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Les 9 étapes d'un ransomware : comment l'IA réagit à chaque étape



Si les ransomwares portent ce nom, c’est parce que leur but consiste à prendre possession de certains actifs et à exiger une rançon pour leur restitution. Les victimes versent cette rançon en échange de la discrétion des voleurs et de leur coopération pour rendre les données exfiltrées et fournir les clés de déchiffrement qui permettront la reprise de l’activité.
Le montant moyen des rançons monte en flèche. Il a atteint 5,3 millions de dollars en 2021, soit une augmentation de 518 % par rapport à l’année précédente. Mais le coût associé à la reprise de l’activité après une attaque par ransomware va bien au-delà du montant de la rançon : après une telle attaque, on constate en moyenne un temps d’arrêt de 21 jours et 66 % des victimes de ransomware rapportent une perte importante de chiffre d’affaires.
Dans cette série, nous analysons ce sujet critique en détail, étape par étape. Les ransomwares comportent plusieurs phases distinctes. Pour les combattre, vous avez besoin d’une solution multiphase qui neutralise chaque l’attaque à chaque stade efficacement et de façon autonome. Découvrir comment l’IA Auto-Apprenante et la Réponse Autonome stoppent les ransomwares instantanément.
1. Intrusion initiale (e-mail)
L’accès initial, première phase d’une attaque par ransomware, peut être établi par force brute sur RDP (service Internet vulnérable), via un site web malveillant ou par un téléchargement opportuniste, par un acteur interne malveillant doté d’identifiants appartenant à l’entreprise, par le biais de vulnérabilités de certains systèmes ou logiciels, ou par de nombreux autres vecteurs d’attaque.
Toutefois, l’e-mail demeure le vecteur d’attaque initial le plus courant. La plus grande faiblesse des organisations en matière de securité sont souvent leurs employés. Les cybercriminels le savent et débordent d’imagination pour exploiter cette vulnérabilité. Ils adressent des e-mails ciblés, bien documentés, à l’aspect légitime à certains employés spécifiques dans le but de susciter une réaction : cliquer sur un lien, ouvrir une pièce jointe, confier ses identifiants ou d’autres informations sensibles.
Les passerelles : stoppent ce qui a déjà été observé
La plupart des outils traditionnels de protection de la messagerie s’appuient sur des indicateurs issus d’attaques antérieures pour tenter de détecter la prochaine menace. Si un e-mail provient d’une adresse IP ou d’un domaine sur liste noire et qu’il utilise un malware connu qui a déjà été observé, il est alors possible de bloquer l’attaque.
En réalité, les cybercriminels savent que la majorité des outils de défense adoptent cette approche historique ; c’est pourquoi ils mettent constamment à jour leur infrastructure d’attaque afin d’échapper à ces outils. En achetant de nouveaux domaines pour quelques centimes, ou modifiant légèrement le code des malwares afin de créer des versions personnalisées, ils évitent et devancent l’approche héritée des passerelles e-mail traditionnelles.
Exemple de situation réelle : attaque par phishing de la chaîne d'approvisionnement
By contrast, Darktrace’s evolving understanding of ‘normal’ for every email user in the organization enables it to detect subtle deviations that point to a threat – even if the sender or any malicious contents of the email are unknown to threat intelligence. This is what enabled the technology to stop an attack that recently targeted McLaren Racing, with emails sent to a dozen employees in the organization each containing a malicious link. This possible precursor to ransomware bypassed conventional email tools – largely because it was sent from a known supplier – however Darktrace recognized the account hijack and held the email back.

