With just one line of code, you achieved the same result. Even though your results are the same, you can see that the behavior isn’t exactly the same. In addition to simply logging in to your profile, InstaPy does some other things, such as checking your internet connection and the status of the Instagram servers. This can be observed directly on the browser or in the logs: INFO [2019-12-17 22:03:19] [username] -- Connection Checklist [1/3] (Internet Connection Status) INFO [2019-12-17 22:03:20] [username] - Internet Connection Status: ok INFO [2019-12-17 22:03:20] [username] - Current IP is "17.283.46.379" and it's from "Germany/DE" INFO [2019-12-17 22:03:20] [username] -- Connection Checklist [2/3] (Instagram Server Status) INFO [2019-12-17 22:03:26] [username] - Instagram WebSite Status: Currently Up Pretty good for one line of code, isn’t it? Now it’s time to make the script do more interesting things than just logging in. For the purpose of this example, assume that your profile is all about cars, and that your bot is intended to interact with the profiles of people who are also interested in cars. First, you can like some posts that are tagged #bmw or #mercedes using like_by_tags(): from instapy import InstaPy session = InstaPy(username="<your_username>", password="<your_password>") session.login() session.like_by_tags(["bmw", "mercedes"], amount=5) Here, you gave the method a list of tags to like and the number of posts to like for each given tag. In this case, you instructed it to like ten posts, five for each of the two tags. But take a look at what happens after you run the script: INFO [2019-12-17 22:15:58] [username] Tag [1/2] INFO [2019-12-17 22:15:58] [username] --> b'bmw' INFO [2019-12-17 22:16:07] [username] desired amount: 14 | top posts [disabled]: 9 | possible posts: 43726739 INFO [2019-12-17 22:16:13] [username] Like# [1/14] INFO [2019-12-17 22:16:13] [username] https://www.instagram.com/p/B6MCcGcC3tU/ INFO [2019-12-17 22:16:15] [username] Image from: b'mattyproduction' INFO [2019-12-17 22:16:15] [username] Link: b'https://www.instagram.com/p/B6MCcGcC3tU/' INFO [2019-12-17 22:16:15] [username] Description: b'Mal etwas anderes \xf0\x9f\x91\x80\xe2\x98\xba\xef\xb8\x8f Bald ist das komplette Video auf YouTube zu finden (n\xc3\xa4here Infos werden folgen). Vielen Dank an @patrick_jwki @thehuthlife und @christic_ f\xc3\xbcr das bereitstellen der Autos \xf0\x9f\x94\xa5\xf0\x9f\x98\x8d#carporn#cars#tuning#bagged#bmw#m2#m2competition#focusrs#ford#mk3#e92#m3#panasonic#cinematic#gh5s#dji#roninm#adobe#videography#music#bimmer#fordperformance#night#shooting#' INFO [2019-12-17 22:16:15] [username] Location: b'K\xc3\xb6ln, Germany' INFO [2019-12-17 22:16:51] [username] --> Image Liked! INFO [2019-12-17 22:16:56] [username] --> Not commented INFO [2019-12-17 22:16:57] [username] --> Not following INFO [2019-12-17 22:16:58] [username] Like# [2/14] INFO [2019-12-17 22:16:58] [username] https://www.instagram.com/p/B6MDK1wJ-Kb/ INFO [2019-12-17 22:17:01] [username] Image from: b'davs0' INFO [2019-12-17 22:17:01] [username] Link: b'https://www.instagram.com/p/B6MDK1wJ-Kb/' INFO [2019-12-17 22:17:01] [username] Description: b'Someone said cloud? \xf0\x9f\xa4\x94\xf0\x9f\xa4\xad\xf0\x9f\x98\x88 \xe2\x80\xa2\n\xe2\x80\xa2\n\xe2\x80\xa2\n\xe2\x80\xa2\n#bmw #bmwrepost #bmwm4 #bmwm4gts #f82 #bmwmrepost #bmwmsport #bmwmperformance #bmwmpower #bmwm4cs #austinyellow #davs0 #mpower_official #bmw_world_ua #bimmerworld #bmwfans #bmwfamily #bimmers #bmwpost #ultimatedrivingmachine #bmwgang #m3f80 #m5f90 #m4f82 #bmwmafia #bmwcrew #bmwlifestyle' By default, InstaPy will like the first nine top posts in addition to your amount value. In this case, that brings the total number of likes per tag to fourteen (nine top posts plus the five you specified in amount). Also note that InstaPy logs every action it takes. As you can see above, it mentions which post it liked as well as its link, description, location, and whether the bot commented on the post or followed the author. You may have noticed that there are delays after almost every action. That’s by design. It prevents your profile from getting banned on Instagram. Now, you probably don’t want your bot liking inappropriate posts. To prevent that from happening, you can use set_dont_like(): from instapy import InstaPy session = InstaPy(username="<your_username>", password="<your_password>") session.login() session.like_by_tags(["bmw", "mercedes"], amount=5) session.set_dont_like(["naked", "nsfw"]) With this change, posts that have the words naked or nsfw in their descriptions won’t be liked. You can flag any other words that you want your bot to avoid. Next, you can tell the bot to not only like the posts but also to follow some of the authors of those posts. You can do that with set_do_follow(): from instapy import InstaPy session = InstaPy(username="<your_username>", password="<your_password>") session.login() session.like_by_tags(["bmw", "mercedes"], amount=5) session.set_dont_like(["naked", "nsfw"]) session.set_do_follow(True, percentage=50) If you run the script now, then the bot will follow fifty percent of the users whose posts it liked. As usual, every action will be logged. You can also leave some comments on the posts. There are two things that you need to do. First, enable commenting with set_do_comment(): from instapy import InstaPy session = InstaPy(username="<your_username>", password="<your_password>") session.login() session.like_by_tags(["bmw", "mercedes"], amount=5) session.set_dont_like(["naked", "nsfw"]) session.set_do_follow(True, percentage=50) session.set_do_comment(True, percentage=50) Next, tell the bot what comments to leave with set_comments(): from instapy import InstaPy session = InstaPy(username="<your_username>", password="<your_password>") session.login() session.like_by_tags(["bmw", "mercedes"], amount=5) session.set_dont_like(["naked", "nsfw"]) session.set_do_follow(True, percentage=50) session.set_do_comment(True, percentage=50) session.set_comments(["Nice!", "Sweet!", "Beautiful :heart_eyes:"]) Run the script and the bot will leave one of those three comments on half the posts that it interacts with. Now that you’re done with the basic settings, it’s a good idea to end the session with end(): from instapy import InstaPy session = InstaPy(username="<your_username>", password="<your_password>") session.login() session.like_by_tags(["bmw", "mercedes"], amount=5) session.set_dont_like(["naked", "nsfw"]) session.set_do_follow(True, percentage=50) session.set_do_comment(True, percentage=50) session.set_comments(["Nice!", "Sweet!", "Beautiful :heart_eyes:"]) session.end() This will close the browser, save the logs, and prepare a report that you can see in the console output. Additional Features in InstaPy InstaPy is a sizable project that has a lot of thoroughly documented features. The good news is that if you’re feeling comfortable with the features you used above, then the rest should feel pretty similar. This section will outline some of the more useful features of InstaPy. Quota Supervisor You can’t scrape Instagram all day, every day. The service will quickly notice that you’re running a bot and will ban some of its actions. That’s why it’s a good idea to set quotas on some of your bot’s actions. Take the following for example: session.set_quota_supervisor(enabled=True, peak_comments_daily=240, peak_comments_hourly=21) The bot will keep commenting until it reaches its hourly and daily limits. It will resume commenting after the quota period has passed. Headless Browser This feature allows you to run your bot without the GUI of the browser. This is super useful if you want to deploy your bot to a server where you may not have or need the graphical interface. It’s also less CPU intensive, so it improves performance. You can use it like so: session = InstaPy(username='test', password='test', headless_browser=True) Note that you set this flag when you initialize the InstaPy object. Using AI to Analyze Posts Earlier you saw how to ignore posts that contain inappropriate words in their descriptions. What if the description is good but the image itself is inappropriate? You can integrate your InstaPy bot with ClarifAI, which offers image and video recognition services: session.set_use_clarifai(enabled=True, api_key='<your_api_key>') session.clarifai_check_img_for(['nsfw']) Now your bot won’t like or comment on any image that ClarifAI considers NSFW. You get 5,000 free API-calls per month. Relationship Bounds It’s often a waste of time to interact with posts by people who have a lot of followers. In such cases, it’s a good idea to set some relationship bounds so that your bot doesn’t waste your precious computing resources: session.set_relationship_bounds(enabled=True, max_followers=8500) With this, your bot won’t interact with posts by users who have more than 8,500 followers. For many more features and configurations in InstaPy, check out the documentation. Conclusion InstaPy allows you to automate your Instagram activities with minimal fuss and effort. It’s a very flexible tool with a lot of useful features. In this tutorial, you learned: How Instagram bots work How to automate a browser with Selenium How to use the Page Object Pattern to make your code more maintainable and testable How to use InstaPy to build a basic Instagram bot Read the InstaPy documentation and experiment with your bot a little bit. Soon you’ll start getting new followers and likes with a minimal amount of effort. I gained a few new followers myself while writing this tutorial. If you prefer video tutorials, there is also a Udemy course by the creator of InstaPy Tim Großmann. If there’s anything you’d like to ask or share, then please reach out in the comments below. Source: realpython.com

