Chuck Leaver – Machine Learning Will Bring Unintended Consequences

Written By Roark Pollock And Presented By Ziften CEO Chuck Leaver


If you are a student of history you will see lots of examples of serious unexpected repercussions when brand-new technology has actually been presented. It typically surprises people that brand-new technologies may have wicked purposes as well as the positive purposes for which they are brought to market however it occurs all the time.

For example, Train robbers utilizing dynamite (“You think you used enough Dynamite there, Butch?”) or spammers utilizing email. More recently making use of SSL to hide malware from security controls has ended up being more common just because the legitimate use of SSL has made this technique more useful.

Due to the fact that new technology is typically appropriated by bad actors, we have no need to think this will not be true about the brand-new generation of machine-learning tools that have actually reached the marketplace.

To what effect will these tools be misused? There are probably a few ways in which attackers might utilize machine-learning to their benefit. At a minimum, malware authors will evaluate their new malware against the new class of sophisticated hazard protection products in a quest to modify their code to ensure that it is less likely to be flagged as malicious. The effectiveness of protective security controls always has a half-life because of adversarial learning. An understanding of artificial intelligence defenses will assist enemies become more proactive in reducing the efficiency of artificial intelligence based defenses. An example would be an enemy flooding a network with phony traffic with the intention of “poisoning” the machine learning model being developed from that traffic. The objective of the assailant would be to fool the protector’s artificial intelligence tool into misclassifying traffic or to create such a high level of false positives that the protectors would dial back the fidelity of the alerts.

Artificial intelligence will likely likewise be utilized as an attack tool by opponents. For instance, some researchers predict that attackers will make use of machine learning strategies to refine their social engineering attacks (e.g., spear phishing). The automation of the effort it takes to tailor a social engineering attack is particularly troubling provided the effectiveness of spear phishing. The capability to automate mass customization of these attacks is a powerful financial incentive for opponents to adopt the techniques.

Expect breaches of this type that provide ransomware payloads to increase greatly in 2017.

The need to automate jobs is a significant driver of investment choices for both assailants and defenders. Artificial intelligence promises to automate detection and response and increase the operational tempo. While the innovation will increasingly become a standard part of defense in depth techniques, it is not a magical solution. It needs to be understood that hackers are actively working on evasion techniques around artificial intelligence based detection products while also utilizing machine learning for their own attack functions. This arms race will need defenders to increasingly attain incident response at machine pace, further exacerbating the need for automated incident response abilities.


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