Cloudbric: Integrating Deep Learning with Crypto Security Ecosystem

In my previous article, I painted an overall picture of the security landscape in the cryptocurrency ecosystem and how Cloudbric is building a revolutionary next-gen solution to tackle the inherent and emerging security challenges of the crypto landscape. To summarize my previous article, Cloudbric is building a disparate solution that would tackle the triumvirate of - Oversaturation of Security Solutions, Centralization of Threat Intelligence and Uncertainty of Security Performance. At the heart of the solution being offered by Cloudbric, lies the disruptive Deep Learning technology. In this article, I will dwell deep into the technical dynamics of Deep Learning technology and how Cloudbric’s patented deep learning model known as Vision will strike at the keystone of the ever-evolving security threats of the cryptocurrency ecosystem.

Deep Learning: Genesis of a neural revolution

While the moorings of deep learning can be traced in bits and pieces to the decades of the 1960s and 70s in the works of several researchers, the actual roots sprouted in 1980s. It was Sejnowski, along with his group of researchers, who challenged the prevailing logic-and-symbol based version of AI. The term was officially introduced to the Machine Learning community in the year 1986 by Rina Dechter, a professor of Computer Science in the Donald Bren School of Information and Computer Sciences at University of California, Irvine. In the year 2000, Igor Aizenberg (who was then working as Chief Research Scientist and VP Research at Neural Networks Technologies, Israel) along with his colleagues introduced the concept to artificial neural networks in the larger context of Boolean threshold neurons.

The first decade of the 21st Century witnessed advancement and further research in deep learning at the hands of several researchers including Geoff Hinton, Ruslan Salakhutdinov, Osindero, and Teh. In 2009, Nvidia propelled what is known as the “big bang” of deep learning by training deep-learning neural networks with Nvidia graphics processing units, thus setting the pace for the emerging boom in this technology. The revolution, actually, broke out in 2012, in the healthcare sector, when a research team led by George E. Dahl won the prestigious “Merck Molecular Activity Challenge” using deep neural networks to dynamically predict the biomolecular delivery targets for a drug. Thereafter, deep learning went on from being an arcane technology to a disruptive technology finding adoption in almost all sectors that we can count.

It should not come as a surprise to acknowledge the fact that our day-to-day life is surrounded by this technology. Our technology dependencies that include Google Translate, and voice-based intelligent assistants Siri and Alexa are powered by this very technology. In fact, early adopters of this technology are reaping in enormous monetary profits by leveraging automated trading powered by deep learning technology across multiple stock exchanges including New York Stock Exchange and London Stock Exchange.

What is Deep Learning?

Deep Learning is a subset of Machine Learning which itself is a part of the broader Artificial Intelligence Technology. Machine Learning, abbreviated as ML, has been designed to work like a human brain. Unlike rigid and mechanical computer programs and applications that are designed to produce specific outputs for specific conditions, Machine Learning applications are designed to learn from data and accordingly calibrate and produce an output - in a manner human brain would have produced.

Deep Learning dwells deeper while accessing those data sets and attempts to emulate human neural networks to produce outputs much similar to a human brain. In simplest terms, it can be regarded as an advanced version of Machine Learning that produces results more or less comparable to a human brain. In certain cases, Deep Learning has been found to produce results far superior to any human expert would have produced.

Cloudbric’s Vision: Sight of a human mind

In my previous article, I introduced Cloudbric’s Vision as a “patented deep learning solution that would bring “one-stop concierge” security services to the cryptocurrency market by powering an all-inclusive suite of cybersecurity solutions.” VISION has been designed by a highly capable team from Cloudbric that includes hardcore Machine Learning developers and Ph.D. scholars from the prestigious institutes of the Korea Institute of Science and Technology (KAIST), Korea University and the Massachusetts Institute of Technology (MIT). The purpose of VISION is to decentralize the security ecosystem by allowing the users to train their suite of security solutions through anonymous threat data including Indicators of Compromise (IOCs).

When it comes to the security landscape, the threat is ever evolving. On a daily basis, malicious actors from across the world succeed in designing new attack patterns that are extremely sophisticated in nature and possess feeble malicious signatures which cannot be identified by a traditional security product. Moreover, security vendors take time to identify and observe these malicious patterns and release updates for their respective products which provides a significant time window for malicious actors to pillage cryptocurrency exchanges. Such an arduous and demanding security challenge can only be tackled through a system that evaluates the web traffic on the go, learns from the evolving patterns and dynamically identifies and the malicious signatures and blocks the malicious traffic without waiting for any security update. Such a security solution can be based only on the deep learning technology thereby freeing the user from any kind of dependence on the security vendor for product updates and new releases.

This is what Cloudbric’s VISION has been designed to do - decentralize the security ecosystem by fostering a new deep learning model that removes any dependencies on the security vendors. VISION can be easily regarded as a seminal breakthrough in deep learning technology as it removes the traditional dependence of a deep learning model to ingest data in only pixels. It does not mean that VISION does not ingest data in pixels, but it first converts the web traffic, made up of characters, alphabets, letters and phrases into images which are in turn made up of pixels, and then ingested to look out for malicious attack patterns. VISION has been trained to differentiate between legitimate/innocuous and malicious online traffic thereby nipping the threat right there in the bud.

VISION: Small steps toward larger goals

 Creating a deep learning solution for cryptocurrency ecosystem was not that easy. Due to the lack of availability of advanced modules, Cloudbric researchers had to build from scratch a module that could satisfactorily convert online web traffic into images. They did so by testing two open source machines on Convolutional Neural Network (CNN) structures that were designed perfectly for this objective. Both the machines were evaluated and a cost-benefit analysis was performed for the parameters of “ease-of-training” and “accuracy”. The best machine was selected and was further improved to accept unconventional characters found in malicious attack URLs for enhanced data ingestion and advanced threat detection.

To put Cloudbric’s achievement in perspective, VISION has produced an enviable result of 85% accuracy rate increase as compared to the standard Cloudbric Web Application Firewall (WAF) engine which in itself is a benchmark product for one of the lowest false positive rated WAF in the entire security market. The achievement is another feather in the cap of Cloudbric but with over 30 years of collective experience in the security industry and an impressive talent pool, it is just a milestone in the disruptive technological journey of Cloudbric. As cryptocurrency continues to gain wider adoption across the world, the number of targeted malicious attempts against crypto exchanges will only continue to rise. If the crypto ecosystem has to prevent another Mt. Gox like incident from occurring, then it must stay two steps ahead of the malicious actors. For that to happen, the crypto community has to stand at the technological forefront and Cloudbric’s VISION provides a perfect solution and opportunity that must be grasped with both hands.

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