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.
Spam filters are essential. Without them we couldn’t cut through the noise of phishing scams and malware links to read our messages. Hatred of spam is well entrenched in society, just as bots (especially scalpers) have become the bane of so many lives in recent years. There are several parallels between the email spam in our inboxes and malicious bots that crawl the web. Both are designed to cause harm to businesses and individuals. Both exploit systems designed to be useful.
Our world is driven by technological innovation. Recent years have seen many companies adopt artificial intelligence (AI) and machine learning technology to analyze larger data sets and perform more complex tasks with faster and more accurate results. This is not limited to technology-based industries such as computer science – now, many industries work continuously to enhance their technology to keep up with consumer expectations, with data-based decision making often central to this drive.
Online travel agencies, more commonly referred to as OTAs, are online booking platforms used to compare prices and book flights, hotels or holiday packages. Well-known OTAs include Expedia, Booking.com and TripAdvisor. While we have seen a significant increase in the use of OTAs for booking travel arrangements in recent years, we have also seen a similar rise in OTA fraud. Total fraud loss to OTAs was predicted to grow by 19% to $25 billion by the year 2020.
As a (fairly) new member of Splunk’s Threat Research team (STRT), I found a unique opportunity to train machine learning models in a more impactful way. I focus on the application of natural language processing and deep learning to build security analytics. I am surrounded by fellow data scientists, blue teamers, reverse engineers, and former SOC analysts with a shared passion and vision to push the state of the art in cyber defense.
As we’ve shown in a previous blog, search-based detection rules and Elastic’s machine learning-based anomaly detection can be a powerful way to identify rare and unusual activity in cloud API logs. Now, as of Elastic Security 7.13, we’ve introduced a new set of unsupervised machine learning jobs for network data, and accompanying alert rules, several of which look for geographic anomalies.
Organizations will spend $327.5 billion in 2021 for AI systems according to the IDC report, but not all AI and Machine Leaning investments will bring the desired outcomes.
At its core, HIPAA compliance is simply about maintaining patient privacy by ensuring the appropriate access to and use of patient data by your users. Electronic Health Record (EHR) solutions provide detail around when patient data is accessed, but without visibility into what users do with sensitive patient data after they access it, the risk of data breaches, compliance violations, and the investigations, fines, and reputational damage that comes with them, is significantly increased.
