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How will artificial intelligence impact global cyber security?

Or put another way:  How to attack and defend cyber assets with a new generation of machine learning technologies?

This paper provides actionable technical insights for business, government and military executives seeking technologies that will provide a competitive advantage in cyber warfare. We believe the requirements for both military and civilian cyber defenses are similar enough to use published (public) military specifications as a common denominator for protecting cyber assets.

Based on more than 50 people-years of research and development, we believe that machine learning is transformational to the “cyber battlespace” – where computers and/or networks are intentionally disrupted to cause harm or further criminal, political, ideological, social, or similar objectives.

Our goal is inspire innovation: ai-one does not provide a solution or services. We only provide core machine learning technologies. We believe that a complete artificial intelligence solution to combat cyber security threats requires combining multiple tiers of technology – possibly including natural language processing (NLP), machine learning, signal processing, Bayesian decisioning tools, packet profile ontologies, etc.

Cyber warfare spans both military and civilian concerns. The US Department of Defense has defined cyber warfare as the “Fifth Battlespace” (after land, sea, air and submarine domains). As a result, all branches of the US military now have cyber-specific commands.

Similarly, the civilian world is justifiably obsessed with the protection of cyber assets. Groups such as Anonymous and WikiLeaks have wreaked havoc on financial institutions, markets and governments by disrupting mission critical networks and disseminating proprietary information.

Cyber security is essential for civil freedoms, economic opportunities and national security.

ai-one is one of a handful of firms that provide off-the-shelf machine learning and artificial intelligence application program interfaces (API). Our breakthrough is creating a system that enables any programmer to build machine learning into almost any program. The core value is simple:

We detect patterns.

If you know the patterns… you know the relationships between data elements.

If you know the relationships… then you know the context of any element.

If you know context… you understand meaning.

ai-one’s APIs enable machines to learn. Any data. Any format. Faster and more accurately than a human.   The implications for this technology spans the entirety of all computing.

First, let’s define the terms artificial intelligence and machine learning as they are used within this paper – as there are many interpretations of both.

Artificial intelligence (AI):

The simulation of human intelligence in machines. Its critical feature is the ability to make decisions. Thus, there is a vast range of capabilities within artificial intelligence. A simple manifestation would be a search engine – such as Google. More sophisticated AI systems would include agents that make autonomous decisions – such as Apple’s SIRI.

Machine learning (ML) 

A branch of AI that is specifically concerned with enabling machines to understand information, intent and context. Its critical feature is to derive the meaning of data by evaluating data from sensors and/or data storage devices. Examples include: latent Dirichlet allocation (LDA) and ai-one’s adaptive holosemantic dataspace (HSDS). Both are self-organizing maps (SOMs) that detect patterns. However, there are significant differences due to LDAs use of Bayesian statistics which make it computationally less efficient than HSDS which is a new form of neural network that is transparent, autonomous and at least as accurate as Bayes.