Photo Cred: Forbes

Last week, I had the pleasure of speaking at Virus Bulletin on the recent news of iPhone (first reported on by Google Project Zero) and Android (first reported on by Volexity) mobile malware being used to target Tibetans (as reported by Citizen Lab) and Uighur Muslims inside and outside the People’s Republic of China. Lots of great research is linked above and you should definitely read it.

Whenever events like these occur, researchers from many organizations are researching pieces of it. If you are interested in Chinese APT attacks against these groups, certainly take a look.

One of the most interesting things to me when looking into these attacks is the sophistication and persistence of the adversary. As vulnerabilities got patched, they reused what pieces they could from their attacks and discovered new vulnerabilities to maintain their ability to action on the surveillance objectives. Some of the tools used indicate relationships to other Chinese APT groups, and certainly these types of attacks could be used against truly foreign adversaries as well.

The chief problem with cyber security is that most of our tools and workforce is geared to waiting for adverse events, detecting those events (sometimes months after the fact), investigating the breach that has already occurred, and then cleaning up. This slow and reactive process ensures breaches happen and security staff us overwhelmed under the noise.

This talk will focus on automation and machine learning techniques that can proactively identify threats seen in the wild based on the latest academic research. This techniques allow organizations to identify suspect infrastructure before it is used to attack them. The key to making this work is infusing machine learning with knowledge of how actual attacks work and the threat landscape. Machine learning without intelligence is merely gussied up mensa math exercises.

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.

Riltok is a mobile banking Trojan that uses mobile phishing pages to steal credit card information from its victims. Discovered in 2018, Riltok started out solely attacking Russian targets, yet it quickly began attacking victims in other European countries as well. The Trojan is spread via malicious SMS messages, which contain links that direct the victims to a fake website posing as a popular free ad service.

Once on the website, victims are prompted to click and download the Trojan, disguised as the ad service’s mobile app. If downloaded, Riltok connects to its C&C server to exfiltrate device data, and opens a fake Google Play screen or phishing page in a browser, requesting the victim’s bank card details.

You’ve probably heard of Threat Intelligence, it's all the rage and all the cool kids are doing it… where’ve you been? Threat Intelligence, or “TI,” is everywhere and in everything, and it can be cool, but it can also be slippery and confusing and complex and a huge waste of time and resources depending on what you do (or don’t do) with it. In this post, we’re going to make a bunch of snarky statements about Threat Intelligence, and we’re going to spill the tea on how you (as a small or medium sized business) can use it and actually get some security value in return.

In retaliation for ongoing attacks against US interests and to be a deterrent against future cyberattacks, the United States has been penetrating Russian power and industrial systems according to recent reporting in the New York Times. There have been multiple articles about attacks on critical infrastructure and attempts to penetrate systems in this space. In the US, no breach has been reported to lead to a wide spread outage, but there has been an increasing level of concern.

A few months ago, JasperLoader (a new malware loader) emerged, infecting systems with various malware payloads, such as the Gootkit Banking Trojan. After a short, initial campaign, the threat actors behind the malware halted their activity and JasperLoader went off the radar for a while. However, since late May, a new and upgraded version of JasperLoader has been spotted infecting machines across Europe.

One of the challenges in threat intelligence is taking the massive amount of data we have about the threat landscape and distilling it into its most relevant components. A huge part of the reason for growth in data science (and in cyber security specifically) is habitually struggling with too much information. (With some exceptions) With this roadblock, it’s a challenge to focus in on the data that’s truly relevant.

Georgia Tech recently notified almost 1.3 million people about a potential breach of sensitive data, and in some cases, including a social security number. Over a four month period, there was a vulnerable server that allowed people to enumerate records on a back-end database, allowing the exfiltration of sensitive information. While universities are seen as more open environments, they do have sensitive information they have to protect.