MQTT data encryption transfer algorithm An improved MQTT protocol data transfer encryption algorithm MQTT-EA is proposed. In this algorithm, the IoT device side and the server-side randomly generate their private keys, then notify each other of their private keys and combine them into the final session master key through the algorithm, which is encrypted and decrypted by DES to transmit secure data. The attack on the data transfer process by adversaries A and B is simulated, and it is verified that MQTT-EA is safe under the premise that the session key generation algorithm is not leaked. Summary of key protocols and application scenarios of time-sensitive networks With the development of information technology, there is an increasing demand for scenarios where things and things are the main body of communication, such as factory automation control, automatic driving, etc. The requirements for data transfer delay of this type of communication far exceed the controllable range of traditional Ethernet. , The time-sensitive network came into being. Time-sensitive networks are based on standard Ethernet, providing standardized technologies for deterministic information transfer, minimizing jitter through time-aware scheduling mechanisms, and providing reliable data transfer guarantees for time-sensitive applications. Through the description of relevant international standards for time-sensitive networks, the core features and mechanisms are introduced, and application scenarios such as in-vehicle networks, industrial Internet, avionics networks, and mobile fronthaul networks are analyzed and researched. Design of LoRa's Remote Distributed Agricultural Environment Monitoring System To solve the problems of complex networking of traditional Internet of Things, short transfer distance, and high power consumption, and agricultural environment monitoring system based on LoRa technology is proposed. The system uses the peripheral functions of the STM32 microcontroller to drive the sensors to monitor a variety of environmental data and uses the LoRa wireless communication module to build a data transfer network. The summary node in the data transfer network receives all the data transmitted from the monitoring node, then packs the data and uploads it to the server through the General Packet Radio Service communication network. The upper computer developed by the C language can realize the monitoring data Real-time display and save. After testing, the system can accurately monitor agricultural environmental data in real-time, is stable and reliable, and can meet the needs of agricultural environmental monitoring.
If you just recorded a home video or created the final hybrid tape , then you will undoubtedly be eager to share it with your friends and family. Depending on the size and number of files you need to send, this may be a problem. For example, Gmail only allows you to attach files up to 25MB to emails. Not to mention the fact that large files lurking in the "Sent" folder will quickly take up your storage space quota! If you need to send large files online, there are many good ways to avoid trouble-and we highlight the 12 best files, most of which are free . Cloud storage veteran IDrive can provide a large amount of storage online, but the cost is incredibly small. The price of 5TB for the first year is $3.48, which is unparalleled so far, as is the support for unlimited devices and the extensive file version control system. ExpressVPN can get Backblaze unlimited cloud storage service for free. ExpressRa is the number one VPN provider in TechRadar. When you sign up for an annual VPN subscription, Backblaze unlimited cloud storage service will be provided for free throughout the year. After uploading large files to the cloud, it becomes easier to share large files online. 1. Use VPN What is the relationship between VPN and sharing large files? Some Internet service providers use broadband traffic management to regulate upload bandwidth . Using a VPN as our first choice ExpressVPN means that your ISP cannot determine the type of file you are uploading, so in theory, traffic shaping cannot be applied to your account. P2P is one of the most popular and reliable methods of moving large amounts of data, and it is the kind of content that is most likely to be flagged and lower priority. We have compiled a list of the best VPN services available. Keep in mind that your mileage will vary, and using a VPN will also slow down your connection. 2. Use specialized services The new generation of file transfer service is browser-based and has built-in proprietary technology to accelerate the upload of large files. Raysync is one of them and specializes in transferring large files via the cloud. It offers a pay-as-you-go pricing model, with a cost of $140 per 1TB downloaded. There are no subscription fees, no contracts, no support fees, user restrictions, or file size/bandwidth restrictions. Although Raysync and similar services are more expensive than traditional file transfer services, they are far faster and more flexible than Dropbox or Google Drive. You can try Raysync for free for 14 days with the 100GB test 3. Use file compression One of the easiest ways to solve the problem of sending large files is to use file compression software, such as the cross-platform program 7-Zip. This is especially convenient if you have multiple files because you can put them in a folder and then compress them all at once. According to experience, the transfer speed of large files is faster than the transfer speed of folders containing small files of the same size. 7-Zip is suitable for Windows, Mac, and Linux, and can compress files into regular ZIP format, and it's slightly more efficient than 7ZIP. Most major operating systems can extract ZIP files without any other software. 