Trend Micro envisages a new era as cybercriminals use machine learning to create advanced threats

Cybersecurity companies use machine learning technology to enhance threat detection capabilities that help fortify organizations’ defense against malware, exploit kits, phishing emails, and even previously unknown threats, Trend Micro revealed. 

The Capgemini Research Institute conducted a study on the usage of machine learning for security and found that of 850 senior executive respondents based in 10 countries, around 20 percent started using the technology before 2019, and about 60 percent will be using it by year’s end.

The use of machine learning in cybersecurity — not to mention in many other fields across various industries — has proven to be beneficial. This technology, however, is also at risk of being used by threat actors. While widespread machine learning weaponization may still be far off, research concerning this area, particularly the use of deepfake technology to extort and misinform, have recently become a topic of interest for the IT community and the general public.

To get a clearer picture of what is possible and what is already a reality with regard to machine learning-powered cyberthreats, research on machine learning-powered malware is still surprisingly scarce, considering that some experts have long considered it as a type of threat that can possess advanced capabilities. In fact, only one PoC of such a threat has been publicized, which was unveiled at Black Hat USA 2018. 

IBM presented a variant named DeepLocker that can deploy untraceable malicious applications within a benign data payload. The malware variant is supported by deep neural networks (DNN) or deep learning, a form of machine learning. The use of DNN disguises the malware’s conditions, which are pieces of information that security solutions need to detect malicious payload.

DeepLocker is designed to hide until it detects a specific victim. In the demonstration, DeepLocker was seen stealthily waiting for a specific action that will trigger its ransomware payload. The action that triggered the payload was the body movement of a targeted victim when he/she directly looked at a laptop webcam, which is operated by a webcam application embedded with malicious code. The application of machine learning in this attack can be considered limited, but it showed how malware variants can be highly evasive and targeted when infused with machine learning.

Experts are increasingly warning the public about deepfake videos, which are fake or altered clips that contain hyperrealistic images. Produced from generative adversarial networks (GANs) that generate new images from existing datasets of images, deepfake videos can challenge people’s perception of realities, confusing our ability to discern what is true from false.

Deepfake technology is mostly used in videos involving pornography, political propaganda, and satire. As for the impact of these videos, a Medium article published in May claimed that there were about 10,000 deepfake videos online, with House Speaker Nancy Pelosi and Hollywood star Scarlett Johansson being two of their popular subjects/victims.

Regarding the use of this technology in profit-driven cybercrime, it can be surmised that deepfake videos may be used to create a new variation of business email compromise (BEC) or CEO fraud. In this variation of the scheme, a deepfake video can be used as one of its social engineering components to further deceive victims.

But the first reported use of deepfake technology in CEO fraud came in the form of audio. In September, a scam involving a deepfake audio was used to trick a U.K.-based executive into wiring US$243,000 to a fraudulently set-up account. The victim company’s insurance firm stated that the voice heard on the phone call was able to imitate not only the voice of the executive being spoofed, but also the tonality, punctuation, and accent of the latter.

Brute force and social engineering methods are old but popular techniques that cybercriminals use to steal passwords and hack user accounts. New ways to do this could be inadvertently aided by user information shared on social media – some still embed publicly shared information into their account passwords. Additionally, machine learning research on password cracking is an area of concern that users and enterprises should closely pay attention to.

Back in 2017, one of the early proofs of machine learning’s susceptibility to abuse was publicized in the form of PassGAN — a program that can generate high-quality password guesses. Using a GAN of two machine learning systems, experts from the Stevens Institute of Technology, New Jersey, USA, were able to use the program to guess more user account passwords than popular password cracking tools HashCat and John the Ripper.

To compare PassGAN with HashCat and John the Ripper, the developers fed their machine learning system more than 32 million passwords collected from the 2010 RockYou data breach, and let it generate millions of new passwords. Subsequently, it attempted to use these passwords to crack a hashed list of passwords taken from the 2016 LinkedIn data breach.

The results came back with PassGAN generating 12 percent of the passwords in the LinkedIn set, while the other tools generated between 6 percent and 23 percent. But when PassGAN and Hashcat were combined, 27 percent  of the passwords from the LinkedIn set were cracked. If cybercriminals are able to devise a similar or enhanced version of this methodology, it could be a potentially reliable way to hijack user accounts.

