Topio Networks

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Many financial-related Web3 applications emerged in Defi Summer 2020. But with the rise of concepts like NFT, Gamefi, and Metaverse, DApps have begun to develop from the field of Defi to images and games. Various decentralized applications have an increasing demand for storage. However, the existing blockchains have limited storage space and high cost, making it unsustainable to meet the growing need for storage. Comparison of blockchain storage costs As shown in the figure, the cost of storing 1GB of data on the Ethereum mainnet is approximately $73,000,000, and storage on Solana costs approximately $1 million per GB. Similarly, the cost of storing 1GB of data on Avalanche is $988,000. With the popularity of these Blockchains, the price will continue to rise. Due to the high cost of storing data on-chain, most DApps usually rely on centralized servers and cloud providers (such as AWS, Google Cloud, and Microsoft Azure) to store data. For example, due to the storage limitations of Ethereum, most NFT files on Ethereum are stored in IPFS or other centralized platforms. While FileCoin and Arweave are decentralized storage solutions, they do not come free of their own drawbacks. FileCoin is a complex system, and its storage services are significantly more expensive than their centralized counterpart (AWS, Google Cloud). Furthermore, Filecoin has a data storage market and a data extraction market, therefore, users are charged two fees. The storage fee gives users access to the data, while the extraction fee varies according to the market conditions for data extraction. This will also result in the user’s data access rights not being guaranteed. In Arweave’s case, It is difficult to deeply integrate Arweave into existing blockchains, since it is incompatible with EVM, and Dapps on other chains cannot easily migrate to it. As Web3 application growth increases the demands on blockchain performance, we are all waiting for a web3-focused, tailored-build blockchain. Topia Topia’s vision is to become the first ultra-high-performance decentralized world computer that deeply integrates distributed storage functionality to realize the mission of becoming the ultimate world computer; developing the infrastructure of WEB 3.0 applications realize ownership of personal data and be controlled by individuals; the best infrastructure for the metaverse; building a more decentralized world with true equality and freedom. Topia has a grand vision and its solutions include the deep integration of distributed storage and the improvement of on-chain transaction performance. Topia aims to be a world computer that is extremely friendly to developers and the user experience, with lower hardware requirements for operating node devices, supporting more nodes, hence resulting in the node distribution being more decentralized. 2.1 Deep integration of distributed storage The storage protocol is divided into the user end and the storage provider. The storage provider can be a local hard drive or any cloud provider. Topia will not provide our own infrastructure, rather cloud service providers, data centers, and idle storage and computing equipment of individuals and organizations will join and contribute to the Topia network by staking Topia tokens. This framework will distribute a certain amount of Topia token rewards according to the service provided, and it obtains a certain reputable value according to the quality of service provided. The process of the storage protocol works as follows: When a user initiates a storage data request (Write), the file is first divided into multiple chunks (the maximum size of each chunk is 256KB), and then a storage execution node is randomly selected according to the reputation of the storage provider. The execution node will select storage providers according to the reputation and resource status of each provider, pass these chunks to them, and then record the corresponding metadata on the chain. According to the storage proof mechanism, the storage worker will periodically submit the storage proof to the chain; If the storage proof-verification fails, the storage node will be punished. The storage costs: The storage fee is paid according to the size of the valid data stored by the storage provider. The minimum unit denomination is KB, data less than 1KB will be stored on-chain. Currently, the provision is 10 TOP/KB. The storage fee is paid according to the actual computing power consumed by the storage provider. Currently, 10 TOP/KTCP is temporarily specified. T opia deeply integrate distributed storage to meet the growing storage needs of decentralized applications and individual users. 2.2 Realize the personal ownership of data & privacy protection With the evolution of the Internet, personal identity has also changed: “Consumer” in the Web1 era, “Creator” in the Web2 era, and“Owner” in the era of Web3. In Gavin’s vision for Web3: “Web3 Foundation believes in an internet where: Users own their own data, not corporations Global digital transactions are secure Online exchanges of information and value are decentralized” （origin： https://web3.foundation/about/） The crypto industry has high expectations for Web3, hoping that the Web3.0 network remains open and transparent, and protects the ownership of personal data and privacy. As one of the most important core concepts in the Web 3.0 era, the ownership of personal data will also become a core feature of Web 3.0 applications. Topia will solve this issue for the ownership of personal data and privacy protection so that data is truly in the hands of individuals. 