7-Zip also allows you to set a password to protect the files, so you can share them safely. Please keep in mind that although uploading very large files may time out. 4. Express 20TB external hard drive The fastest way to transfer a large number of large files is not via the Internet, but to use disk drives and messengers. All large cloud providers can use hard drives to transfer large amounts of data. Microsoft Azure charges a nominal fixed fee, which means that it costs approximately US$75 per storage device, but you must be prepared to provide your drives. This is similar to Amazon Web Services' import/export disk, while Google uses a third party. The WD My Book Duo external hard drive is priced at $699.99 and has a capacity of 20TB , making it the largest and most cost-effective device in its class. On a consumer-grade broadband line, it will take more than 500 hours to transmit the contents of a 20TB external hard drive on a 100Mb dedicated line, and it is expected to last more than a month and is only used for uploading. Remember to keep a copy of the file and encrypt the hard drive to be sent. 5. Google Drive Although Gmail messages can only contain attachments up to 25MB, when the file is too large, Google can give you the option to place it in Google Drive and send a link for sharing. Gmail users can share files and folders up to 10GB. Considering that Google's free tier can provide you with 15GB of storage space, so you can share large files repeatedly for absolutely free . Google allows you to choose to create a link that can be shared with anyone or just to create a link to the person who sent you an email with the link. The premium plan starts at $1.99 per month and can store 100GB of storage space. 6. FTP Although FTP may be outdated compared to cloud services such as Dropbox and Google Drive, it is still one of the most reliable methods for uploading and downloading files. All operating systems support FTP, and many websites and add-ons support uploading and downloading from your browser, such as FireFTP. Windows and Mac users can also use the free desktop FTP client Cyber duck. The only disadvantage is that you need to have access to remote servers . Many companies like DriveHQ provide some free storage space , and the price is comparable to cloud storage providers. 7. Mediafire True to its name, MediaFire is a pioneer. Sign up for a free account and you will get 10GB of storage space. Connect your Facebook and Twitter accounts, install mobile apps, and refer friends to get up to 40GB of bonus space. You can upload files directly from your computer or the web and generate a link that will allow others to download the file from the MediaFire website. The paid subscription costs US$3.75 per month and includes 1TB of storage space, a 20GB file size limit, and the elimination of annoying verification codes and advertisements. Another convenient advanced feature is a one-time link, which ensures that once the recipient downloads the files, they can no longer be accessed. 8.Hightail Hightail has considered the needs of business users. After registration, you can create special "spaces" for various files and projects, which can then be shared with others. The convenient "PipPoints" function can even be used to take notes on documents while you and others are working on them. The free Lite version of Hightail only allows files up to 100MB to be shared. The Pro subscription is priced at US$12 per month, which includes unlimited workspace and supports files up to 25GB. There is also no limit to the number of people who can access a file at any given time. 9. WeTransfer WeTransfer is one of the simplest services for sharing large files. With just a few clicks, the website will automatically send you the files, which can be downloaded for 7 days. Everything is also very user-friendly, with a step-by-step wizard to guide you through the upload process. You can transfer up to 2GB on a free account, but for US$12 per month or US$120 per year , you can upgrade to WeTransfer Plus, which can transfer up to 20GB of files at a time, and 1TB Storage. You can also choose to set a password to download the file-in addition, you can customize the background and email as needed. 10. Resilio synchronization This handy tool used to be BitTorrent Sync, which uses the BitTorrent protocol and can directly synchronize files between devices. This peer-to-peer connection can be used for two or more devices, such as phones and desktop PCs. Resilio Sync also supports generating secure links to allow your contacts to download files from your folder. This naturally means that your device must be online at the time to be accessible. The software itself is provided for free, and there is no limit to how much data you can transfer or store. Please note that only "Personal Sync" is free. Sync Home adds more features and is priced at US$60 , while Sync for Family can accommodate up to 5 family members and retails for US$100 . 