Invented by Charles F. Barry, Sumanta SAHA, Luminous Cyber Corp
The market for highly likely identification of related messages by sparse hash functions is growing rapidly, driven by the increasing demand for secure and efficient data processing. This market is expected to continue to expand in the coming years, as more companies and organizations seek out reliable methods of identifying related messages.
One of the key advantages of sparse hash functions is their ability to quickly and accurately identify related messages, even when dealing with large volumes of data. This makes them an ideal solution for applications such as fraud detection, network security, and data analytics, where the ability to quickly identify related messages can be critical to success.
Another advantage of sparse hash functions is their ability to work with a wide range of data types, including text, images, and video. This makes them a versatile solution that can be used in a variety of applications, from social media monitoring to video surveillance.
As the market for highly likely identification of related messages by sparse hash functions continues to grow, we can expect to see a range of new applications and use cases emerge. From financial services to healthcare, these functions are poised to play a critical role in the future of data processing and security.
Overall, the market for highly likely identification of related messages by sparse hash functions is a promising one, with significant potential for growth and innovation. As more companies and organizations seek out secure and efficient methods of identifying related messages, we can expect to see continued demand for these powerful tools.
The Luminous Cyber Corp invention works as follows
The disclosure includes methods, systems, apparatus, and software for network monitoring and analysis. The methods, apparatus, and systems for network monitoring perform highly probable identifications of related messages by using one or several sparse hash functions sets. A network monitoring and analysis system can trace the trajectory of the message as it traverses the network, and measure its delay between observation points using highly probable identification. A network monitoring and analytics systems can use the sparse hash value or identity to determine transit paths, transit times, entry points, exit points, and/or any other information regarding individual packets. They can also identify bottlenecks as well as broken paths, lost data and other network analytics using aggregated individual message data.
Background for Highly likely identification of related messages by sparse hash functions
In various embodiments computer-implemented systems and methods are disclosed. In one embodiment, an algorithm for computer-implemented methods comprises computing, by a processor using a first function, a hash of a message at multiple observation points in a network. The hash value of the message is calculated using the fields that are constant. The computer-implemented technique further comprises associating metadata with the hash values of the message, tracking the transit of the message across the network by the processor and generating one or more network analyses for the message. The associated metadata is used to generate the one or more network analyses.
In various embodiments computer-implemented systems and methods are disclosed. In one embodiment, an computer-implemented system comprises receiving by a processor a plurality metadata packets that correspond to a number of messages. Each metadata packet contains a sparse value hash. The computer-implemented technique also includes identifying by the processor a plurality matching sparse values. The plurality matching sparse values corresponds to at least one first message and one second message. The processor disambiguates the first and second messages in the computer-implemented technique. The metadata associated with a plurality of sparse values is used to disambiguate the first message from the second message.
The appended claims describe the novel features of the embodiments as described in this document. The following description taken together with the accompanying illustrations will help you better understand the embodiments in terms of organization and operation.
FIG. FIG. 6 shows one embodiment of derived meta data for an IPv4 package.
FIG. FIG. 9 shows one embodiment of incremental aggregated metadata.
FIG. FIG. 10.
Now, we will refer in detail to several embodiments including examples of implementations for systems and methods that monitor and analyze network traffic. In the figures, similar or similar reference numbers can be used to indicate functionality that is similar or similar. Figures are intended to illustrate only example embodiments. The following description will allow a person skilled in the arts to recognize that other example embodiments of structures and methods shown herein can be used without deviating from the principles described.
In various embodiments, methods and systems for network monitoring and analysis are disclosed. In certain embodiments, systems and methods of network monitoring and analysis include highly probable identifications of related messages by using one or multiple sparse hash functions sets. In some embodiments the systems and methodologies for network monitoring and analysis include metadata correlation and disambiguation. A network visualization display may be disclosed in some embodiments.
In various embodiments, methods and systems for network analytics and monitoring are disclosed. The network monitoring system can be scaled to track and monitor the transit of every message across a network. The network monitoring system can be scaled to monitor all message communication processes in and among virtual or physical servers, switches and routers, as well as between them, in and among datacenters. Statistic are collected on message transits, including but not limited, to message size, type, source(s) or originator(s), destinations, delay per observation pair, loss, location between which the loss has occurred, and transittopology.
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