Deep learning delivers proactive cyber defense

The increased pace of high-profile threats (e.g., ransomware) is up to doubledigit (15.8%) growth. The result is a dangerous path most likely to lead to continued losses for organizations that fall victim to a cyberattack without any gains in defensive powers. Indeed, a 2021 data breach report by IBM and the Ponemon Institute reveals that the average cost of a data breach is $4.24 million.

Beyond costs, a cyberattack can cause irreparable damage to a company’s brand, share price, and day-to-day operations. According to a recent Deloitte survey, 32% of respondents cited operational disruption as the biggest impact of a cyber incident or breach. Other repercussions cited by surveyed companies include intellectual property theft (22%), a drop in share price (19%), reputational loss (17%), and a loss of customer trust (17%).

Given these significant risks, organizations simply can’t afford to accept the status quo on protecting digital assets. “If we are to ever get ahead of our adversaries, the world needs to change the mindset from detection to one of prevention,” says Caspi. “Organizations need to change the way they perform security and combat hackers.”

Deep learning can be the difference

Up until now, many cybersecurity experts have viewed machine learning as the most innovative approach to safeguarding digital assets. But deep learning is ideally suited to change the way we prevent cybersecurity attacks. Any machine learning tool can be understood, and theoretically reverse engineered to introduce a bias or vulnerability that will weaken its defenses against an attack. Bad actors can also use their own machine learning algorithms to pollute a defensive solution with false data sets.

Fortunately, deep learning addresses the limitations of machine learning by circumventing the need for highly skilled and experienced data scientists to manually feed a solution data set. Rather, a deep learning model, specifically developed for cybersecurity, can absorb and process vast volumes of raw data to fully train the system. These neural networks become autonomous, once trained, and do not require constant human intervention. This combination of a raw data-based learning methodology and larger data sets means that deep learning is eventually able to accurately identify much more complex patterns than machine learning, at far faster speeds.

“Deep learning outshines any deny list, heuristic-based, or standard machine learning approach,” says Mirel Sehic, vice president general manager for Honeywell Building Technologies (HBT), a multinational corporation and provider of aerospace, performance materials, and safety and productivity technologies. “The time it takes for a deep learning-based approach to detect a specific threat is much quicker than any of those elements combined.”

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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