In the past year, the healthcare industry has been increasingly targeted by advanced cyber-attacks. While a marked rise in medical IoT devices has allowed healthcare companies to become much more efficient, this increase has also opened new avenues for threat actors attempting to infiltrate their networks.
Medical staff now carry multiple connected devices with them, including personal devices that lack appropriate security controls. Confidential patient records and life-critical medical systems run an increased risk of being compromised, and the sensitive nature of the information they contain could impact patient safety and hospital reputation. Financially, healthcare companies are also at greater risk: according to a study by the Ponemon Institute, lost or stolen healthcare records can cost up to 136% more than data breached in other industries.
Going for gold
Towards the end of last year, we observed a noticeable spike in the number of crypto-mining infections within the healthcare sector. In December 2017 alone, the number of crypto-malware attempts on healthcare customers’ systems was 800% higher than in the six months prior and following.
Whilst healthcare companies have always been the target of malware infections, the sudden increase in crypto-malware was significant. This could be attributed to the price of Bitcoin and similar cryptocurrencies skyrocketing around the same time. As their price has now fallen, so have the crypto-mining attempts.
Breaking through Windows
Although 2018 brought with it a decrease in crypto-mining attempts, the healthcare sector experienced an increase in active malware infections captured by sinkhole domains. The infections were varied, with no bias towards botnets, trojans, or ransomware, but were almost entirely united in that the threat actors widely targeted outdated Windows operating systems.
The risks of the EternalBlue SMB vulnerability are now well known. However, as learnt in the aftermath of WannaCry, entire NHS trusts are also susceptible to other unpatched Windows 7 vulnerabilities, including those that facilitate remote code execution and privilege escalation – prime pickings for any malware that successfully enters a system.
Hiding in plain sight
A private medical institution recently trialed Darktrace’s Enterprise Immune System technology through a Proof of Value. Darktrace immediately discovered that an AXIOS spectrometer, a medical IoT device for characterizing materials using x-ray, had been compromised. It had breached hundreds of models, many of a potentially serious nature. The device was continuously making outbound SSH connections to rare external IP addresses, transferring over 1GB of data a week.
Further analysis determined that the compromised medical device was being used to send large volumes of outbound spam mail, resulting in the medical institution’s external IP address being blocked by spam filters. Effectively classified as a sender of junk mail, emails from the medical institution risked falling into recipients’ trash or not being received at all – anything from appointment updates, to the results of cancer scans. Faith in the institution’s ability to handle patient data and uphold its duty of care could have been severely undermined, risking its reputation among prospective patients and service providers.
Likely C2 beaconing was also noted from this device, indicating that it might have been part of a wider botnet, or network of compromised devices being used to propagate malicious spam malware. On further investigation, at least one of the HTTP connections was to a server utilized within cryptocurrency exchange and bitcoin activity, which suggests a crypto-mining malware presence. The institution’s security team were advised immediately. The device was then isolated, giving the team precious time to conduct further investigation.
The healthcare sector is a clear target for threat actors, especially considering the wealth of sensitive data such networks safeguard, and the security holes left open in the challenge to continuously maintain and patch highly complex and distributed networks. WannaCry and Petya ransomware were unlikely to have been the last aggressive attacks that successfully exploit such vulnerabilities.
Insider threat is also manifest in healthcare networks. User compliance problems are prevalent, for example, there is a sizable use of Tor as the preferred VPN, widespread use of BitTorrent, and a high volume of illicit uploads to cloud storage services.
Darktrace’s technology has the unique ability to detect and respond to in-progress cyber-attacks that would ordinarily bypass traditional security tools. As threat actors are continually employing novel methods to compromise a network, a growing number of healthcare companies are now having to play catch-up in a fast-evolving threat landscape.
As the market increasingly moves to the next wave of computing models, over 90% of organizations are expected to adopt hybrid infrastructures by 2020. This move to the cloud brings undeniable benefits for most organizations - from start-ups looking for minimal up-front costs to large organizations striving to boost efficiency, scale on demand, and benefit from constant availability of services and increased agility.
