Even before the term IoT was coined, we had the routers at the gateway, most of the time publicly exposed on the internet. In the context of the IoT, the router is perhaps the most important device for the whole infrastructure. All traffic goes through it and it allows for the provision of many services, such as Dynamic Host Configuration Protocol (DHCP), Domain Name System (DNS), content filtering, firewalls, and Voice over Internet Protocol (VoIP), to all connected devices, including computers, smartphones, and IP cameras. If an attacker is able to compromise the router, every device connected to it can be affected. And that’s what a hacking group in Brazil just did.Read More
In this blog post, we will discuss how we developed a human-readable machine learning system that is able to determine whether a downloaded file is benign or malicious in nature.
The development of this actionable intelligent system stemmed from the question: How can we make our knowledge about global software download events actionable? More specifically, how can we use such information to do a better job at detecting the threats posed by the large amounts of new malicious software circulating on a daily basis?
In this last installment of this blog series, we will answer such questions and give a summary of what we did with the information we’ve obtained. Our research paper titled Exploring the Long Tail of (Malicious) Software Downloads provides a more comprehensive look into how we’ve gathered and analyzed our software downloads data.Read More
Using a machine learning system, we analyzed 3 million software downloads, involving hundreds of thousands of internet-connected machines, and provide insights in this three-part blog series. In the first part of this series, we took a closer look at unpopular software downloads and the risks they pose to organizations. We also briefly mentioned the problem regarding code signing abuse, which we will elaborate on in this post.
Code signing is the practice of cryptographically signing software with the intent of giving the operating system (like Windows) an efficient and precise way to discriminate between a legitimate application (like an installer for Microsoft Office) and malicious software. All modern operating systems and browsers automatically verify signatures by means of the concept of a certificate chain.
Valid certificates are issued or signed by trusted certification authorities (CAs), which are backed up by parent CAs. This mechanism relies entirely and strictly on the concept of trust. We assume that malware operators are, by definition, untrustworthy entities. Supposedly, these untrustworthy entities have no access to valid certificates. However, our analysis shows that is not the case.Read More
As a large cyber security vendor, Trend Micro deals with millions of threat data per day. Our Smart Protection Network (SPN), among other technologies, helps us conduct research and investigate new threats and cybercrimes to improve our ability to protect our customers.
In this blog post, the first of a three-part series, I would like to share some insights on trends that we have observed in the wild after analyzing 3 million software downloads, involving hundreds of thousands of internet-connected machines.
Specifically, we turn our focus on web downloads originating from browsers or any other (HTTP) client application installed on a machine. Note that we limited the study to machines that execute software after download. Given the huge quantity of data, we also limited our research to unpopular software downloaded from URLs that were not whitelisted. This automatically excludes software from Windows Updates and other well-known domains. All this information is PII anonymized.
We classify these downloads as benign (legitimate software), malicious or unknown. Unknown means that the downloaded software is currently unknown to us or to other public data sources that we monitor.Read More
As new trends and developments in the malicious mining of cryptocurrency emerge, a smart and sustainable way of detecting these types of threats is swiftly becoming a cybersecurity necessity. By using Trend Micro Locality Sensitive Hashing (TLSH), a machine learning hash that is capable of identifying similar files, we were able to group together similar cryptocurrency-mining samples gathered from the wild. By grouping together samples based on their behavior and file types, detection of similar or modified malware becomes possible.Read More