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Cybercrimes have become big news, with large data and security breaches at companies generating headlines, and cyberthreats from foreign locales such as China and Russia threatening U.S. businesses and elections. Cybersecurity Ventures predicts cybercrime damages will cost the world $6 trillion annually by 2021.

Today, it has never been so important to protect your information as, cyber threats became an inevitable part of ever expanding modern digital environments, with targeted attacks and threats coming from both foreign and internal sources.

But how do you protect against a threat that is continuously evolving and continuously fighting your networks? How do you implement a long-lasting system to protect your information? The obvious solution is to continuously update security measures and firewalls. But, to do so how much effort does one spend? What if you don’t have to put in any effort? What if there is an intelligent automated system that updates on its own and fights back threats continuously and develops a robust system to keep your information safe at all times?

Realizing cyber threat is an inevitable part of doing business, RIVA, has been exploring solutions on how it can provide a continuous approach to cyber security. RIVA’s Innovation Lab is always evaluating new and emerging technologies to bring the best solutions to our government customers. In order to do this, we have developed an end-to-end cybersecurity solution using Darktrace, Splunk, and others to deliver an end-to-end cybersecurity technology solution that uses advanced machine learning and artificial intelligence algorithms to detect and respond to previously unknown cyber-threats as they emerge in real-time.

Our solution of identifying threats early on and mitigating them before they become a full-blown crisis can be deployed across both IT and OT environments to provide full coverage of your organization. We will explore how RIVA can help your organization protect your information and continuously identify and mitigate cyber threats.

RIVA is able to provide an advanced cybersecurity technology, powered by artificial intelligence capable of finding anomalies that bypass other security tools. With a proven track record RIVA’s cyber defense suite is able to detect a wide range of cyber-threats, using a probabilistic approach that takes into account multiple weak indicators to form a compelling picture of overall threat.

Here is the underlying principle of our cybersecurity suite that is used by Machine learning to create next generation of cyber security solutions powered by Artificial intelligence:

  • A system that can autonomously learn what is normal within a network – and doesn’t depend upon knowledge of previous attacks.
  • It thrives on the scale, complexity and diversity of modern businesses, where every device and person are slightly different.
  • It turns the innovation of attackers against them – any unusual activity is visible.
  • It constantly revisits assumptions about behavior, using probabilistic mathematics.
  • It is always up to date and not reliant on human input.

Using a probabilistic approach to cyber security based on a Bayesian framework RIVA integrates a huge number of weak indicators of potentially anomalous network behavior to produce a single clear measure of how likely a network device is to be compromised. This probabilistic mathematical approach is critical to our solution’s unique ability to understand important information, amid the noise of the network even when it does not know what it is looking for.

One could ask there could be so many anomalies happening in day to day operations of enterprise level organization, finding a critical threat is like finding a needle in a haystack. With the added challenge of haystack growing incrementally every day. And how do you define the needle? With millions of versions of sophisticated malware circulating, thousands of users accessing data, hundreds of supply chain companies and partners walking in and out of your digital premises every day.

The challenge of finding the needle is cumbersome and if neglected long enough we don’t know how it is behaving or what its objective is, and by the time we find where it is, it could too late. Cyber-attacks are impossible to guess how and where they will start and finish. At RIVA we identified this problem early on and our team has experimented with countless technologies that was capable of a robust cyber security system that is one of discovery – of knowing, ahead of time, about the threats and can perform these activities with minimum human supervision.

RIVA found an ingenious solution to identify and respond to in-progress cyber-threats by unsupervised machine learning. By implementing artificial intelligence with unsupervised machine learning we let the computer do all the hard work for us, it autonomously detects, prioritizes, alerts, mitigates by taking action against cyber-threats within all types of networks, including physical, cloud, virtual, IoT, and ICS environments and provide a very intuitive way of interaction with the system that is visually pleasing and easy to use; an industry first.

RIVA has mastered the means of delivering the next generation of machine learning cybersecurity solutions powered by artificial intelligence to any organization to continuously protect your data and fight against cyber attackers. With unsupervised machine learning methods, we do not require training data with pre- defined labels. Instead the AI can identify key patterns and trends in the data, without the need for human input.

The advantage of unsupervised learning is that it allows computers to go beyond what their programmers already know and discover previously unknown relationships. With unsupervised machine learning algorithms to analyze network data at scale, intelligently handle the unexpected, and embrace uncertainty. Instead of relying on knowledge of past threats to be able to know what to look for, it can independently classify data and detect compelling patterns that define what may be normal behavior.

Any new behaviors that deviate from those, which constitute this notion of ‘normality,’ may indicate threat or compromise. The impact of unsupervised machine learning on cyber security is transformative:

  • Threats from within, which would otherwise go undetected, can be spotted, highlighted, contextually prioritized and isolated using these algorithms.
  • The application of machine learning has the potential to provide total network visibility and far greater detection levels, ensuring that networks have an internal defense mechanism.
  • It has the capability to learn when to action automatic responses against the most serious cyber threats

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