‘Deep learning is a completely terrible idea for security,’ says cybersecurity expert

February 22, 2022, 11:23 PM UTC

Zulfikar Ramzan, the former chief technology officer for cybersecurity giant RSA, holds a controversial belief regarding artificial intelligence and security. 

“I think deep learning is a completely terrible idea for security,” Ramzan bluntly says. His statement flies in the face of conventional wisdom that neural networks and their ability to discover patterns in massive amounts of data will revolutionize the cybersecurity industry.

Ramzan, now the chief scientist and CEO of Aura Labs, concedes that “there’s always exceptions” and it’s possible that there are deep learning experts who are doing innovative work applying A.I. to security problems. But, deep learning still has some major issues that need to be addressed before he feels comfortable enough for the A.I. technique to be used more widely in the security industry.

For instance, Ramzan notes that researchers who were able to obtain major advancements in computer vision with deep learning had access to a wealth of labeled images and data. Security researchers may not have the same amount of clean data needed to train useful neural networks.

“You got to start off with good data; good data is your foundation,” Razman says. “You can’t make good wine from bad grapes or, you know, good beer from bad hops.”

He also believes that hackers could potentially exploit a major current problem with neural networks: researchers have trouble explaining how the software makes its decisions. Ramzan says if criminals were able to compromise the data used to train a deep learning system (sometimes referred to as data poisoning), they could subtlety influence the neural network without a company’s security team realizing the problem.

“I think in a world where you have adversaries, trying to do deep learning is very dangerous because it’s hard to understand what the model is doing,” Ramzan says.

Ramzan believes rule-based systems are easier to describe and debug than deep learning systems. 

He remembers an incident that happened a few years ago when an unnamed top distinguished software engineer from a “super well-known” company built an A.I. system designed to detect malware. The engineer didn’t realize, however, that the data he used for training the system contained timestamps indicating when each digital file was created. The problem was that the A.I. system began incorporating the timestamps into its computational analysis, despite the timestamps having no role in determining malware. 

“He collected data in a way that was kind of subtly biased,” Ramzan says. “There was an attribute in there that should not have been relevant.”

Ramzan was only able to spot the problem because he recognized that the datasets used to train the A.I. contained irrelevant attributes. 

If the model went into production and no one scanned the underlying datasets, the company could have been running an ineffective malware detection tool powered by A.I.

Ramzan isn’t completely opposed to A.I., and he noted that many security products incorporate more basic machine learning as opposed to the more cutting-edge deep learning technology that’s harder for researchers to explain.

But he opposes the idea of doing deep learning just for the sake of doing it when there may be more conventional techniques available that work just fine. When deep learning goes wrong, the consequences can be great.

“You can have a great tool, but if someone doesn’t know how to use it properly, it’s like giving somebody a bazooka and they can shoot themselves in the foot with it,” Ramzan says.

Jonathan Vanian 


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A new A.I. hub hits the scene. Underwriters Laboratories and Northwestern University have created an A.I. research hub intended to examine A.I. systems and their impact on society. The goal is for the hub to develop methods to “better incorporate safety and equity into the fast-growing technology,” according to a statement from the organizations.


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From the paper: In this paper, we considered a parking video analytics platform and proposed RL-CamSleep, a deep reinforcement learning-based technique that can improve the system’s overall energy savings while retaining its utility (in the form of accuracy). Our approach is orthogonal to existing work that focuses on improving hardware and software efficiency. We evaluated our approach on a city-scale parking dataset with diverse parking profile patterns.


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Several of the biggest developments in A.I. have stemmed from Canadian universities and researchers, with major tech companies setting up research hubs in the country to help them pursue their own A.I. projects and hire local talent.

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