Alongside this growth, the challenge of securing critical data in the cloud has taken on a new dimension. As internal servers are so commonly affected by malware infections or insider threats, there exists a common misconception that the data stored within the cloud is somehow more secure than the data resting on company fileservers. However, this is not necessarily the case – the information stored on cloud infrastructure may be just as (un)safe as any other corporate data store.
Much of this risk comes from the misconception of the network position of cloud servers themselves. Although rented out for use by the company and used every day as part of fundamental business purposes, connections to cloud servers (if not facilitated by a VPN or other strong encrypted channels) cross the perimeter of the network and traverse the public internet. This means that data uploaded to and from the cloud is a prime target for man-in-the-middle attacks, carried out by opportunistic actors hoping to sniff usernames, passwords, and other sensitive details that they could then leverage for direct corporate data theft.
The reality is that while organizations can outsource their IT services, they cannot outsource their security function altogether. In fact, protecting the cloud comes with its own challenges, with most of the existing native security controls and third-party security solutions suffering from significant limitations.
Customer use case
A city government in the United States had outsourced the storage of SQL databases to a cloud storage provider. However, it had not interrogated the protocols that the server by default employed to upload and download information. Addresses, phone numbers, vehicle registration plate numbers: the city government was uploading it all to the external database via unencrypted connections. This highly sensitive data was intended for limited access by select employees within the city government, but the security oversight had made the data available to any attacker clued-up enough to park themselves on the perimeter of the network and collect the data-rich MySQL packets that came their way.
Darktrace Cloud detected an unusual SQL connection to a rare external IP from a desktop device within the company. This communication was verified as being SQL-related via packet capture, which then revealed the sensitive public data.
The customer was unaware of this vulnerability, which remained under the radar of its entire security stack. An attacker could easily exploit it to gather material for spear phishing attacks or potentially even identity fraud.
In order to reduce risk and identify atypical or suspicious behavior, full visibility of all cloud services is critical, as hosting data on external servers can create dangerous blind spots and introduce subtle threats that circumvent traditional signature-based tools.
Already over 500 Darktrace customers use Darktrace Cloud to defend cloud environments and SaaS applications, including AWS, Microsoft Azure, Salesforce, and Google Cloud Platform. Darktrace provides businesses with fundamental visibility and real-time threat detection across their entire distributed infrastructures. Through the power of unsupervised machine learning, businesses are now able to confidently tackle the potential risks of data leakage and man-in-the-middle attacks that can affect cloud users.
JA3 is a methodology for fingerprinting Transport Layer Security applications. It was first posted on GitHub in June 2017 and is the work of Salesforce researchers John Althouse, Jeff Atkinson, and Josh Atkins. The JA3 TLS/SSL fingerprints created can overlap between applications but are still a great Indicator of Comprise (IoC). Fingerprinting is achieved by creating a hash of 5 decimal fields of the Client Hello message that is sent in the initial stages of an TLS/SSL session.
JA3 is an interesting approach to the increasing usage of encryption in networks. There is also a clear uptick in cyber-attacks using encrypted command and control (C2) channels – such as HTTPS – for malware communication.
The benefits of JA3 for enhancing rules-and-signatures security
These near-unique fingerprints can be used to enhance traditional cyber security approaches such as whitelisting, blacklisting, and searching for IoCs.
Let’s take the following JA3 hash for example: 3e860202fc555b939e83e7a7ab518c38. According to one of the public lists that maps JA3s to applications, this JA3 hash is associated with the ‘hola_svc’ application. This is the infamous Hola VPN solution that is non-compliant in most enterprise networks. On the other hand, the following hash is associated with the popular messenger software Slack: a5aa6e939e4770e3b8ac38ce414fd0d5. Traditional cyber security tools can use these hashes like traditional signatures to search for instances of them in data sets or trying to blacklist malicious ones.
While there is some merit to this approach, it comes with all the known limitations of rules-and-signatures defenses, such as the overlaps in signatures, the inability to detect unknown threats, as well as the added complexity of having to maintain a database of known signatures.