11.Send Anywhere Send Anywhere is suitable for almost every platform you can think of and can transfer files up to 10GB completely free. The file-sharing service is available through the following URL: https://send-anywhere.com web applications, Chrome browser extensions, mobile applications for Android and iOS, and downloadable software for Windows and macOS. There are also versions for Linux and Amazon Kindle, as well as plugins for WordPress and Outlook. The browser widget allows you to share files up to 10GB, but the paid service allows you to share files without restrictions. 12.Dropbox Sign up for this cloud storage service and you can share all files moved into the Dropbox folder using a web link. Some operating systems allow you to do this by right-clicking, while other operating systems may require you to log in to the site and click the "share" link. Most importantly, the person you send the link to does not have to be a Dropbox user-they just need to download the file from the site. Dropbox has a free tier that gives you 2GB of storage space, but you can earn more by referring friends to use the service-or by signing up for Dropbox Plus for $9.99 a month to increase the limit to 2TB. The latter also allows you to better control files, including version control and remote device wipe and can set download passwords. To find a flexible solution with security and speed, cloud storage is the only way to go. Cloud solutions such as Raysync, Google Drive, Dropbox, and the like provide amazingly flexible pricing and even free storage. We, at Raysync, are committed to providing you with the latest information about the best content on the latest fashion topics in any major field. To learn more about Raysync's different needs, please click here.
The ability to effectively use big data to gain value comes down to the ability of organizations to run analytical applications on the data, usually in the data lake. Assume that the challenges of capacity, speed, diversity, and accuracy are solved-measuring data readiness. The data is ready to pave the way for predictive analysis. Data readiness is built on the quality of the big data infrastructure used to support business and data science analysis applications. For example, any modern IT infrastructure must support data migration associated with technology upgrades, integrated systems, and applications, and can transform data into required formats and reliably integrate data into a data lake or enterprise data warehouse ability. Three big challenges facing big data technology So why do so many big data infrastructures collapse early in the implementation life cycle? All this goes back to the last of McKinsey’s big data offer in 2011: “As long as the right policies and driving factors are formulated”. Some reasons why big data projects cannot be started are as follows: 1. Lack of skills Despite the increase in machine learning, artificial intelligence, and applications that can run without humans, the imagination to drive big data projects and queries is still a data scientist. These "promoters" referred to by McKinsey represent skills that are in great demand on the market and are therefore rare. Big data technology continues to affect the recruitment market. In many cases, big data developers, engineers, and data scientists are all on-the-job, learning processes. Many high-tech companies are paying more and more attention to creating and training more data-related positions to use the principles of big data. It is estimated that by 2020, 2.7 million people will be engaged in data-related jobs, and 700,000 of them will be dedicated to big data science and analysis positions to highly competitive and expensive employees. 2. Cost The value of the big data analytics industry is nearly $125 billion, and it is only expected to grow. For big data implementation projects, this means expensive costs, including installation fees and regular subscription fees. Even with the advancement of technology and the reduction of barriers to entry, the initial cost of big data may make the project impossible. Investment may require traditional consulting, outsourcing analysis, internal staffing, and storage and analysis software tools and applications. Various cost models are either too expensive, or provide the functionality of the minimum viable product, and cannot provide any actual results. But first, a company that wants to properly implement big data must prioritize architecture and infrastructure. 3. Data integration and data ingestion Before performing big data analysis, data integration must be performed first, which means that various data need to be sourced, moved, transformed, and provisioned into big data storage applications using technologies that ensure security. Control during the entire process. Modern integration technologies that connect systems, applications, and cloud technologies can help organizations produce reliable data gateways to overcome data movement problems. Companies striving to modernize their systems and deploy strategies to integrate data from various sources should tend to adopt a B2B-led integration strategy that ultimately drives the development of partner ecosystems, applications, data storage, and big data analysis platforms To provide better business value.