Poate veni de oriunde https://www.theverge.com/2019/4/11/18306525/scott-mark-kelly-twins-year-international-space-station-nasa-dna-genes-health

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Information systems, their use in business, and the larger impact they are having on our world. DAVID BOURGEOIS JOSEPH MORTATI, SHOUHONG WANG, AND JAMES SMITH Overall • New and updated images, especially those related to statistics, in order to bring them up to date. • References brought up to date. • Added labs for every chapter. • Added an index. • Editing for consistency. Chapter 1: What is an information system? • Added video: Blum’s fibre optic TED Talk viii | Changes from Previous Edition Chapter 2: Hardware • Removed text which discussed increasing dependency on tablets and decreasing use of desktops • Clarification of bit vs. byte, binary vs. digital. Added tables to Understanding Binary sidebar • Added Huang’s Law on graphics processor units • Modified text regarding Moore’s Law to state that his law is no longer able to be maintained Chapter 3: Software • Added information about Ubuntu Linux • Added Eclipse IDE • Added information about Tableau • Supply Chain Management: added an emphasis on use of Information Systems up and down supply chain by Walmart to gain competitive advantage Chapter 4: Data and Databases • Database schemas redesigned • Data types added • SQL examples include output • NoSQL described • Data Dictionary re-ordered to column name • New section on “Why database technology?” • Differentiation of data, information, and knowledge • Section on Data models • Changed illustrative example of database tables and relationships. Changes from Previous Edition | ix • Updated section on Business Intelligence to focus on the rise of analytics and data science. Includes a new “What is Data Science?” sidebar. Chapter 5: Networking and Communication • History of ARPANET initial four nodes, etc. • Metcalfe’s Law Chapter 6: Information Systems Security • Added information on blockchain and Bitcoin. Chapter 8: Business Processes • Introduce tools (DFD, BPMN, UML) of business process modeling • Introduce examples of DFD. Chapter 10: Information Systems Development • Java sample code • Mismanaging Change side bar • Added section on mobile development. • Added sidebar on risks of end-user computing x | Information Systems for Business and Beyond (2019) Chapter 11: Globalization and the Digital Divide • World 3.0 written by economist Pankaj Ghemawat; also his TED talk video Chapter 12: The Ethical and Legal Implications of Information Systems • Facebook and Cambridge Analytics data privacy • General Data Protection Regulation section Chapter 13: Trends in Information Systems • Waze mapping app • Drone video • Drone blood delivery in Kenya video • Added sidebar on Mary Meeker and her Internet Trends report Download Source

Staking offers crypto HODLERS an easy way to earn a passive income Cryptocurrencies offer many ways to earn a passive income. Staking is just one, and a great entry point for those looking to take the next step toward financial independence. From the outset, what made decentralized technology so unique was that it rewarded miners for confirming transactions across the networks. This is called the Proof of Work or PoW method. But, in 2012, the Proof of Stake (PoS) consensus was introduced. With that in mind, let’s learn more about staking and explore the cryptocurrencies that can be utilized to earn a passive income. Then, learn more about the rewarding process of PoS and also what staking coins to look out for in 2020. What is staking? Staking is a system whereby owners of certain cryptocurrencies receive rewards for their contribution to the network. Incentives given to cryptocurrency owners depend on the number of tokens they have in their wallets. This method over time has also proved to be more democratic and popular than PoW (mining). The PoS method of confirming transactions involves owners of certain cryptocurrencies being compensated with rewards for their contribution to the smooth running of the network. The reward amount is dependent on the number of crypto tokens held in their wallets. In the long-term, this has proved a far more popular method because there is a higher chance of everyone getting compensated with a passive income. Unlike PoW where only large scale miners earn good incentives. Staking has also encouraged crypto enthusiasts to hold on to tokens for a longer amount of time, adding more buoyancy to the ecosystem. PoS blockchains simply allow users to validate the transaction by depositing and also hodling a certain amount of cryptocurrency. Therefore eliminating the need for mining hardware to confirm their transactions. Staking is an excellent way to put your crypto to work and make a passive income. Knowing the right cryptocurrencies as well as the most profitable staking systems is the key. This will ensure you earn the highest amount of dividend possible for your work. Let’s explore the five best coins to stake in 2020 to generate a passive income. EOS EOS makes use of the delegated PoS consensus method and actually prived a very useful reward calculator on their site for potential users to weigh up any gains. Scatter is the main wallet of EOS and when staking the first thing you will need to do is to download “Scatter“. EOS staking (also known as block producing) is one of the best options in the cryptocurrency space. All EOS holders have to vote for a Block Producer (BP). The 21 BP with the highest votes will be allowed to produce blocks. EOS staking also has an annual reward of app. 4.4%. Token: EOS Annual Return: 4.4% Staking wallet: Scatter wallet Dash Formerly known as XCoin and DarkCoin, renamed to Dash in May 2015. One of the first cryptocurrencies to switch over to a PoS consensus mechanism. Users are promised anonymous, trustless and instant payments. Dash currently yields 7.01% for operating a masternode and also touts itself as a decentralized P2P electronic cash system with hopes to become as acceptable as fiat. DASH hodlers can earn staking rewards by running a masternode. However, to operate a masternode, you need at least 1000 DASH. One unit of Dash is about $75, at the time of writing. $75, 000 (1000 x $75) might seem like a lot. But when you consider that for every $75,000 you invest, you receive an annual interest of 7.5%. Token: DASH (Dash) Annual Return: 7.5% Staking Wallet – DASH Desktop Wallets for staking NEO Formerly known as Antshares, NEO was the first Chinese open-source blockchain platform. Touted as being a “distributed network for the smart economy”. The NEO blockchain supports two native tokens: NEO and Gas. Gas is a given to people who hold NEO and NEO blocks generate 8 Gas every 15-20 seconds. Staking NEO has an interest rate of 5.5% with Gas issuance is expected to stop in approximately 22 years. In order to earn NEO, you will first need to purchase some from a crypto exchange. You will also need to download and install the NEO wallet, this is because it is the only one that can receive GAS. One GAS is worth approx $24. Token: NEO Annual Return: 5.5% Staking Wallet: NEO staking wallet (NEON) Ontology Ontology is a digital which at the time of writing was priced at $0.40. Its daily trading volume amounts to $102,741,273 with an annual staking reward of 5.6%. In order to stake ONT, you will need to purchase 500 Ontology first. 500 ONT is approximately $200. You will also need to download and install the latest version of OWallet. One note to mention is that users are offered the option to join one of the top seven nodes. We would advise you to choose the lowest-ranked node (Rank 7) as it is expected to produce a higher monthly reward payout. Token: Ontology [ONT] Annual Return: 5.6% Staking wallet: OWallet NULS NULS offers one of the easiest coins for staking. Its price at the time of writing is $0.18 with a daily trading volume of $7,412,226. The crypto has an annual yield of approximately 13.1% to 15%. Nuls is a little different to the other tokens mentioned in this article and has some specific steps which must be followed to get started. Firstly you will need to swap your ERC20 NULS tokens to MAINNET NULS coins. To qualify for staking you also need a minimum of 2000 tokens (app. $360) to start staking. Then, download the latest version of NULS wallet from the official website. Finally, you choose a node to join and get started. Token: NULS [Nuls] Annual return: Ranging between 13.1% and 15% Staking wallet: NULS wallet Via dappradar.com

Cum se numeste tema?

