PCWorld reports that CISA has flagged four actively exploited security vulnerabilities affecting Windows, VMware vCenter, Microsoft SharePoint, and Apple macOS systems. Critical flaws carry severity scores as high as 9.8 out of 10, with attackers using them to deploy ransomware, install Monero mining malware, and bypass authentication entirely. Users and administrators are urged to apply available patches immediately, including recent Windows, SharePoint, VMware, and macOS updates, to avoid becoming targets. The Cybersecurity and Infrastructure Security Agency (CISA) has issued a warning about four security vulnerabilities that are being exploited by attackers in the wild. CISA has added the vulnerabilities to its catalog of Known Exploited Vulnerabilities (KEV) . Microsoft Windows and SharePoint, VMware vCenter, and Apple macOS are affected. Inclusion in the KEV catalog sends an important signal: CISA doesn't simply list every known vulnerability out there, but only those for which there is concrete evidence of active exploitation by attackers. The agency points out that such vulnerabilities are among the most commonly used attack vectors and pose a significant risk.

Safety Net Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Hub Versus Say More Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Deal pricing and availability subject to change after time of publication. The BlockChance Bitcoin Ticket Super Miner lets you participate in real solo Bitcoin mining with a quiet, plug-and-play desktop device for $49.99 (reg. You don't need a room full of noisy hardware -- or a stress-inducing electricity bill -- to see how Bitcoin mining works.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Versus Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Deal pricing and availability subject to change after time of publication. The Keysmart SmartCard is a compact tracker that can fit in your wallet, and it's on sale for $31.99. You can try as hard as you want, you probably aren't going to get an AirTag into your wallet in a way that looks normal.

Since its 2009 genesis block, the Bitcoin network has processed >1.08 billion (B) transactions representing >8.72BBTC, offering rich potential for machine learning (ML); yet, its pseudonymity and obscured flow of funds inherent in its UTxO-based design, have rendered this data largely inaccessible for ML research. Addressing this gap, we present an ML-compatible graph modeling the Bitcoin's economic topology by reconstructing the flow of funds. This temporal, heterogeneous graph encompasses complete transaction history up to block 863000, consisting of >2.4B nodes and >39.72B edges. Additionally, we provide custom sampling methods yielding node and edge feature vectors of sampled communities, tools to load and analyze the Bitcoin graph data within specialized graph databases, and ready-to-use database snapshots. This comprehensive dataset and toolkit empower the ML community to tackle Bitcoin's intricate ecosystem at scale, driving progress in applications such as anomaly detection, address classification, market analysis, and large-scale graph ML benchmarking.

The proposed research on pre-training temporal graph neural networks across multiple networks has the potential to advance the field of machine learning and its applications significantly. By introducing methodologies to enhance the scalability and transferability of TGNNs, this work could revolutionize areas like network security, financial fraud detection, and real-time social network analysis, where dynamic and adaptive models are essential. The publicly available dataset of 84 Ethereum-based temporal networks will serve as a valuable resource for the research community, fostering innovation and collaboration. Furthermore, the principles of multi-network pre-training introduced here can inspire analogous advances in other temporal data domains, such as healthcare, transportation, and climate science. This research opens up a new direction in training generalizable temporal graph models that, for the first time, can be trained on distinct temporal networks, paving the way for Temporal Graph Foundation Models. This work also introduces a set of Ethereum transaction token networks, which are publicly available to users who have the necessary resources, such as fast SSDs, large RAM, and ample disk space, to synchronize Ethereum clients and manually extract blocks. Additionally, all Ethereum data is accessible on numerous Ethereum explorer sites such as etherscan.io. An Ethereum user's privacy depends on whether personally identifiable information (PII) is associated with any of their blockchain address, which serves as account handles and are considered pseudonymous. If such PII were obtained from other sources, our datasets could potentially be used to link Ethereum addresses. However, real-life identities can only be discovered using IP tracking information, which we neither have nor share. Our data does not contain any PII. Furthermore, we have developed a request to exclude an address from the dataset. Benchmark datasets have become fundamental for advancing graph machine learning, providing a common ground to evaluate models and facilitate the development of graph foundation models. Early graph ML studies often relied on a handful of small, static benchmark graphs (e.g., citation networks like Cora/Citeseer and molecular graphs from the TU collection [37]).

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . China's brain chip breakthrough raises big questions Should you change your phone number after a hack? McDonald's AI drive-thru may take your next order The Father's Day gift that protects your dad from scammers New Air Force One'flying palace' gifted by Qatar unveiled for President Trump Kevin O'Leary warns U.S. must accelerate data center growth to keep pace with China in AI race Americans need protection against'warrantless surveillance': Rep Chip Roy Spencer Pratt's use of AI to boost campaign sparks debate China approves world's first commercial brain chip Crypto kiosks helped scammers turn fear into fast cash.

We propose BlockScan, a customized Transformer for anomaly detection in blockchain transactions. Unlike existing methods that rely on rule-based systems or directly apply off-the-shelf large language models (LLMs), BlockScan introduces a series of customized designs to effectively model the unique data structure of blockchain transactions. First, a blockchain transaction is multi-modal, containing blockchain-specific tokens, texts, and numbers. We design a novel modularized tokenizer to handle these multi-modal inputs, balancing the information across different modalities. Second, we design a customized masked language modeling mechanism for pretraining the Transformer architecture, incorporating RoPE embedding and FlashAttention for handling longer sequences. Finally, we design a novel anomaly detection method based on the model outputs.

Pump.Fun's Bounties Platform Is a Black Hole of Circular Grifting The crypto platform claims you can "pay anyone to do anything," from quitting a job on camera to getting a memecoin-themed tattoo. But it mostly seems like people trying to scam each other. Would you run into a crowded university lecture hall, fart into a megaphone, and bellow "fartcoin" at the top of your lungs? If so--and should you have the means to document this stunt on video, preferably capturing the audience's reaction--you may claim a reward of approximately $1,000 . The money, of course, will be dispensed in fartcoin, a meme cryptocurrency trading at a little over 10 cents at time of publication, with a total market capitalization hovering around $130 million. Such is the promise of Pump.Fun GO, a new feature on Pump.Fun, one of the fastest-growing crypto businesses of the past few years.

They swear they haven't peeked at the closely guarded secret and that they'll keep the cryptographic competition going. On a blustery March day, the artist Jim Sanborn received visitors at his studio on an isolated island in the Chesapeake Bay. The visitors sat him down in front of a laptop, and he typed in a secret message. They compressed the message using a unique hash function, sent that to the cloud, and wiped the laptop clean. Sanborn hoped that this action would set him free.

Since its 2009 genesis block, the Bitcoin network has processed >1.08 billion (B) transactions representing >8.72B BTC, offering rich potential for machine learning (ML); yet, its pseudonymity and obscured flow of funds inherent in its UTxO-based design, have rendered this data largely inaccessible for ML research. Addressing this gap, we present an ML-compatible graph modeling the Bitcoin's economic topology by reconstructing the flow of funds. This temporal, heterogeneous graph encompasses complete transaction history up to block 863000, consisting of >2.4B nodes and >39.72B edges. Additionally, we provide custom sampling methods yielding node and edge feature vectors of sampled communities, tools to load and analyze the Bitcoin graph data within specialized graph databases, and ready-to-use database snapshots. This comprehensive dataset and toolkit empower the ML community to tackle Bitcoin's intricate ecosystem at scale, driving progress in applications such as anomaly detection, address classification, market analysis, and large-scale graph ML benchmarking. Dataset and code available at https://github.com/B1AAB/EBA.