Adversarial machine learning is a technique that threat actors can use to cause a machine learning model to malfunction. They can do so by crafting adversarial samples, which are modified input fed to the machine learning system to mess up its ability to predict accurately. In essence, this technique — also called an adversarial attack — turns the machine learning system against itself and the organization running it.

This method has been proven capable of causing machine learning models for security to perform poorly, for example, by making them produce higher false positive rates. They can do this by injecting malware samples that are similar to benign files to poison machine learning training sets.

Machine learning models used for security can also be tricked using infected benign Portable Executable (PE) files or a benign source code compiled with malicious code. This technique can make a malware sample appear benign to models, preventing security solutions from accurately detecting it as malicious since its structure is still mostly comprised of the original benign file.

When it comes to dealing with advanced password cracking tools such as the machine learning-powered PassGAN, users and organizations can move towards two-factor authentication schemes to reduce their reliance on passwords. One approach to this is using a one-time password (OTP) — an automatically generated string of characters that authenticates the user for a single login session or transaction.

Meanwhile, technologies are continuously being developed to defend against deepfakes. To detect deepfake videos, experts from projects initiated by the Pentagon and SRI International are feeding samples of real and deepfake videos to computers. This way, computers can be trained to detect fakes. 

To detect deepfake audio, experts are training computers to recognize visual inconsistencies. And as for the platforms where deepfakes can creep in, Facebook, Google, and Amazon, among other organizations, are joining forces to detect them via the DeepFake Detection Challenge (DFDC) — a project that invites people around the world to build technologies that can help detect deepfakes and other forms of manipulated media.

Adversarial attacks, on the other hand, can be prevented by making machine learning systems more robust. This can be done in two steps: First, by spotting potential security holes early on in its design phase and making every parameter accurate, and second, by retraining models via generating adversarial samples and using them to enhance the efficiency of the machine learning system. 

Reducing the attack surface of the machine learning system can also ward off adversarial attacks. Since cybercriminals modify samples in order to probe a machine learning system, cloud-based solutions, such as products with Trend Micro XGen security, can be used to detect and block malicious probing.

Governments and private organizations, particularly cybersecurity companies, should anticipate a new era where cybercriminals use advanced technologies such as machine learning to power their attacks. As they have done in the past, cybercriminals will continue to develop more advanced and new forms of threats to be one step ahead. In this light, technologies for combating these threats should likewise continue to evolve. 

However, while it would be a good choice to implement a tailor-fit technology to detect such threats, a multilayered security defense (one that combines a variety of technologies) and the consistent application of cybersecurity best practices are still the most effective ways to defend against a wide range of threats.

Kaspersky sees a sky-rise of droppers with phishing and malware attacks surface amid premiere of famous space saga

According to research from Kaspersky, the latest and final film of the trilogy has drawn the attention of attackers even before the premiere, with fraudulent websites and malicious files of the yet-to-be-released film flooding the web. Popular films are often used by cybercriminals as bait to distribute malware, and the latest movie saga from ‘a galaxy far, far away’ is no exception. 

Films are one of the main forms of entertainment users seek to access for free, which creates fertile soil for cyberattacks. Online streaming, torrents and other methods of digital distribution often infringe upon content copyright, and yet they remain popular as a source of free content. 

Torrent-trackers and illegal streaming platforms pose a threat to users’ cyber-safety, since they can host malicious files, masked behind the name of movie files. Given this tendency, Kaspersky studied how the sci-fi franchise’s name is being abused by cybercriminals in order to fool fans.

Public attention on “Star Wars: The Rise of Skywalker,” which premieres Dec. 19, is already attracting cybercriminals. Kaspersky researchers found over 30 fraudulent websites and social media profiles disguised as official movie accounts (the actual number of these sites may be much higher) that supposedly distribute free copies of the latest film in the franchise. These websites collect unwary users’ credit card data, under the pretense of necessary registration on the portal.

The domains of websites used for gathering personal data and spreading malicious files usually copy the official name of the film and provide thorough descriptions and supporting content, thereby fooling users into believing that the website is, in some way, connected to the official film. 

Such practice is called “black SEO,” which enables criminals to promote phishing websites high up in search engine results (such results often show up for search terms such as ‘name-of-the-film watch free’).

