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Machine Learning Is Cybersecurity’s Latest Pipe Dream
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Whoopty
Whoopty,
 User Rank: Ninja
10/30/2015 | 8:02:29 AM
Experts>AI
I have to agree with the idea to keep paying experts rather than relying just on AI. Although I think machine learning has the potential in the future to be a major tool used to combat security issues and can be used sparingly now for some uses, there really is no substitute for an intuitive security expert. 

Not only can they improvise on the fly and make jumps way further down the alphabet than a computer controlled system can, they can intuitively figure out how human hackers may think and can provision for them. That's something that will take AI much longer to figure out, as they'll never understand how we think quite like we do.
RyanSepe
RyanSepe,
 User Rank: Ninja
10/31/2015 | 5:04:38 PM
Re: Experts>AI
Very much agree. There will most certainly need to be a human element in cybersecurity.

Unfortunately to improve AI is a catch 22 situation. You need the man power to put hours into benefitting the technology. But then that takes hours away from that persons security knowledge being taken being used in the workforce.
gyp
gyp,
 User Rank: Author
11/5/2015 | 4:01:36 PM
Experts vs. ML is false dichotomy
The role of ML in security is not and never was replacing the experts, rather to free them from doing tedious tasks and to give them efficient tools. You don't want you experts to manually go through tens of thousands of log lines or do manual data munging every time they need to investigate an incident. Actually, if they are true experts, they would be fed up with that quite fast. Build or buy is a valid question, whether an off-the-shelf product can truly find the problems in your scenario better than something custom-built could but discarding data science as part of security alltogether would be a mistake.
dfabbri
dfabbri,
 User Rank: Apprentice
11/19/2015 | 6:42:56 PM
Explanation-Based Auditing - A different type of machine learning method for cyber security
The author rightly comments that standard anomaly detection systems are difficult to apply to cyber security. Outlier detection may help identify large scale scraping, but individual and subtle inappropriate acts are hard to find (e.g., accessing an ex-girlfriend's medical record). 

As the author notes:

"Any ML system must attempt to separate and differentiate activity based either on pre-defined (i.e. trained learning) or self-learned classifications"

Thus, for a cybersecurity machine learning system to be effective, it must have some principled and structured method to differentiate appropriate and inappropriate access. And, moreover, the system must have the correct context to make such a decision.

The author makes the statement that ML systems struggle to do this:

"Unfortunately, ML systems are not good at describing why a particular activity is anomalous, and how it is related to others. So when the ML system delivers an alert, you still have to do the hard work of understanding whether it is a false positive or not, before trying to understand how the anomaly is related to other activity in the system."

I would point the author to a new line of machine learning algorithms for access auditing called Explanation-Based Auditing.

A detailed peer-reviewed publication can be found at vldb.org/pvldb/vol5/p001_danielfabbri_vldb2012.pdf.

The general idea is to learn why accesses to data occur (e.g., the doctor accessed a record because of an appointment with the patient). This can be modeled as a graph search between the person accessing the data and the data accessed. When such an "explanation" is found, the system can determine the reason for access, filtering it away from manual review.

Thus, as the previous comment states, such as system can remove a tremendous amount of false positives, allowing the privacy or security officer to focus on the unexplained and suspicious. 

 

 


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