Clickjacking

zBang is a risk assessment tool that detects potential privileged account threats zBang is a special risk assessment tool that detects potential privileged account threats in the scanned network. Organizations and red teamers can utilize zBang to identify potential attack vectors and improve the security posture of the network. The results can be analyzed with the graphic interface or by reviewing the raw output files. More details on zBang could be found in the Big zBang Theory blog post by @Hechtov: https://www.cyberark.com/threat-research-blog/the-big-zbang-theory-a-new-open-source-tool/ The tool is built from five different scanning modules: ACLight scan - discovers the most privileged accounts that must be protected, including suspicious Shadow Admins. Skeleton Key scan - discovers Domain Controllers that might be infected by Skeleton Key malware. SID History scan - discovers hidden privileges in domain accounts with secondary SID (SID History attribute). RiskySPNs scan - discovers risky configuration of SPNs that might lead to credential theft of Domain Admins Mystique scan - discovers risky Kerberos delegation configuration in the network. For your convenience, there is also a summarized Data Sheet about zBang: https://github.com/cyberark/zBang/blob/master/zBang Summarized Data Sheet.pdf Execution Requirements Run it with any domain user. The scans do not require any extra privileges; the tool performs read-only LDAP queries to the DC. Run the tool from a domain joined machine (a Windows machine). PowerShell version 3 or above and .NET 4.5 (it comes by default in Windows 8/2012 and above). Quick Start Guide Download and run the release version from this GitHub repository link or compile it with your favorite compiler. Sometimes, when downloading it through the browser, you will need to "unblock" the downloaded zBang.exe file. In the opening screen, choose what scans you wish to execute. In the following example, all five scans are chosen: 3. To view demo results, click “Reload.” zBang tool comes with built-in initiating demo data; you can view the results of the different scans and play with the graphic interface. 4. To initiate new scans in your network, click “Launch.” A new window will pop up and will display the status of the different scans. 5. When the scans are completed, there will be a message saying the results were exported to an external zip file. 6. The results zip file will be in the same folder of zBang and will have a unique name with the time and the date of the scans. You can also import previous results into the zBang GUI without the need of rerunning the scans. To import previous results, click “Import” in the zBang’s opening screen. Go Over zBang Results A. ACLight scan: Choose the domain you have scanned. You will see a list of the most privileged accounts that were discovered. On the left side - view “standard” privileged accounts that get their privileges due to their group membership. On the right side - view “Shadow Admins.” Those accounts get their privileges through direct ACL permissions assignment. Those accounts might be stealthier than standard “domain admin” users, and therefore, they might not be as secure as they should be. Attackers often target and try to compromise such accounts. On each account, you can double click and review its permissions graph. It may help you understand why this account was classified as privileged. 6. The different abusable ACL permissions are described in a small help page. Click the “question mark” in the upper right corner to view: 7. More details on the threat of Shadow Admins are available in the blog post - “Shadow Admins – The Stealthy Accounts That You Should Fear The Most”: https://www.cyberark.com/threat-research-blog/shadow-admins-stealthy-accounts-fear/ 8. For manual examination of the scan results, unzip the saved zBang results file and check the results folder: "[Path of the zBang’s unzipped results file]\ACLight-master\Results”, contains a summary report - “Privileged Accounts - Layers Analysis.txt”. 9. On each of the discovered privileged accounts: Identify the privileged account. Reduce unnecessary permissions from the account. Secure the account. After validating these three steps, you can mark the account with a “V” in the small selection box, turning it green on the interface. 