Cryptojacking is the illegal use of another person’s computing power to mine cryptocurrencies. One of the most crucial skills a security team may possess is the capacity to identify dangers and take action as soon as possible. The level of disruption and operational effect will be reduced the quicker they can react to a data breach.
This is more easily said than done, which is the issue. When using manual administrative methods, it might be quite difficult to detect harmful behavior in the environment and start a response.
However, the detection and response efforts of an organization may be accelerated by technologies like artificial intelligence (AI) and machine learning.
Sysdig, a supplier of a unified container and cloud security, today at the Black Hat Conference announced the release of a new machine learning-driven cloud detection and response (CDR) solution to counter attempts at cryptojacking.
According to Sysdig’s statement, machine learning is a crucial technology that businesses and other decision-makers may use to scale up their efforts to find and fix vulnerabilities.
Dealing with cryptojacking
The amount of malicious crypto mining attacks increased by 30% to 66.7 million between January and June, according to the 2022 SonicWall Cyber Threat Report, even though the cryptocurrency market has taken some substantial hits in recent months.
Cybercriminals will try to leverage a target’s computing resources to mine cryptocurrencies while staying undiscovered for as long as feasible. This offers unique issues for enterprise security teams. The financial gain of the attack increases with the length of time they go unnoticed.
In decentralized cloud environments, despite these efforts to escape discovery, technologies like machine learning have the ability to identify and address cryptojacking attacks quickly.
“Sysdig gives real-time visibility at scale to address risk across containers and multiple clouds, eliminating security blind spots. We use context to prioritize security alerts so teams can focus on high-impact security events and improve efficiency. By understanding the entire source to runtime flow and suggesting guided remediation, we shorten time to resolution,” stated senior product marketing manager at Sysdig, Daniella Pontes.
In essence, Sysdig’s ML-powered solution helps security teams to find and prioritize fixing software anomalies and vulnerabilities before it’s too late.
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The solution operates with deep container visibility, the ability to examine process activity and other system behaviors, and a specialized ML model specifically trained to recognize crypto miner behavior running in containers.
The company claims that this strategy is so successful that 99% of the time, its threat engine and detection algorithms successfully prevent efforts at cryptojacking.
How does the cloud security posture management market look?
In the cloud security posture management (CSPM) market, which analysts predict will expand from a value of $4.2 billion in 2022 to a total of $8.6 billion by 2027, Sysdig is one of the biggest competitors.
CrowdStrike is one of Sysdig’s main marketplace rivals. With integrated threat intelligence, its Falcon Horizon solution can automatically discover cloud-native assets and identify configuration errors, security vulnerabilities, and security threats.
With InsightCloudSec, Sysdig competes against companies like Rapid7. This solution provides real-time analysis and automation capabilities to help security teams safeguard workloads during runtime with vulnerability assessments and automated remediation to eliminate misconfigurations and vulnerabilities.
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The fact that Sysdig is shifting away from utilizing machine learning for more general anomaly detection and toward using it for more specialized objectives or use cases, such as identifying crypto mining, is one of the fundamental distinctions between Sysdig and other providers, according to Pontes.
“Our solution is based on an ML model trained to recognize the anatomy of crypto miners from the processing activity in running containers. We use our deep visibility into containers at runtime to collect the necessary type of data to identify crypto miners’ behavior,” Pontes explained.
