Analytics 101

From today’s smart home applications to autonomous vehicles of the future, the efficiency of automated decision-making is becoming widely embraced. Sci-fi concepts such as “machine learning” and “artificial intelligence” have been realized; however, it is important to understand that these terms are not interchangeable but evolve in complexity and knowledge to drive better decisions.

Distinguishing Between Machine Learning, Deep Learning and Artificial Intelligence

Put simply, analytics is the scientific process of transforming data into insight for making better decisions. Within the world of cybersecurity, this definition can be expanded to mean the collection and interpretation of security event data from multiple sources, and in different formats for identifying threat characteristics.

Simple explanations for each are as follows:

  • Machine Learning: Automated analytics that learn over time, recognizing patterns in data.  Key for cybersecurity because of the volume and velocity of Big Data.
  • Deep Learning: Uses many layers of input and output nodes (similar to brain neurons), with the ability to learn.  Typically makes use of the automation of Machine Learning.
  • Artificial Intelligence: The most complex and intelligent analytical technology, as a self-learning system applying complex algorithms which mimic human-brain processes such as anticipation, decision making, reasoning, and problem solving.

Benefits of Analytics within Cybersecurity

Big Data, the term coined in October 1997, is ubiquitous in cybersecurity as the volume, velocity and veracity of threats continue to explode. Security teams are overwhelmed by the immense volume of intelligence they must sift through to protect their environments from cyber threats. Analytics expand the capabilities of humans by sifting through enormous quantities of data and presenting it as actionable intelligence.

While the technologies must be used strategically and can be applied differently depending upon the problem at hand, here are some scenarios where human-machine teaming of analysts and analytic technologies can make all the difference:

  • Identify hidden malware with Machine Learning: Machine Learning algorithms recognize patterns far more quickly than your average human. This pattern recognition can detect behaviors that cause security breaches, whether known or unknown, periodically “learning” to become smarter. Machine Learning can be descriptive, diagnostic, predictive, or prescriptive in its analytic assessments, but typically is diagnostic and/or predictive in nature.
  • Defend against new threats with Deep Learning: Complex and multi-dimensional, Deep Learning reflects similar multi-faceted security behaviors in its actual algorithms; if the situation is complex, the algorithm is likely to be complex. It can detect, protect, and correct old or new threats by learning what is reasonable within any environment and identifying outliers and unique relationships.  Deep Learning can be descriptive, diagnostic, predictive, and prescriptive as well.
  • Anticipate threats with Artificial Intelligence: Artificial Intelligence uses reason and logic to understand its ecosystem. Like a human brain, AI considers value judgements and outcomes in determining good or bad, right or wrong.  It utilizes a number of complex analytics, including Deep Learning and Natural Language Processing (NLP). While Machine Learning and Deep Learning can span descriptive to prescriptive analytics, AI is extremely good at the more mature analytics of predictive and prescriptive.

With any security solution, therefore, it is important to identify the use case and ask “what problem are you trying to solve” to select Machine Learning, Deep Learning, or Artificial Intelligence analytics.  In fact, sometimes a combination of these approaches is required, like many McAfee products including McAfee Investigator.  Human-machine teaming as well as a layered approach to security can further help to detect, protect, and correct the most simple or complex of breaches, providing a complete solution for customers’ needs.

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Story added 18. October 2017, content source with full text you can find at link above.


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