JA3 in Darktrace
Darktrace creates JA3 hashes for every TLS/SSL connection it encounters. This is incredibly powerful in a number of ways. First, the JA3 can add invaluable context to a threat hunt. Second, Darktrace can also be queried to see if a particular JA3 was encountered in the network, thus providing actionable intelligence during incident response if JA3 IoCs are known to the incident responders.
Things become much more interesting once we apply our unsupervised machine learning to JA3: Darktrace’s AI algorithms autonomously detect which JA3s are anomalous for the network as a whole and which JA3s are unusual for specific devices.
It basically tells a cyber security expert: This JA3 (3e860202fc555b939e83e7a7ab518c38) has never been seen in the network before and it is only used by one device. It indicates that an application, which is used by nobody else on the network, is initiating TLS/SSL connections. In our experience, this is most often the case for malware or non-compliant software. At this stage, we are observing anomalous behavior.
Darktrace’s AI combines these IoCs (Unusual Network JA3, Unusual Device JA3, …) with many other weak indicators to detect the earliest signs of an emerging threat, including previously unknown threats, without using rules or hard-coded thresholds.
Catching Red-Teams and domain fronting with JA3
The following is an example where Darktrace detected a Red-Team’s C2 communication by observing anomalous JA3 behavior.
The unsupervised machine learning algorithms identified a desktop device using a JA3 that was 100% unusual for the network connecting to an external domain using a Let’s Encrypt certificate, which, along with self-signed certificates, is often abused by malicious actors. As well as the JA3, the domain was also 100% rare for the network – nobody else visited it:
It turned out that a Red-Team had registered a domain that was very similar to the victim’s legitimate domain: www.companyname[.]com (legitimate domain) vs. www.companyname[.]online (malicious domain). This was intentionally done to avoid suspicion and human analysis. Over a 7-day period in a 2,000-device environment, this was the only time that Darktrace flagged unusual behavior of this kind.
As the C2 traffic was encrypted (therefore no intrusion detection was possible on the payload) and the domain was non-suspicious (no reputation-based blacklisting worked), this C2 had remained undetected by the rest of the security stack.
Combining unsupervised machine learning with JA3 is incredibly powerful for the detection of domain fronting. Domain fronting is a popular technique to circumvent censorship and to hide C2 traffic. While some infrastructure providers take action to prevent domain fronting on their end, it is still prevalent and actively used by attackers.
The only agreed-upon method within wide parts of the cyber-security community to detect domain fronting appears to be TLS/SSL inspection. This usually involved breaking up encrypted communication to inspect the clear-text payloads. While this works, it commonly involves additional infrastructure, network restructuring and comes with privacy issues – especially in the context of GDPR.
Unsupervised machine learning makes the detection of domain fronting without having to break up encrypted traffic possible by combining unusual JA3 detection with other anomalies such as beaconing. A good start for a domain fronting threat hunt? A device beaconing to an anomalous CDN with an unusual JA3 hash.
JA3 is not a silver bullet to pre-empt malware compromise. As a signature-based solution, it shares the same limitations of all other defenses that rely on pre-identified threats or blacklists: having to play a constant game of catch-up with innovative attackers. However, as a novel means of identifying TLS/SSL applications, JA3 hashing can be leveraged as a powerful network behavioral indicator, an additional metric that can flag the use of unauthorized or risky software, or as a means of identifying emerging malware compromises in the initial stages of C2 communication. This is made possible through the power of unsupervised machine learning.
Ransomware continues to be one of the most serious and disruptive cyber threats. The business models, motivations, and infection techniques of emerging campaigns have diversified, and new strands of ransomware continue to outpace the release of decryption tools. By 2019, global ransomware damage costs are expected to surpass $11.5 billion per year.