To further support the promotion of fraudulent websites, cybercriminals also set up Twitter and other social media accounts, where they distribute links to the content. Coupled with malicious files shared on torrents, this brings the criminals results. So far, 83 users have already been affected by 65 malicious files disguised as copies of the upcoming movie.

Phishing is not the only way cybercriminals tend to utilize popular film franchises. Just as with TV shows, they often disguise malicious programs as yet another episode of the story. In 2019, Kaspersky detected 285,103 attempts to infect 37,772 users seeking to watch movies of the renowned space-opera series, a 10 percent rise compared to last year. The number of unique files used to target the users amounted to 11,499, a 30 percent drop from last year.

“It is typical for fraudsters and cybercriminals to try to capitalize on popular topics, and ‘Star Wars’ is a good example of such a theme this month,” said Tatiana Sidorina, security researcher at Kaspersky. “As attackers manage to push malicious websites and content up in the search results, fans need to remain cautious at all times. We advise users to not fall for such scams and instead enjoy the end of the saga on the big screen.”

Users have been advised to take the following steps by paying attention to the official movie release dates in theaters, on streaming services, TV, DVD, or other sources; avoid clicking on suspicious links, such as those promising an early view of a new film; and paying special attention to the downloaded file extension. The file should have an .avi, .mkv or .mp4 extension, among other video formats, and not the suspicious .exe extension.

Consumers must check the website’s authenticity, and avoid visiting websites to watch a movie until they are sure that they are legitimate and start with ‘https.’ Confirm that the website is genuine by double-checking the format of the URL or the spelling of the company name, reading reviews about it and checking the domains’ registration data before starting downloads. It also uses reliable security solution, such as Kaspersky Security Cloud, for comprehensive protection from a range of threats.

Emotet security attack causes shutdown of Frankfurt’s IT network

The city of Frankfurt, Germany, became the latest victim of Emotet after an infection forced it to close its IT network. But the financial center wasn’t the only area that was targeted by Emotet, as there were also incidents that occurred in Gießen and Bad Homburg, a town and a city north of Frankfurt, respectively, as well as in Freiburg, a city in southwest Germany.

The infection started after an employee of the Fechenheim (a district in Frankfurt) civil registry clicked on an Emotet-laden attachment from a malicious spam email, apparently sent by a city authority. Alarms were raised by the security system, prompting officials to restrict city services and take the IT system off the network as a precautionary measure.  

Germany has been a frequent target over the past few weeks by threat actors employing Emotet, and in general has been a target for malicious activity this year, according to data from the Trend Micro Smart Protection Network infrastructure. In fact, the German Federal Office for Information Security (BSI) issued a press release warning the public about malicious spam emails that carry Emotet.

First detected in 2014, Emotet has become one of the most notorious malware families of the past few years. Its original iteration was as an information-stealing banking malware — however, it has since undergone multiple evolutions, including acting as a loader for other malware families. It went into hiatus earlier in the year but came back after a few months with a vengeance. This recent spate of attacks on Germany is likely a continuation of Emotet’s comeback campaigns.

Despite all the changes Emotet has undergone, spam mail remains the malware’s most prominent distribution method. The key strategy organizations can implement is to educate their employees regarding email threats and to encourage them to follow the recommended security best practices when accessing their emails. This includes always double-checking an email for any red flags, as well as refraining from clicking any links or downloading any attachments haphazardly.

Combating threats like Emotet calls for a multilayered and proactive approach to security that involves protecting all fronts — gateway, endpoints, networks, and servers. Trend Micro endpoint solutions such as Trend Micro Smart Protection Suites and Worry-Free Business Security can protect users and businesses from these threats by detecting malicious files and spammed messages, as well as blocking all related malicious URLs.

To bolster their security capabilities and further protect their end users, organizations can consider security products such as the Trend Micro Cloud App Security solution, which uses machine learning (ML) to help detect and block spam and phishing attempts. 

If a malicious email is received by an employee, it will go through sender, content, and URL reputation analysis, which is followed by an inspection of the remaining URLs using computer vision and AI to check if website components are being spoofed. The solution can also detect suspicious content in the message body and attachments and provide sandbox malware analysis and document exploit detection.