When malware is difficult to discover — and has limited samples for analysis — we propose a machine learning model that uses adversarial autoencoder and semantic hashing to find what bad actors try to hide. We, along with researchers from the Federation University Australia, discussed this model in our study titled “Generative Malware Outbreak Detection.”
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Cybercriminals have become more and more creative and efficient in their efforts to successfully bypass network security. Reports of unauthorized network intrusions that have compromised enterprise security, resources, and data, plague experts on a day-to-day basis, and will continue to do so if not prevented by a more efficient detection system or method. Currently, attackers use polymorphism, encryption, and obfuscation, among other techniques, to automate and increase variants in an attempt to evade traditional intrusion detection methods such as rule-based techniques.
To address these growing number of network threats and keep abreast with the changing sophistication of network intrusion methods, Trend Micro looked into network flow clustering — a method that leverages the power of machine learning in strengthening current intrusion detection techniques.
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Microsoft’s SettingContent-ms has become a recent topic of interest. In July, we saw one spam campaign use malicious SettingContent-ms files embedded in a PDF to drop the remote access Trojan FlawedAmmyy, a RAT also used by the Necurs botnet. That campaign was mostly targeting banks in different countries across Asia and Europe.
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While ransomware has noticeably plateaued in today’s threat landscape, it’s still a cybercriminal staple. In fact, it saw a slight increase in activity in the first half of 2018, keeping pace by being fine-tuned to evade security solutions, or in the case of PyLocky (detected by Trend Micro as RANSOM_PYLOCKY.A), imitate established ransomware families and ride on their notoriety.
In late July and throughout August, we observed waves of spam email delivering the PyLocky ransomware. Although it tries to pass off as Locky in its ransom note, PyLocky is unrelated to Locky. PyLocky is written in Python, a popular scripting language; and packaged with PyInstaller, a tool used to package Python-based programs as standalone executables.
Read MoreThe ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS) is an avenue for cybersecurity research breakthroughs, techniques, and tools. At the ACM ASIACCS 2018 in Incheon, South Korea, we presented our research using DefPloreX-NG, a tool for identifying and tracking web defacement campaigns using historical and live data. “DefPloreX-NG” is a play on the phrase “defacement explorer.” The appended “NG” acronym means “Next Generation,” signifying improvements from the previous version of the tool. DefPloreX-NG is equipped with an enhanced machine learning algorithm and new visualization templates to give security analysts and other professionals a better understanding of web defacement campaigns.
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