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Machine Learning, Deep Learning and Neural Networks, Oh My! – Security Boulevard

The following is an excerpt from our recently published whitepaper, “Self-Supervised Learning – AI for Complex Network Security.” The author, Dr. Peter Stephenson, is a cybersecurity and digital forensics expert having practiced in the security, forensics and digital investigation fields for over 55 years. Section 2 – Machine Learning, Deep Learning and Neural Networks, Oh My!There are three more terms that get bandied about without much real definition in typical marketing materials. They, like AI, have become buzzwords without usually giving a lay description of what they really mean (or don’t mean). Since Russell’s whitepaper gives us a great start, let’s take a little deeper dive. This is important because, as we progress to the all-important “what’s in it for me?” question, we need a little technical background to help us get past the jargon.If we think of machine learning (ML) as fact gathering and Deep Learning (DL) as interpretation we’ll be pretty close on the meat of the terms. It’s really a bit more than that, of course. Machine learning does, in fact, gather data but it does not require explicit programming to do so. For example, first generation anti-virus products worked on the basis of signatures. They looked for a signature – a specific bit pattern – to identify a virus.That was pretty simple to fool and the moment a new .dat file emerged from a vendor, a set of changes to the viruses it could identify appeared in the criminal hacker underground. The virus writers made the changes and the AV product’s efficacy was diminished substantially until the next round at which time, of course, the cycle repeated. These .dat files and their countermeasures by the adversary were not ML. They were, simply, pattern recognition.The next step the AV vendors took was the introduction of heuristics and behavior-based analysis. These at their inception were crude examples of ML. The AV was observing known signatures and looking for things that looked or behaved a bit like them. So the fact gathering was there but there was a bit more intelligence applied to it.Deep learning makes decisions based upon the data it sees and the data that it doesn’t see but infers from what it does see. This became useful in the AV industry when the adversary introduced polymorphic viruses. These are viruses that change their appearance on the fly and not always in the same way.For example, a polymorph may be encrypted to hide its signature. At some point in its execution cycle it decrypts itself, does its damage and re-encrypts, this time using a different key. An ML system would have a hard time with this because of the seemingly random changes but a DL system might look at the code, decide that it’s encrypted and make some decisions as to what to do about it.Continue reading this whitepaper here.MixMode Articles You Might Like:4 Challenges of Stand-Alone SIEM PlatformsEncryption = Privacy ≠ SecuritySelf-Supervised Learning – The Third-Wave in Cybersecurity AIHow the Role of the Modern Security Analyst is ChangingOne Thing All Cybersecurity teams Should Have During COVID-19The Cybersecurity Processes Most Vulnerable to Human ErrorNew Video: How Does MixMode’s AI Evolve Over Time With a Customer’s Environment?New Whitepaper: How Predictive AI is Disrupting the Cybersecurity Industry
*** This is a Security Bloggers Network syndicated blog from MixMode authored by Christian Wiens. Read the original post at: