During the last few years, we have witnessed an increase in advanced cyber attacks. Cybercriminals utilize advanced technology to breach the digital boundary and exploit enterprises’ security vulnerabilities. No industry feels secure; security professionals do their utmost to close security gaps and strengthen their cyber defense.
Cryptominers are one of the main cloud threats today. Miner attacks are low risk, low effort, and high reward for a financially motivated attacker. Moreover, this kind of malware can pass unnoticed because, with proper evasive techniques, they may not disrupt a company’s business operations. Given all the possible elusive strategies, detecting cryptominers is a complex task, but machine learning could help to develop a robust detection algorithm.
Is cryptojacking draining your resources and exposing your organization to financial and reputation damage risk? The rise in cryptojacking, which is an illegal form of mining cryptocurrency by the unauthorized use of someone’s computing resources, has reached alarming levels. According to the Google Threat Horizon report, 86% of compromised cloud instances in 2021 were used for cryptomining. That paints the picture quite clearly.
This is the fifth in a series of articles focused on AI/ML. Source code is a critical part of an organization’s intellectual property and digital assets. As more and more centralized source code repositories are moving to the cloud, it is imperative for organizations to use the right security tools to safeguard their source code.
CrowdStrike data scientists often explore novel approaches for creating machine learning pipelines especially when processing a large volume of data. The CrowdStrike Security Cloud stores more than 15 petabytes of data in the cloud and gathers data from trillions of security events per day, using it to secure millions of endpoints, cloud workloads and containers around the globe with the power of machine learning and indicators of attack.
At CrowdStrike, we combine cloud scale with machine learning expertise to improve the efficacy of our machine learning models. One method for achieving that involves scanning massive numbers of files that we may not even have in our sample collections before we release our machine learning models. This prerelease scan allows us to maximize the efficacy of our machine learning models while minimizing negative impact of new or updated model releases.
Supercharging CrowdStrike’s artificial intelligence requires both human professionals and the right technologies to deliver blisteringly fast and accurate machine learning model training with a small footprint on the CrowdStrike Falcon® sensor. CrowdStrike data scientists continuously explore theoretical and applied machine learning research to advance and set the industry standard in protecting customers from sophisticated threats and adversaries.
In this blog post, we explore how we boost our MLaaS approach to create the best blend of innate human nuance and computational ability of machines to solve your business challenges…
CrowdStrike data science researchers recently explored and experimented with the use of Bidirectional Encoder Representation from Transformers (BERT) for embedding command lines, focusing on anomaly detection, but without detailing the model itself. Diving deeper into that research, CrowdStrike researchers explain the reasons for using BERT for command line representation and how to train the model and assess its performance.
Machine learning (ML) detections are a powerful tool for detecting emerging threats when we don’t yet know what we’re looking for. The power of anomaly detection is the ability to detect and provide early warning on new threat activity for which rules, indicators, or signatures are not yet available.
