The Cyber Threat Intelligence (CTI) field has evolved rapidly and most of its reporting is now fairly stan-dardized. Where the Cyber Kill Chain was its sole reference framework 5 years ago, today ATT&CK is the de facto standard for reporting adversary tactics, techniques and procedures (TTPs). CTI frameworks are effectively abstraction layers of malicious behavior and thus effective CTI dissemination hinges on their ability to accurately represent this behavior. We argue that this is an area with significant opportunity for improvement. The aforementioned models are attacker- and intrusion-centric, while much of the CTI reporting currently is artifact- and malware-centric. In other words, most analysis is performed using artifacts of adversary tools, while in-the-wild evidence of adversary techniques and procedures is limited or lacking. Applying an intrusion model to artifact-based analysis leads to information loss, affecting and potentially misleading CTI-based decision-making. Intelligence analysis naturally builds on imperfect information, but CTI frameworks should be oriented more towards this key premise. In this conceptual work we compare the intrusion-centric ATT&CK with Malware Behavior Catalog (MBC), which is malware-centric. We compare how their application affects reporting of analysis outcomes. For this we reverse a piece of APT malware, replicating how many CTI reports are produced. We find that compared to ATT&CK, the abstraction offered by MBC enhances the information density of our reporting. While currently in most industry malware reports ATT&CK is applied, our analysis shows that on these occasions using MBC, potentially in tandem with ATT&CK, improves reporting. With the daily amount of new malware samples only increasing, accurate behavior labeling is key to the success of CTI sharing and dissemination.

Amplification attacks generate an enormous flood of unwanted traffic towards a victim and are generated with the help of open, unsecured services, to which an adversary sends spoofed service requests that trigger large answer volumes to a victim. However, the actual execution of the packet flood is only one of the activities necessary for a successful attack. Adversaries need, for example, to develop attack tools, select open services to abuse, test them, and adapt the attacks if necessary, each of which can be implemented in myriad ways. Thus, to understand the entire ecosystem and how adversaries work, we need to look at the entire chain of activities.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target an ecosystem’s weakest points. In Bitcoin, the exchange platforms are one of them. Each exchange breach is a threat not only to direct victims, but to the credibility of Bitcoin’s entire ecosystem. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We show that while the security regimen of Bitcoin exchanges is subpar compared to other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. Furthermore we show that overall security posture has improved, but still has major flaws. To discover adversarial methods post-breach, we have analyzed two cases of BTC laundering. Through this analysis we provide insight into how exchange platforms with lax cyber security even further increase the intermediary risk introduced by them into the Bitcoin ecosystem.