The PerformanceNet/REMTCS' computing platform has been purpose built to accomplish its designated tasks in real-time in an environment with countless variables changing on an hourly.

The HPC platform makes use of specially designed hardware clusters that use general purpose computing on Graphical Processing Units (GPUs) and field programmable gate array (FPGA) cards. This design provides high performance computing without the use of supercomputing hardware. The developers’ vast experience with financial trading system and market data backgrounds allow them to continuously enhance the HPC platform. This has, thus far resulted in a system with an increase of 2000 - 2500% in performance coincident with the use of less power and less space than slower supercomputers.
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The use of virtual application containers in memory maximizes the efficiency of I/O sources. This results in end-to-end processing times thousands of times faster than standard CPUs writing and reading from disk. The REMTCS technology overcomes the high demand of conventional CPUs on the I/O system including network, storage, databases and other inputs/outputs.

The HPC hardware has been installed and operational on trading floors for over six (6) years without a single failure.
To defend against zero-day attacks, malware variants and unpermitted access to data, your cybersecurity solution must operate in real-time and utilize high speed computing and artificial intelligence.
ANNI software incorporates a neural network design that resembles the neurological connections in the brain. ANNI creates nodal connections as the learning or rendering process occurs. The rendering process builds the IT infrastructure and IT “use” profiles of the enterprise’s cyber environment. Every element of the IT infrastructure and its users and their use of IT become known to ANNI. ANNI immediately recognizes any anomalies to these profiles and acts on them to protect against a breach or misuse of IT resources.

Current system recognizing an attack or breach can sandbox or quarantine the attacking agent and notify or report out the attack. This allows the cybersecurity team to examine the attack and determine if it is a real threat and if it has caused a breach. Typically less than 5% of all anomalies or threats are assessed. This is due to the inability of the cyber team to deal with the large number of alerts that are received on a daily basis.

According to a Ponemon Institute study from January 2015, “The Cost of Malware Containment” there is an extrapolated average of 16,937 alerts per day for the average company in the study. Of these 3,218 (19%) are considered to be valid. Of these the cyber team selects 705 (22%) for analysis. This means that 96% of all alerts are ignored and 78% of valid threats are not examined. ANNI responds to and analyzes every attack, real or false is analyzed and responds and reported as necessary. ANNI even provides attribution of the source at an FBI evidentiary level. ANNI is the equivalent of 16 senior analysts working 24/7/365. It is more than likely that malware currently exists within your IT enterprise, but as yet has not been activated or recognized. Once malware is found, it takes cybersecurity vendors up to a year to update their systems, and therefore your system. ANNI automatically examines all alerts and takes appropriate action on each one. There is no need for the cyber team to determine which are valid and which require analysis. ANNI writes its own zero-day code which is the equivalent of “patching” itself as soon as the attack is recognized, analyzed and validated. Your systems are protected within 40 micro seconds of the attack and the breach is prevented. ANNI provides your organization with the equivalent of 16 of the most senior cyber analysts working 24/7/365 without coffee breaks and vacations or the possibility of leaving for a bigger paycheck.
Your cybersecurity system has a single, cumbersome algorithm to deal with all attacks. ANNI has over 190 algorithms designed to mimic the search-and-destroy behavior of human antibodies. The algorithms are stacked and sequenced allowing ANNI to “learn” so that variants to known threats as well as unknown or new threats are detected. ANNI’s software system uses open source Encog, Prolog, and other machine learning technologies in combination with PerformanceNet/REMTCS’ proprietary cluster, or combinatoric sets, of algorithms to achieve the optimal learning of patterns. (Combinatorics is a branch of mathematics concerning the study of finite or countable discrete structures.)

ANNI-PASS implementations can “learn” from each other. An attack launched and recognized at one implementation can be reported such that all ANNI-PASS implementations can be almost immediately updated. This reduces every implementation’s vulnerability to attack.
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