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  1. Amsterdam's cybercrime police team made a significant breakthrough in their investigation into hacking, data theft, blackmail, and money laundering by arresting three young men. The suspects, aged 21 and 18, were picked up on January 23, and two of them have been restricted to contact with their lawyers only in the interest of the ongoing investigation. The justice department spokesperson revealed that one of the three suspects is a "very clever hacker" who had already come under the police radar before. "Data is the new gold, and these are the new bank robbers," the spokesperson added, highlighting the significance of the case. The investigation began in March 2021 when a large Dutch company reported a hack. However, since then, the police discovered that thousands of small and large companies, both national and international, have fallen victim to hacking and data theft. As a result, the privacy-sensitive information of tens of millions of people, including their names, addresses, credit card details, dates of birth, and BSN numbers, ended up in the hands of criminals. Several companies that fell victim to the hack include Ticketcounter, an online amusement park, and zoo ticket vendor, a major educational institution, and a meal delivery service. The hackers gained access to these companies' systems and sent a threatening email demanding payment in bitcoin. If the companies didn't pay, the hackers threatened to destroy their digital infrastructure or publish the stolen information. Shockingly, many companies paid up, fearing the consequences of non-compliance. According to RTL Nieuws, the main suspects are believed to have had a "criminal income" of €2.5 million. It's also worth noting that the January arrests followed the arrest of a 25-year-old man from Almere in November. He was found to have databases in his possession that the police were already aware of following reports of data hacks. The suspects allegedly knew each other from online forums and chat services such as Telegram, where they exchanged tips and offered each other services. The Dutch government is now considering giving a more prominent role to data security organizations such as DIVD or Dutch Institute for Vulnerability Disclosure in tackling cybercrime. The police have made significant progress in this case, but investigations are still ongoing. The case highlights the need for greater vigilance in protecting personal data, particularly with the rise of cybercrime.
  2. ORBIT Blockchain Transactions Investigation Tool Introduction Orbit is designed to explore network of a blockchain wallet by recursively crawling through transaction history. The data is rendered as a graph to reveal major sources, sinks and suspicious connections. Usage Let's start by crawling transaction history of a wallet python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F Crawling multiple wallets is no different. python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F,1ETBbsHPvbydW7hGWXXKXZ3pxVh3VFoMaX Orbit fetches last 50 transactions from each wallet by default, but it can be tuned with -l option. python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -l 100 Orbit's default crawling depth is 3 i.e. it fetches the history of target wallet(s), crawls the newly found wallets and then crawls the wallets in the result again. The crawling depth can be increased or decresead with -d option. python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -d 2 Wallets that have made just a couple of interactions with our target may not be important, Orbit can be told to crawl top N wallets at each level by using the -t option. python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -t 20 If you want to view the collected data with a graph viewer of your choice, you can use -o option. python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -o output.graphml Support Formats graphml (Supported by most graph viewers) json (For raw processing) This is your terminal dashboard. Visualization Once the scan is complete, the graph will automatically open in your default browser. If it doesn't open, open quark.html manually. Don't worry if your graph looks messy like the one below or worse. Select the Make Clusters option to form clusters using community detection algorithm. After that, you can use Color Clusters to give different colors to each community and then use Spacify option to fix overlapping nodes & edges. The thickness of edges depends on the frequency of transactions between two wallets while the size of a node depends on both transaction frequency and the number of connections of the node. As Orbit uses to render the graph, more information about the various features and controls is available in Quark's README. Download: [Hidden Content]
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