New Solutions For Zero-Day Attacks

A zero day threat is a vulnerability that developers and security researchers have known about for less than a day. In many cases, these threats are first identified by penetration testers and white hats, which gives them time to issue emergency patches.

They are usually enabled by unknown vulnerabilities and defending against zero-day attacks is one of the most fundamentally challenging security problems yet to be solved. 

Zero-day attacks continue to challenge even the strongest network security defenses. A zero-day attack path is formed when a multi- step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. 

These attacks can take over a computer systems security and it can take weeks to get the systems working again. 
For instance, the WannaCry ransomware attack, which occurred in May 2017, targeted more than 200,000 Windows computers across 150 countries and caused an estimated $4 billion to $8 billion worth of damage. This adaptive machine learning-driven method was developed to address current limitations in a method to detect and respond to cyber-attacks, called moving target defense, or MTD.

Now, researchers at Penn State University have used reinforcement learning, to create an adaptive cyber defense against zero-day attacks.The team’s approach relies on reinforcement learning, which, along with supervised and unsupervised learning, is one of the three main machine learning paradigms. 

According to the researchers, reinforcement learning is a way that a decision-maker can learn to make the right choices by selecting actions that can maximise rewards by balancing exploitation, leveraging past experiences, and exploration, trying new actions. “The decision-maker learns optimal policies or actions through continuous interactions with an underlying environment, which is partially unknown,” said Peng Liu, MD Professor of Cybersecurity in the College of Information Sciences and Technology. “So, reinforcement learning is particularly well-suited to defend against zero-day attacks when critical information, the targets of the attacks and the locations of the vulnerabilities, is not available.”

Zero-day cyber attacks are among the most dangerous threats to computer systems and can cause serious and lasting damage. Due to the information asymmetry between attackers and defenders, detecting zero-day attacks remains a major challenge. Their use in cyber attacks is still at an early phase and hackers can be expected to adapt and become smarter and more effective  

TechXplore:       Research Gate:       Faronics:     NIST:        NIST:     I-HLS:    Image: Unsplash

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