How ML Can Be Used to Cope Up with Cryptojacking Attempts?

Artificial intelligence and machine learning may speed up any organization to detect cryptojacking

The illicit use of another person's processing resources to mine bitcoins is known as cryptojacking. The ability to spot threats and act quickly is one of the most important abilities a security team may have. The faster they can respond to a data breach, the less interruption there will be and how little it will affect operations.

The problem is that this is easier said than done. Identifying dangerous behavior in the environment and launching a reaction may be quite challenging when employing manual administrative approaches.

However, technology like artificial intelligence (AI) and machine learning may speed up an organization's detection and response activities.

To thwart efforts at cryptojacking, Sysdig, a provider of a unified container and cloud security, today at the Black Hat Conference announced the availability of a new machine learning-driven cloud detection and response (CDR) solution.

Machine learning is a critical technology, according to Sysdig's statement, that organizations and other decision-makers may utilize to scale up their efforts to detect and patch vulnerabilities.

Dealing with Cryptojacking

Despite the cryptocurrency market suffering significant losses recently, the number of harmful crypto mining assaults surged by 30% to 66.7 million between January and June, according to the 2022 SonicWall Cyber Threat Report.

To mine cryptocurrency and avoid detection for as long as possible, cybercriminals would try to take advantage of a target's computational power. For business security teams, this presents particular problems. The longer the attack goes undetected, the more money it will bring in.

Despite these attempts to avoid detection, technologies like machine learning can swiftly recognize and stop cryptojacking assaults in decentralized cloud settings.

"Sysdig eliminates security blind spots by providing real-time visibility at scale to handle risk across containers and various clouds. To help teams concentrate on high-impact security incidents and increase productivity, we leverage context to prioritize security notifications. We reduce time to resolution by comprehending the complete source to runtime cycle and recommending guided remediation, according to Sysdig senior product marketing manager Daniella Pontes.

The main benefit of Sysdig's ML-powered solution is that it enables security teams to identify and prioritize resolving software abnormalities and vulnerabilities before it's too late.

The solution uses a specialized ML model that has been trained to recognize crypto miner behavior running in containers, together with deep container visibility, and the ability to investigate process activity and other system behaviors.

The business asserts that this tactic is so effective that its threat engine and detection algorithms effectively thwart attempts at cryptojacking 99% of the time.