Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Anomaly Detection


Multivariate Anomaly Detection: Safeguarding Organizations from Internal Threats

‍ The term “internal threat” refers to the risk that somebody from inside a company could exploit a system to cause damage or steal data. Internal threats are particularly troubling, as employees may abuse extended privileges, leading to massive losses for the organization. One such infamous case is of an ex-Google employee who was charged with theft of trade secrets from Google for a ride-hailing start-up Uber.

Featured Post

JUMPSEC works on a prototype lightweight anomaly detection system

Deploying machine learning models in the cyber security industry is complicated - especially with budget and technology limitations. Especially when it comes to anomaly detection, there's been much debate over privacy, balance, budget, robustness, cloud security and reliable implementation. For cyber security companies using machine learning technologies, ensuring clients' safety with trustworthy artificial intelligence (AI) must always be the primary objective.

Graylog Security Anomaly Detection: Metrics Ease the Workload

Everything that makes employees’ lives easier, makes yours harder. Detecting insider threats — both employees and cybercriminals pretending to be employees — has never been more difficult or more important. The cloud technologies that make everyone else more efficient make security less efficient. They’re noisy. They send a lot of alerts. You’re tired. You’re overworked. You’re overloaded.

Anomaly Detection in Cybersecurity for Dummies

One of the best ways to defend against both internal and external attacks is to integrate anomaly detection, a.k.a. user and entity behavior analytics capabilities, into your security analytics solution. In this e-book, we break down the different types of security anomalies and explain what each one looks like. We also explain how to determine the risk score of every user and host in the network. Finally, we cover five ways in which you can harden your defenses with anomaly detection.

BERT Embeddings: A New Approach for Command Line Anomaly Detection

The large amounts of behavioral data being generated today necessitate accurate labels for machine learning classifiers. In an earlier blog post, Large-Scale Endpoint Security MOLD Remediation, we discussed how to remediate labeling noise. In this blog post, we experiment with an unsupervised approach that eliminates the need for learning from labeled data.


October Release Rollup: New Anomaly Detection, UX Features

We’re excited to share several recent user experience improvements we’ve made across the platform, including multivariate anomaly detection and other new features aimed at improving content governance. Continue reading to learn about some of our top product releases for October.


Detect security threats with anomaly detection rules

Securing your environment requires being able to quickly detect abnormal activity that could represent a threat. But today’s modern cloud infrastructure is large, complex, and can generate vast volumes of logs. This makes it difficult to determine what activity is normal and harder to identify anomalous behavior. Now, in addition to threshold and new term –based Threat Detection Rules , Datadog Security Monitoring provides the ability to create anomaly


Identify API Incidents with Built-in Anomaly Rules

One of Bearer's super powers is anomaly detection. Anomalies are unexpected issues that happen when making an API call. These could be high error rates, unexpected response codes, latency spikes, and more. By monitoring APIs with anomaly detection, we can identify problems with an API or within your application. Anomaly detection makes debugging easier and can help you identify API performance issues that affect your end users.