Defending Against Adversarial AI with Deep Learning

Defending Against Adversarial AI with Deep Learning

Despite the rise of adversarial AI, legacy machine learning implementations are still a preferred security approach for hundreds of firms that may soon become sitting ducks. Taking on this challenge with a prevention-oriented solution built on deep learning would finally give businesses a fighting chance against this silent weapon.

With an ever-growing arsenal of new and improved weapons, cybercriminals are constantly ramping up their efforts. Adversarial artificial intelligence is one of the most recent developments. AI has been widely integrated into everyday lives — from medical equipment to domestic appliances — but the power of AI makes it extremely appealing to threat actors, as evidenced by the recent emergence of adversarial AI. Adversarial AI is the cybercriminal’s latest hidden weapon, with specialized capabilities to circumvent the victim’s own machine learning defenses — and one with potentially disastrous implications.

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Adversarial AI capabilities

Today’s machine learning-based cybersecurity systems use pre-labelled datasets to distinguish between malicious and benign threats, allowing for autonomous network monitoring for breaches and incoming threats. This enables firms to protect against incoming cyber-attacks without having to devote additional human resources to the task. One of the recent growing risks utilized by advanced threat actors is the use of their own adversarial AI capabilities to fool defenders’ ML-based security systems into believing their incoming attacks are benign, giving them virtually unnoticed access and mobility. These attacks are notoriously difficult to identify, let alone prevent.

Stopping a threat that can go undetected until the damage is done is extremely tough. This approach has progressed much beyond the basic smash-and-grab method. Rather, the goal is to study an ML system’s behavior and decision boundary in order to create an effective bypass. To discover a breach, firms must comb through threat data for signs of compromise, which takes a lot of time and work. Worse, few businesses have the resources to devote to this time-consuming task, allowing adversarial AI to slip unreported until it’s too late.

Defending against adversarial AI

So, how do companies defend themselves against something that is built to deceive their strongest defense? The best place to start is by replacing the weak link. Because some cybercriminals have embraced and enhanced machine learning, a new type of defense is required. Deep learning, which is designed to go above and beyond legacy machine learning skills, is the answer. End-to-end deep learning models are trained on raw data rather than easily modifiable designed features that are understood by humans. The implementation of accurate deep learning still relies on labeled datasets and runs in supervised learning, but robust deep neural networks are more computationally complex. Deep learning models are far more difficult to bypass because of these two aspects.

Deep learning is not a silver bullet in and of itself, and there are other risks that enterprises must be aware of, but when integrated into an existing security stack, it delivers improved countermeasures. Organizations must completely comprehend how this technology works before using it to ensure that the system performs as efficiently as feasible. Prior to its deployment, it is critical to provide key individuals with precise, complete knowledge on how deep learning works and is used.

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The path forward

One of the most serious challenges that businesses face today is the modern cyber-attacker. With time, more attackers will be equipped with AI expertise to attack and damage unprepared enterprises rapidly and effectively. Furthermore, it’s just a matter of time until adversarial AI algorithms are turned into frameworks that can be sold on the Dark Web in bulk. Malicious AI will become much more freely available as a result of this, not only for common cybercriminals but also for nation-states and tier-1 threat groups. It also begs the question, “How long before threat actors use deep learning?” Given the stakes, it’s critical that firms face the AI war now, before it’s too late.

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