Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Machine Learning

sysdig

The Quiet Victories and False Promises of Machine Learning in Security

Contrary to what you might have read on the Internet, machine learning (ML) is not magic pixie dust. It’s a broad collection of statistical techniques that allows us to train a computer to estimate an answer to a question even when we haven’t explicitly coded the correct answer into the program.

Machine Learning, AI & Cyber Security Part 2: Malicious Actors

- Machine Learning, AI & Cyber Security Part 2: Malicious Actors Welcome to a new episode of the Razorwire Podcast! Welcome to part two of our episode on Machine Learning, AI and Cyber Security. In part one, we discussed what it will be like for us as security professionals when we have access to AI tools, what we are doing with them now and how we could use them in future. In part two, we are re-joined by our guests Oliver Rochford of Securonix and Jonathan Care, a mentor of mine who specialises in cyber security and fraud detection.

Machine Learning, AI & Cyber Security Part 1: Used for Good

Welcome to another episode of Razorwire Podcast! We are joined today by Oliver Rochford of Securonix and Jonathan Care, a mentor of mine who specialises in cyber security and fraud detection. As AI and its application in cyber security are such a big topic, this podcast will be in two parts. We will discuss machine learning and artificial intelligence for information security in the first part of our discussion today.
tripwire

Convergence and adoption of AI and ML countering the cyber threat

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.

sysdig

Cryptominer detection: a Machine Learning approach

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.

sysdig

Detect cryptojacking with Sysdig's high-precision machine learning

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.

netskope

The Importance of a Machine Learning-Based Source Code Classifier

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

A Deep Dive into Custom Spark Transformers for Machine Learning Pipelines

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.

CrowdStrike

How CrowdStrike's Machine Learning Model Automation Uses the Cloud to Maximize Detection Efficacy

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.

CrowdStrike

How CrowdStrike Achieves Lightning-Fast Machine Learning Model Training with TensorFlow and Rust

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.