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In the Wake of the Yahoo Breach: What to Do if Your Account Was Compromised

On September 22nd, 2016, Yahoo confirmed that they were victim to a state-sponsored attack that compromised 500 million user accounts. According to Yahoo, "The account information may have included names, email addresses, telephone numbers, dates of birth, hashed passwords (the vast majority with bcrypt) and in some cases, encrypted or unencrypted security questions and answers." Yahoo is recommending users change their passwords and review their accounts for suspicious activity.

Gathering Evidence Through Network Monitoring

In this field, we know that gathering evidence is critical to identifying the attack vector, understanding how to stop the attack quickly, and moving ongoing investigations further. One of the best ways to gather forensic evidence is through network monitoring.

Temporal Chain Normalization: The Unsung Hero of Event Correlation

When it comes to correlation capabilities, LogRhythm has you covered. With AI Engine you can perform a variety of activities, from observing a single activity to applying advanced behavior rules across multiple dimensions (entities, devices, log sources, metadata, etc.). In addition to some of the more obvious capabilities, I’m here to tell you about one not so known feature of AI Engine called Temporal Chain Normalization (TCN).

LogRhythm Challenge: Black Hat 2016

For the LogRhythm Challenge at Black Hat USA this year, we wanted to give participants the opportunity to use several different analytic skills in their attempt to beat the challenge. The goal of the challenge was to identify exfiltrated data from Swish Inc., a fictional video streaming company who was recently exposed as having data leaked to a public file sharing site. We’ll tell you how to find each of the hidden flags within the PCAP.

DPA-Powered Dashboards

With the proliferation of top-level domains, threat actors are using all sorts of DNS tricks to entice people to engage with malicious sites or to mask malicious traffic in the noise of normal traffic. So how do you sort through the noise to find abnormal top-level domains (TLDs)?