10. The goal is to make all the accounts marked as “secured” with the green color. B. Skeleton Key scan In the scan page (click the relevant bookmark in the above section), there will be a list of all the scanned DCs. Make sure all of them are clean and marked with green. If the scan finds a potential infected DC, it is crucial to initiate an investigation process. 4. More details on Skeleton Key malware are available in the blog post “Active Directory Domain Controller Skeleton Key Malware & Mimikatz” by @PyroTek3: https://adsecurity.org/?p=1255 C. SID History scan In this scan page, there will be a list of the domain accounts with secondary SID (SID History attribute). Each account will have two connector arrows, one to the left for its main SID, the other to the right for its secondary SID (with the mask icon). If the main SID is privileged, it will be in red, and if the SID history is privileged, there will be displayed as a red mask. You should search for the possible very risky situations, in which an account has a non-privileged main SID but at the same time has a privileged secondary SID. This scenario is very suspicious and you should check this account and investigate why it received a privileged secondary SID. Make sure it wasn’t added by a potential intruder in the network. * For a visualization convenience, if a large number of accounts with non-privileged SID history are present (more than ten), they will be filtered out from the display, as those accounts are less sensitive. 5. For manual examination of the scan results, unzip the saved zBang results file and check csv file: “[Path of the zBang’s unzipped results file]\SIDHistory\Results\Report.csv". 6. More details on abusing SID History are available in the blog post “Security Focus: sIDHistory” by Ian Farr: https://blogs.technet.microsoft.com/poshchap/2015/12/04/security-focus-sidhistory-sid-filtering-sanity-check-part-1-aka-post-100/ D. RiskySPNs scan In the scan results page, there will be a list of all the SPNs that registered with user accounts. If the user account is a privileged account, it will be in red. It is very risky to have SPNs that are registered under privileged accounts. Try and change/disable those SPNs. Use machine accounts for SPNs or reduce unnecessary permissions from the users who have SPNs registered to them. It’s also recommended to assign strong passwords to those users, and implement automatic rotation of each password. 4. For manual examination of the scan results, unzip the saved zBang results file and check csv file: “[Path of the zBang’s unzipped results file]\RiskySPN-master\Results\RiskySPNs-test.csv". 5. More details on Risky SPNs are available in the blog post “Service Accounts – Weakest Link in the Chain”: https://www.cyberark.com/blog/service-accounts-weakest-link-chain/ E. Mystique scan The scan result page includes a list of all the discovered accounts trusted with delegation permissions. There are three delegation types: Unconstrained, Constrained and Constrained with Protocol Transition. The account color corresponds to its delegation permission type. Disable old and unused accounts trusted with delegation rights. In particular, check the risky delegation types of “Unconstrained” and “Constrained with Protocol Transition.” Convert “Unconstrained” delegation to “Constrained” delegation so it will be permitted only for specific needed services. “Protocol Transition” type of delegation must be revalidated and disabled, if possible. 4. For manual examination of the scan results, unzip the saved zBang results file and check csv file: “[Path of the zBang’s unzipped results file]\Mystique-master\Results\delegation_info.csv". 5. More details on risky delegation configuration are available in the blog post - “Weakness Within: Kerberos Delegation”: https://www.cyberark.com/threat-research-blog/weakness-within-kerberos-delegation/ Performance zBang runs quickly and doesn’t need any special privileges over the network. As the only communication required is to the domain controller through legitimate read-only LDAP queries, a typical execution time of zBang on a network with around 1,000 user accounts will be seven minutes. When you intend to scan large networks with multiple trust-connected domains, it’s recommended to check the domain trusts configuration or run zBang separately from within each domain to avoid possible permission and connectivity issues. Authors zBang was developed by CyberArk Labs as a quick and dirty POC intended to help security teams worldwide. Feedback and comments are welcome. Main points of contact: Asaf Hecht (@Hechtov), Nimrod Stoler (@n1mr0d5) and Lavi Lazarovitz (@__Curi05ity__) Source: github.com