The three most memorable ransomware campaigns of 2017 - Wannacry, NotPetya, and Bad Rabbit - were ground-breaking in their scope, spread, and destructive power, demonstrating that every business, industry, and country is a potential victim. Although the damage caused by these attacks highlighted the importance of good cyber hygiene, many companies have struggled to address even the most widely reported vulnerabilities. As prevention is better than cure, this article will discuss some of the most common infection vectors and how the Darktrace Enterprise Immune System can assist security teams in catching ransomware threats.
Motivations: financial gain or wreaking havoc?
Ransomware is traditionally linked with making a quick buck by getting the victim to pay a set fee to unlock encrypted files. The phenomenon of ransomware-as-a-service has made this easier than ever before, as it has allowed virtually anyone to purchase ever more potent ransomware distribution kits on the Dark Web. The recent growth in cryptocurrencies has also made maintaining anonymity much easier than before, resulting in a definite increase in financially motivated cyber-criminals.
Regrettably, the goal of ransomware is no longer just to make money. NotPetya and other campaigns such as Ordinypt were designed to purposefully destroy data instead. Even though NotPetya provided its victims with payment instructions, it had no way of identifying who had actually made the payment. The uncertainty surrounding the recovery of lost files and the possibility of being associated with funding malicious organizations have therefore deterred many victims from meeting the ransom demands.
No matter how much a business tries to safeguard their assets, incidents are inevitable, and ransom attacks are an increasingly likely choice of criminal action. But it is now possible to identify in-progress attacks and handle them before they become a crisis.
Case Study 1: Executable file download from a compromised website
Many prolific ransomware strands have been distributed by phishing emails, infected file downloads, compromised websites, malvertising, and exploit kits. In many cases, ransomware is often downloaded and installed without the victim’s knowledge. To illustrate the ransomware download mechanics, we will analyze the life-cycle of a GandCrab incident. In the case study detailed below, the Darktrace Enterprise Immune System flagged a customer device retrieving an executable file from a previously unmonitored location following a redirection from another rare site.
The file containing ransomware was downloaded from a website registered to a Polish domain. Shortly after downloading the file, the customer’s device began reaching out to two locations which had not been contacted by any other network devices, nomoreransom.bit and bleepingcomputer.bit. Both are command and control servers for GandCrab ransomware. Once contacted, the malicious virus proceeded to encrypt files on the SMB server, adding the .GDCB (GandCrab) extension as it moved through the folders.
Within seconds of the virus appearing on the company’s network, the Darktrace Cyber Analyst team alerted the security team of the threat. Preventative action was then taken, which allowed the threat to be contained within a timely manner.
Case Study 2: Bruteforcing Remote Desktop Protocol access
In addition to devising clever ways of downloading ransomware onto victim’s machines, some hackers have turned to bruteforcing Remote Desktop Protocol (RDP) access instead (HC7 & Lockcrypt). Exposing Remote Desktop services to the Internet is risky, as attackers can force access into a network by guessing login information and remotely exploiting a range of possible vulnerabilities and administrative tools in order to infect other available machines.
In another particularly serious breach, Darktrace detected a series of suspicious activities indicating that a malicious actor had taken control of a key server and was using it as a pivot point in order to move laterally throughout the network and install Remote Access Tools (RATs) on multiple devices.
In the initial stage of the attack, the Darktrace Enterprise Immune System observed over 400,000 incoming connections on a port that was targeting devices with RDP turned on and immediately flagged the first signs of a bruteforce attack.
The attack was successful; a compromised server was then used to retrieve malware that granted backdoor access and scanned the network for devices with open RDP channel. The hacker subsequently tunneled through the intermediary, gained control over multiple other machines, and installed third-party remote access software to all available devices.
Although most RDP bruteforcing incidents the Darktrace Enterprise Immune System observes do not escalate this far, the Darktrace Cyber Analyst team are constantly flagging instances of publicly accessible remote management services. To prevent ransomware that specifically exploits insecure RDP configuration, businesses should move these critical services to a virtual private network. Moreover, with Darktrace Antigena, Darktrace’s autonomous response solution, businesses can benefit from an added layer of protection. In this case, it would have blocked any anomalous RDP connections to the server, thus preventing any lateral movement throughout the network.