Most malware is often delivered from otherwise legitimate sites. Sometimes this occurs via compromising existing websites, but more often than not, it is by using existing advertising networks as a means to ultimately deliver malware. Quite simply, the attacker buys impressions via existing channels and uses a variety of malvertising tricks to either directly compromise the web browser, or at the least trick the user to installing the malware. This specialized form of malware delivery requires a specialized collection methodology to detect such attacks.

One of the chief problems in cybersecurity is the inherent reactivity of most forms of defense. An attack has to be observed, analyzed and reverse-engineered. THEN, protection can be developed. This means attackers are successful, and inside environments, for a period of time before the attack is noticed, before the indicators for that attack can be extracted, and before a policy can be disseminated to stop it.

There has been a wide variety of research in recent years around this problem. How to speed up the cycle to recognize attacks and to potentially get out in front of attackers to block them before the attacks start. Both my own PhD research and other researchers have noticed that one attribute that is overwhelmingly an indicator of maliciousness in DNS is “newness,” that is to say, the newer a domain is, the more likely that it is bad. More importantly, when a domain is new and otherwise benign, it is rarely in meaningful use except by the organization that’s setting up whatever will go there.

Making connections and finding new indicators is an important part of IOC analysis, and is probably the most enjoyable part as well. Blog posts and reports on new threats will usually mention the indicators seen to be used by the specific malware sample or attack vector analyzed, yet in many cases there is a larger malicious infrastructure behind them just waiting to be uncovered (and blocked!). Sometimes, a whole other malicious infrastructure can be revealed by examining IOCs related to malicious IPs and domains. There are a variety of tools out there that can help analysts investigate indicators of compromise and their infrastructure, and perform enrichment to shed light on related, malicious IOCs.

In this post, we will review some of our Security Research Team’s favorite connection and enrichment platforms.

Welcome To Our New Weekly Series, Free Open Source Analysis Tools.

This Week's Topic: Free Open-Source Analysis Tools, Why Use IOCs?

Throughout this series, we'll be talking about a Security Analyst’s IOC analysis journey. From discovering relevant indicators and performing the analysis, to finding enrichments and new IOCs. We will also share recommendations for free open-source analysis tools and use cases completed by ThreatSTOP's Security and Research Team, showing how to utilize the various platforms and tools. Let's get started.

ThreatSTOP will be implementing changes to our severity labels to be consistent and clearer throughout our policies. We are not changing the policies themselves. Some targets, however, will have different severities and that may impact the volume of alerts you see in your portal account. Accordingly, we wanted to communicate those changes and the rationale behind them.