https://www.facebook.com/611317165628530/posts/2848816265211931/

te referi gen "Nu dezgheța fraierii că te inundă" Lasa man sa stie lumea realitatea la mine miroase a cauciuc incis si aici, si in Berceni , ca nu e nimeni Julian Assange

citeste tu! citez "long time ago" Da-il tu daca tot esti tare in gura, in primul rand l-am intrebat

Targets included government agencies in Beijing and Shanghai and Chinese diplomatic missions abroad. Foreign state-sponsored hackers have launched a massive hacking operation aimed at Chinese government agencies and their employees. Attacks began last month, in March, and are believed to be related to the current coronavirus (COVID-19) outbreak. Chinese security-firm Qihoo 360, which detected the intrusions, said the hackers used a zero-day vulnerability in Sangfor SSL VPN servers, used to provide remote access to enterprise and government networks. Qihoo said it discovered more than 200 VPN servers that have been hacked in this campaign. The security firm said that 174 of these servers were located on the networks of government agencies in Beijing and Shanghai, and the networks of Chinese diplomatic missions operating abroad, in countries such as: Italy United Kingdom Pakistan Kyrgyzstan Indonesia Thailand UAE Armenia North Korea Israel Vietnam Turkey Malaysia Iran Ethiopia Tajikistan Afghanistan Saudi Arabia India In a report published today, Qihoo researchers said the entire attack chain was sophisticated and very clever. Hackers used the zero-day to gain control over Sangfor VPN servers, where they replaced a file named SangforUD.exe with a boobytrapped version. This file is an update for the Sangfor VPN desktop app, which employees install on their computers to connect to Sangfor VPN servers (and inherently to their work networks). Qihoo researchers said that when workers connected to hacked Sangfor VPN servers, they were provided with an automatic update for their desktop client, but received the boobytrapped SangforUD.exe file, which later installed a backdoor trojan on their devices. DARKHOTEL HACKERS HAVE BEEN GOING AFTER COVID-19 TARGETS The Chinese security firm said it tracked the attacks to a hacker group known as DarkHotel. The group is believed to operate out of the Korean peninsula, although it is yet unknown if they are based in North or South Korea. The group, which has been operating since 2007, is considered one of today's most sophisticated state-sponsored hacking operations. In a report published last month, Google said that DarkHotel used a whopping five zero-day vulnerabilities last year, in 2019, more than any other nation-state hacking operation. Despite being only April, the Sangfor VPN zero-day is the third zero-day DarkHotel has deployed in 2020. Earlier this year, the group has also been seen using zero-days for the Firefox and Internet Explorer browsers to target government entities in China and Japan. Qihoo researchers said the recent attacks against Chinese government agencies could be related to the current coronavirus (COVID-19) outbreak. The Chinese security firm said it believes DarkHotel is trying to get insights into how the Chinese government handled the outbreak. The attacks on Chinese government entities appear to fit a pattern. Two weeks ago, Reuters reported a DarkHotel attack against the World Health Organization, the international body coordinating the global response to the current COVID-19 pandemic. PATCHES ARE ALREADY AVAILABLE Qihoo said it reported the zero-day vulnerability to Sangfor last Friday, on April 3. When ZDNet reached out for a statement earlier today, the Shenzen-based vendor didn't want to comment on the attacks, targets, or hackers, and instead redirected us to a WeChat post it published earlier in the day. On WeChat, the vendor said that only Sangfor VPN servers running firmware versions M6.3R1 and M6.1 were vulnerable and have been confirmed to have been compromised using the zero-day used by DarkHotel Sangfor said that patches would be coming today and tomorrow -- today for the current version of its SSL VPN server, and tomorrow for the older versions. The company also plans to release a script to detect if hackers have compromised VPN servers, and a second tool to removes files deployed by DarkHotel. Sangfor customers can find additional details in the company's WeChat post and its SRC-2020-281 security advisory (non-public). Via zdnet.com

acesta? PS: pe viitor pune @ in fața nicknames,

Mai hacherilor, de tot va dați cu presupusul, de ce nu lua-ti acces la dosarele clasate sa aflati adevarul? Koreea de nord, Area 51 sunt blurate in Google map, de ce?

Fiecare cu ipotezele lui, oricum e off-topic, king-coroana-rege v1, v2 Mor in fiecare zi sute de milioane de oameni, se nasc alte sute de milioane oameni, iar facem statistici la 5k de batrani care au murit de frig in ploaie in Anglia oricum intervine BOR ca vine pastele si tre sa-si scota banii pe catedrala mantuirii de la babele care fac inconjurul lumii in 4 labe, care e cat casa (poporului) + sf Parascheva Iasi, unde se aduna sute de mii de oameni la pelerinaj, ramane asa? catedrala aia face miliarde, bani investiti, bani pierduti?