Facing the challenges and threats related to the security of big data transmission, the industry has conducted targeted practices and investigations on security protection technologies. This article focuses on three aspects of the development of big data security technology: platform security, data security, and privacy protection. Technologies related to platform security, data security, and privacy protection are improving, allowing us to solve big data security issues and challenges. However, to respond to new methods of cyber attacks, protect new data applications, and meet increased privacy protection requirements, higher standards and functions will be required. In terms of platform technology, centralized security configuration management and security mechanism deployment can meet the security requirements of the current platform. However, vulnerability scanning and attack monitoring technologies for big data platforms are relatively weak. In terms of technologies for defending platforms from network attacks, current big data platforms still use traditional network security measures to defend against attacks. This is not enough for the big data environment. In the big data environment, the extensible defense boundary is vulnerable to attack methods that cover up the intrusion. Besides, the industry pays little attention to potential attack methods that may come from the big data platform itself. Once new vulnerabilities appear, the scope of the attack will be huge. In terms of data security, data security monitoring and anti-sabotage technologies are relatively mature, but data sharing security, unstructured database security protection, and data violation traceability technologies need to be improved. Currently, there are technical solutions for data leakage: technology can automatically identify sensitive data to prevent leakage; the introduction of artificial intelligence and machine learning makes the prevention of violations move toward intelligence; the development of database protection technology also provides a powerful way to prevent data leakage Guarantee. The ciphertext calculation technology and the data leakage tracking technology have not yet been developed to the extent that they can meet the needs of practical applications, and it is still difficult to solve the confidentiality assurance problem of data processing and the problems related to tracking data flow. Specifically, the ciphertext calculation technology is still in the theoretical stage, and the calculation efficiency does not meet the requirements of practical applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications; data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications; data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. Digital watermarking technology cannot meet the needs of large-scale and fast-updated big data applications; data lineage tracking technology requires further application testing and has not yet reached the mature stage of industrial applications. In terms of privacy protection, technological development clearly cannot meet the urgent need for privacy protection. The protection of personal information requires the establishment of a guarantee system based on legal, technical, and economic methods. Currently, the widespread use of data desensitization technology poses challenges to multi-source data aggregation and may lead to failure. So far, there are few practical application case studies for emerging technologies such as anonymization algorithms, and there are other common problems with this technology, such as low computational efficiency and high overhead. In terms of computing, continuous improvement is needed to meet the requirements of protecting privacy in a big data environment. As mentioned earlier, the conflict between big data applications and personal information protection is not just a technical issue. Without technical barriers, privacy protection still requires legislation, strong law enforcement, and regulations to collect personal information for big data applications. Establish a personal information protection system that includes government supervision, corporate responsibility, social supervision, and self-discipline of netizens.
