Michael Gorelik, Morphisec Chief Technology Officer and Head of Morphisec Labs will be hosting the first in a series of threat analysis workshops/discussions on Thursday, September 27, 2018.
A new highly sophisticated botnet incorporating numerous malicious, evasive techniques is quickly spreading its tentacles. Dubbed MyloBot, the botnet uses an usually complex chain attack and combines multiple anti-analysis techniques to make it more difficult to detect the payload and harder to analyze by security researchers. Initial research published by Deep Instinct points out that everything on the victim’s end takes place in memory, while the main business logic of the botnet is executed in an external process using code injection. This makes it even harder to detect and trace.
Two weeks ago, Morphisec Lab, led by VP R&D Michael Gorelik, warned of a new attack by the FIN7 cybercrime group against restaurants across the US. Earlier this year, the financially motivated FIN7 group, one of the leading threat actor groups operating today, targeted restaurant chains Chipotle, Baja Fresh and Ruby Tuesday, among others. And you certainly remember the massive 2016 attack on the Wendy’s fast food chain, which resulted in over 1000 Wendy’s locations hit by a credit card breach. Numbers were also big in the Arby’s data breach discovered in January 2017: according to the credit union service PSCU, 350,000 credit and debit card accounts might have been impacted by the hack on Arby’s point-of-sale (PoS) systems.
Fueled by access to ever-increasing computational power, the past few decades have seen an explosion in Artificial Intelligence (AI) capabilities and applications. Today, AI is used in everything from image and speech recognition, to recommendation systems, to biomedical informatics to self-driving cars. Recently, various cyber security vendors are adapting "AI Technologies" in their products in order to improve the detection rate of malware and attacks. In particular, AI is expected to slowly replace the old-style signature-based detection of malware. Signature-based detection has proved to be ineffective against today's "one-million-new-samples-per-day" malware variants. But what does it really mean to use AI in detection of attacks and malware; can it really live up to its promises?
This article previously appeared on Information Management. Mordechai Guri is Chief Science Officer at Morphisec.
National Cybersecurity Awareness Month closed by focusing on scenarios straight out of action movies and nightmares – attacks on our critical infrastructure. These days, however, the threat is more likely to come from an innocent seeming email than bomb-toting terrorists à la Die Hard.
Utilities, hospitals, transportation systems, and all the other systems our communities and countries depend on are increasingly digitally controlled and connected. This brings tremendous productivity and reliability gains: better alignment of supply and demand, predictive maintenance planning, predictive outage response, instantaneous sharing of vital data and more. In some cases, like health care, it can make the difference between life and death.
Imagine this. You are in charge of public health and must deal with an unrelenting epidemic. You have two options for protecting the population.
The first option is to monitor each person for symptoms of infection. You buy analytical technology and infrastructure, hire staff and build hospitals. You send forth specialists to monitor everyone. When they notice symptoms, more tests are performed. The symptoms are
subtle (fatigue, headache, stiffness), and healthy and sick people look a lot alike, so to be on the safe side you test far more people than are truly ill. Once you suspect infection, you quarantine the person and start a course of treatment. Sometimes the people are cured. Sometimes they are not. You can’t guarantee that you will find everyone who is infected. Or that everyone you treat is ill. The monitoring and mandatory quarantine intrude on civil liberties, disrupt lives and interfere with the economy. To compound matters, the disease mutates, so you have to continually design new screening tests and retrain the specialists.
With fileless malware popping up more and more frequently, particularly sophisticated PowerShell attacks, we thought it useful to examine these threats by reverse engineering those in-memory samples from Virus Total that have the lowest detection rates.
The technology research group TechTarget recently published their findings from a survey on endpoint security at medium to large enterprises. The results corroborate trends all too evident in the news: Despite the features and functionality added to endpoint protection software over the last few years, “organizations are still in search of effective protection techniques against unknown threats and malware.”
The Carbanak APT group, aka “Anunak,” (dubbed Carbanak by Kaspersky Labs to reflect its Carberp origins) is one of the most notorious cybercriminal groups to target the Financial sector. Since Carbanak was first released in December 2014, around 100 financial institutions in approximately 30 countries have fallen victim to it, losing nearly $1 billion. Carbanak attacks begin with malware infected documents sent as email attachments to targeted bank employees. The malicious document is accompanied by an email message establishing an innocent seeming context. Once activated, the document delivers the malware, usually by exploiting an unpatched Office application vulnerability, in this case Microsoft Word. After obtaining the required credentials / data from the unprotected target victims, the Carbanak malware continues to its next stage of infiltrating the financial institution’s network.