2.2.1 Decentralized database and DApp container In the service layer, Topia will provide a decentralized database and DApp container based on Topia distributed storage, which ensures that DApp data is stored in a user-controlled manner, and allow its own autonomy over its data. Each DApp corresponds to a DApp container, through which user data is stored in a location controlled by the users themselves. The DApp needs user authorization to read the data; each user has its own access to the DApp URL, which is passed through the decentralized DNS resolution. 2.2.2 Privacy Computing In addition to providing a decentralized database and Dapp container to ensure the ownership of personal data, Topia will also use the following means to protect the privacy of data: Zero-Knowledge Proofs, Homomorphic Encryption, Secure Multi-party Computation, Proxy Re-Encryption. Conclusion The core of web3 is owned by users. With the vigorous development of web3, the data generated by users will be massive. At present, no blockchains can bear such high storage costs. For example, Opensea, the largest NFT trading platform, stores metadata such as image information on a centralized server. Users’ NFT assets are at risk of loss once their server is maliciously attacked. There are even times when Opensea randomly removes users’ NFTs. These are all due to the fact that the user’s data storage security is not well addressed and guaranteed. If the storage security of data is not guaranteed, then web3 based on user-owned data will be at stake. Therefore, a high-performance blockchain with storage is an indispensable underlying infrastructure for the future web3 revolution. If Web3 is the next revolution in digitalization, then Topia, as the forerunner of this track, is a brand new step on the road to a utopian digital civilization. Other Reference Articles: Layer-1 Performance: Comparing 6 Leading Blockchains（CoinCodex） https://ethereum.org/en/developers/docs/web2-vs-web3/ https://web3.foundation/about/ https://arweave.news/the-beginning-of-arweave-is-the-end-of-ethereum丨-8btc-twitter-space-ama/ https://github.com/TopiaNetwork

The Blockchain Infrastructure that Caters to Web3.0 Needs

Many cybersecurity investigations can easily begin with a check on a domain of interest’s WHOIS record. It’s a simple way to find out more about who may be behind an attack. In this day and age, WHOIS records are increasingly redacted, however, that is likely not enough. Investigations need to consider a variety of other factors and sources of information. That is where consulting various come in. In fact, security analysts and researchers can use a combination of cyber threat intelligence feeds to enrich investigations and prioritize security events. threat intelligence feeds In this article, we explore examples that illustrate the use cases and benefits of leveraging cyber threat intelligence feeds. Manage Your Potential Attack Surface and Third-Party Risks with the Help of Threat Intelligence Feeds You can use a combination of cyber threat intelligence feeds that provide WHOIS, IP geolocation and netblocks, subdomain, website contact and categorization, and typosquatting data to do the following: Enumerate the domain names that contain your company or brand name, find out who owns these , and identify others that share the look-alike domains’ registrant email addresses aided by a WHOIS database. potential typosquatting domains Find out that make up your attack surface are located and who their Internet service providers (ISPs) are so they can help with possible takedowns aided by WHOIS and IP geolocation databases. where the IP addresses Identify the , who owns them, and if these have been reported for malicious activity using an IP netblocks WHOIS database. IP netblocks connected to suspicious domains Determine that may widen your potential attack surface with the help of WHOIS and passive DNS (pDNS) databases. other domains that share your IP addresses Check if that you did not create have been added to your infrastructure and monitor domains that contain your brand or company name in their subdomains with the help of a WHOIS database and subdomain data feeds. new subdomains Pinpoint the in your attack surface belong to and block these, if possible, using a WHOIS database and website contacts and categorization tools. categories the domains Look for and who their owners are aided by a WHOIS database and typosquatting data feeds. bulk-registered look-alike domains Analyzing Attack Surface and Third-Party Risks in Practice Back in 2017, PayPal’s new acquisition TIO Networks , possibly affecting 1.6 million of the payment processor’s customers. suffered from a breach We wanted to see if the perpetrators could have bulk-registered similar-looking domain names since the attack a few years ago. So we conducted a Domain Discovery analysis for the string “tionetworks” and . A Subdomain Discovery query, meanwhile, let us identify containing the string. identified 36 domains 4 subdomains More recently, a November 2020 Typosquatting Data Feed check for the strings “paypal” revealed at least , excluding misspelled variations. However, we did not identify any domain with the string “tionetworks” during that month. This indicates that suspicious bulk registrations in that case probably concerns the Paypal brand as a whole, but no longer relates to TIO networks specifically. 