Big data transfer is becoming a new driving force for economic and social development, and is increasingly affecting economic operations, lifestyles and national governance capabilities. The security of big data transfer has been improved to the level of national security. Based on the challenges and problems facing big data transfer security and the development of big data security technology, we put forward the following 5 opinions for the development of big data security technology. 1. Build an integrated big data security defense system from the perspective of an overall security Security is a prerequisite for development. It is necessary to comprehensively improve the security of big data security technology, and then establish a comprehensive three-dimensional defense system that runs through the cloud management of big data applications to meet the needs of both countries. Big data strategy and it's market application. First, it is necessary to establish a security protection system covering the entire data life cycle, from collection to transfer, storage, processing, sharing, and final destruction. It is necessary to fully utilize data source verification, encryption of large-scale data transfer, encrypted storage in non-relational databases, privacy protection, data transaction security, prevention of data leakage, traceability, data destruction, and other technologies. The second is to enhance the security defense capabilities of the big data platform itself. It should introduce authentication for users and components, fine-grained access control, security audits for data operations, data desensitization, and other such privacy protection mechanisms. It is necessary to prevent unauthorized access to the system and data leakage while increasing attention to the inherent security risks involved in the configuration and operation of big data platform components. It is necessary to enhance the ability to respond to emergency security incidents that occur on the platform. Finally, it uses big data analysis, artificial intelligence, and other technologies to automatically identify threats, prevent risks and track attacks, and transition from passive defense to active detection. Ultimately, the goal is to enhance the security of big data from the bottom up and enhance the ability to defend against unknown threats. 2. Starting from attack defense, strengthen the security protection of big data platforms Platform security is the cornerstone of big data system security. From an earlier analysis, we can see that the nature of cyberattacks against big data platforms is changing. Enterprises are facing increasingly serious security threats and challenges. Traditional defensive surveillance methods will find it difficult to keep up with this change in the threat landscape. In the future, research on the security technology of big data transfer platforms should not only solve operational security issues but also design innovative big data platform security protection systems to adapt to the changing nature of cyber attacks. In terms of security protection technology, both open source and commercial big data platforms are in a stage of rapid development. However, the cross-platform security mechanism still has shortcomings. At the same time, the development of new technologies and new applications will reveal platform security risks that are not yet known. These unknown risks require all parties in the industry to start from the offensive and defensive aspects, invest more in the security of the big data platform, and pay close attention to the development trend of big data network attacks and defense mechanisms. It is necessary to establish a defense system suitable for and build a more secure and reliable big data platform. 3. Use key links and technologies as breakthrough points to improve the data security technology system In the big data environment, data plays a value-added role, its application environment is becoming more and more complex, and all aspects of the data life cycle are facing new security requirements. Data collection and traceability have become prominent security risks, and cross-organizational data cooperation is extensive, leading to confidentiality protection requirements that trigger multi-source aggregate computing. At present, technologies such as sensitive data identification, data leakage protection, and database security protection are relatively mature, while confidentiality protection in multi-source computing, unstructured database security protection, data security early warning, emergency response, and traceability of data leakage incidents, Still relatively weak. Actively promote the development of industry-university-research integration, and accelerate the research and application of key technologies such as ciphertext calculations to improve computing efficiency. Enterprises should strengthen support for data collection, calculation, traceability, and other key links; strengthen data security monitoring, early warning, control, and emergency response capabilities; take data security key links and key technology research as a breakthrough; improve the big data security technology system; To promote the healthy development of the entire big data industry. 4. Strengthen the investment in the industrialization of privacy protection core technologies, while considering the two important priorities of data use and privacy protection In the big data application environment, data usage and privacy protection will naturally conflict. Homomorphic encryption, secure multi-party computing, and anonymization technologies can strike a balance between the two and are ideal technologies to solve the privacy challenges in the application of big data. The advancement of core privacy protection technologies will inevitably greatly promote the development of big data applications. Currently, the core problem of privacy protection technology is efficiency, and its problems include high computing costs, high storage requirements, and lack of evaluation standards. Some researches, in theory, have not been widely used in engineering practice. It is very difficult to deal with privacy security threats such as multiple data sources or statistics-based attacks. In the big data environment, personal privacy protection has become a topic of much concern, and with the increasing demand for privacy protection in the future, it will drive the development and industrial application of dedicated privacy protection technologies. To improve the level of privacy protection technology in the big data environment, we must encourage enterprises and scientific research institutions to study privacy protection algorithms such as homomorphic encryption and secure multi-party computing, and at the same time promote data desensitization, audit applications, and other technical methods. 5. Pay attention to the research and development of big data security review technology and build a third-party security review system At present, the state has formulated a series of major decision-making arrangements for big data security. The government promotes the deep integration of big data and the real economy and emphasizes the need to effectively protect national data security. The National Informatization Plan puts forward an implementation plan for the big data security project. It is foreseeable that the government's supervision of big data security will be further strengthened in the future, the legislative process related to data security will be further accelerated, big data security supervision measures and technical means will be further improved, and the disciplinary work of big data security supervision will be further strengthened.
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