92 potential look-alike domains Subjecting those 92 domain names to a bulk WHOIS lookup told us that only six of these could be publicly attributed to PayPal. Any of the could theoretically figure in related attacks since ownership attribution is unclear. We also checked TIO Networks’s tionetworks[.]com domain’s WHOIS record details, and those match the record of its parent company Paypal since late 2017. 86 remaining domain names With the purpose of identifying websites that might currently be active, a Screenshot Lookup told us that 10 of the domains hosted content, although nine showed a 404 error page with a redirect button that does not seem to lead anywhere for now. The remaining domain led to a live (though likely parked) page. We looked at several of the pDNS records of the 86 non-publicly attributable domains and found that these resolved to at least 11 IP addresses that may be of questionable nature. Additionally, a Reverse IP/DNS Lookup showed that at least to the 11 IP addresses at some point in time. 2,769 domains resolved A closer look at these non-publicly attributable domains and connected IP addresses through VirusTotal helped us identify several properties flagged as “malicious” or “suspicious.” Enrich Your Security Platforms to Better Prioritize Alerts Aided by Threat Intelligence Feeds Alert fatigue is real and needs to be addressed as much as possible. Enhancing your security platforms’ capacity to reduce false alarms is one way to go about that. Platform access to WHOIS, IP geolocation and netblocks, pDNS, subdomain, website contacts and categorization, and typosquatting data can make the following possible: Know when a suspicious domain was created, who owned it before WHOIS redaction, and what other domains share its registrant email address or other details aided by a WHOIS database. Identify the host IP address of a suspicious domain, where it is located, and if its location is a fraud or cybercrime hotspot using WHOIS and IP geolocation databases. Determine what ranges an IP address belongs to and who manages it in case you want it taken down with the help of WHOIS and IP netblocks databases. Enumerate other domains hosted on an IP address and if these are all being flagged by your platform aided by WHOIS and pDNS data. Find out how many subdomains a suspicious domain has, when these were last and first seen according to pDNS data, and if the dates coincide with cyber incidents using a WHOIS database and subdomain data feeds. Obtain an offending website’s contact information and see if its domain falls under a blacklisted category with the help of WHOIS and site contacts and categorization data. Check if the domain was bulk-registered and with what aided by a WHOIS database and typosquatting data feeds. Better Security Alert Prioritization in Practice Every corporate IT security team typically keeps network logs to monitor for threats. So let us say in this example that a company’s network alerted its cybersecurity team to the 30 IP addresses in the list below. How should the team prioritize the said alerts? One way is by using an IP geolocation tool along with a list of the world’s top cybercrime hotspots, such as that kept by the . IP Geolocation Lookup would tell analysts that nine of the IP addresses point to commonly cited cybercrime hotspots. It might also be a cause for concern if the company does not serve areas where some of the IP addresses point to, and this information could be used for alert prioritization. Global Tech Council For more information about IP ranges, IP Netblocks API calls or Database access would give abused contact persons’ names, email addresses, and phone numbers. The IP addresses’ ISPs can also be found and contacted for owner identification or takedown requests if needed. Consulting an open-source intelligence (OSINT) resource like AbuseIPDB would also help confirm suspicions. Once done, security analysts would find that 13 of the flagged IP addresses are spotted as malicious. From there, analysts can widen the scope of their investigation by searching for potentially related domains using a reverse IP/DNS search tool. Doing so for the 13 IP addresses would provide a list of 312 domains. --- These are just two of several ways by which threat intelligence feeds can help organizations enrich their investigations and prioritize events. Cyber threat intelligence feeds can also prove useful in other cybersecurity applications, including phishing and brand protection, and threat hunting.

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