2. Intrusion initiale (côté serveur)
Comme les organisations ont rapidement étendu leur périmètre connecté à Internet, c’est toute la surface d’attaque qui a augmenté. On a donc logiquement observé une explosion des attaques « traditionnelles » par force brute et côté serveur.
De nombreuses vulnérabilités touchant les serveurs et les systèmes connectés à Internet ont été révélées cette année. Du côté des cybercriminels, il n’a jamais été aussi facile de cibler les infrastructures connectées à Internet : des outils tels que Shodan ou MassScan permettent de parcourir facilement Internet à la recherche de systèmes vulnérables.
Les attaquants peuvent également établir un accès initial en employant une approche par force brute RDP ou des identifiants dérobés ; il est d’ailleurs fréquent de recycler des identifiants légitimes obtenus à partir de fuites de données antérieures. Cette méthode est bien plus précise et génère moins de bruit qu’une attaque par force brute classique.
De nombreux ransomwares utilisent le protocole RDP comme vecteur d’entrée. Cette méthode s’inscrit dans une tendance plus générale, celle du « Living off the Land » : il s’agit d’utiliser des outils légitimes disponibles dans le commerce (utilisation abusive des protocoles RDP ou SMB1 ou d’outils de ligne de commande WMI ou Powershell) pour désorienter les outils de détection et d’attribution en se fondant parmi les activités typiques des administrateurs. Il ne suffit pas de s’assurer que les sauvegardes sont isolées, les configurations renforcées et les correctifs système appliqués : la détection en temps réel de chaque action anormale est vitale.
Antivirus, pare-feu et SIEM
Les antivirus présents sur les endpoints sont uniquement capables de détecter les téléchargements de malware si ce malware a déjà été observé et consigné. Les pare-feu requièrent en général une configuration spécifique à chaque organisation ; ils doivent souvent être modifiés en fonction de l’évolution des besoins de l’entreprise. Si le pare-feu ne contient aucune règle ou signature correspondant exactement à l’attaque rencontrée, il la laissera passer.
Les outils SIEM et SOAR recherchent également des malwares connus en s’appuyant sur des règles prédéfinies et des réponses préprogrammées. Même si ces outils recherchent bel et bien des modèles anormaux, ces modèles sont définis à l’avance, et leur approche part du principe qu’une nouvelle attaque ressemblera suffisamment à des attaques déjà connues.
Exemple de situation réelle : ransomware Dharma
Darktrace a détecté au Royaume-Uni une attaque ciblée utilisant le ransomware Dharma, lancée en exploitant une connexion RDP ouverte sur des serveurs connectés à Internet. Le serveur RDP a commencé à recevoir un grand nombre de connexions entrantes provenant d’adresses IP rares via Internet. L’identifiant RDP utilisé pour cette attaque avait sans doute été compromis avant l’attaque, soit par force brute, soit par des attaques de type stuffing ou phishing. Parmi les techniques couramment utilisées, l’une des plus populaires en ce moment consiste à acheter des identifiants RDP et à passer directement à l’accès initial.

Figure 2 : violations de modèle déclenchées au cours de cette attaque, notamment concernant l’activité RDP anormale
Malheureusement, dans ce cas, la Réponse Autonome n’était pas installée et le ransomware Dharma a continué son déploiement jusqu’aux étapes finales. L’équipe de sécurité a alors dû intervenir de façon agressive en déconnectant le serveur RDP en plein chiffrement.
3. Mise en place de l’accès initial d’un canal C2
Que ce soit grâce au phishing, à une attaque par force brute ou toute autre méthode, le cybercriminel réussit à pénétrer le réseau. Il prend ensuite contact avec la ou les machine(s) compromise(s) afin d’établir une présence initiale.
Cette étape permet à l’attaquant de contrôler à distance les phases suivantes de l’attaque. Tout au long de ces communications de commande et contrôle (C2), d’autres malwares peuvent être transmis aux machines. Ils renforcent la présence de l’attaquant au sein de l’organisation et facilitent les déplacements latéraux.
Les cybercriminels peuvent adapter les fonctionnalités des malwares à l’aide de différents plug-ins prêts à l’emploi, qui leur permettent de se faire discrets sur le réseau d’entreprise. Les ransomwares modernes et sophistiqués sont capables de s’adapter eux-mêmes à leur environnement et d’opérer de façon autonome, en se fondant dans l’activité normale de l’entreprise, même lorsqu’ils sont déconnectés de leur serveur de commande et contrôle. Ces ransomwares « autosuffisants » posent un problème de taille aux outils traditionnels, qui détectent uniquement les menaces en fonction des connexions externes indésirables qu’elles ont établies.
La détection des connexions isolées face à une compréhension globale de l’entreprise
Les outils de sécurité traditionnels, tels que les systèmes de détection d’intrusions (IDS) et les pare-feu, ont tendance à examiner les connexions isolément au lieu de s’intéresser aux connexions précédentes, potentiellement pertinentes, ce qui rend la détection des canaux C2 très difficile.
Les IDS et les pare-feu sont capables de bloquer les domaines malveillants connus ou d’utiliser une forme de blocage par géolocalisation, mais un cybercriminel averti utiliserait probablement une nouvelle infrastructure dans un tel cas.
Certains aspects ne sont pas analysés par ces outils : la périodicité, la régularité ou l’irrégularité des connexions de communication, l’âge ou encore la rareté du domaine dans le contexte de l’environnement.
Darktrace adapte constamment sa compréhension de l’entreprise numérique. Ainsi, les connexions C2 suspectes et les téléchargements qui s’ensuivent sont détectés, même si le cybercriminel utilise des programmes et des méthodes habituelles pour l’entreprise. La technologie d’IA corrèle de multiples signaux subtils indicateurs de menaces en tenant compte notamment des connexions anormales vers des endpoints récents et/ou inhabituels, des téléchargements de fichiers anormaux, des connexions RDP entrantes, ainsi que des téléversements et téléchargements de données inhabituels.
Once they are detected as a threat, Autonomous Response halts these connections and downloads, while allowing normal business activity to continue.
Exemple de situation réelle : attaque WastedLocker
Lorsqu’une attaque menée à l’aide du ransomware WastedLocker a touché un institut agricole aux États-Unis, Darktrace a immédiatement détecté l’activité C2 SSL initiale inhabituelle (en rapprochant la rareté de la destination, le caractère inhabituel de la méthodologie JA3 et l’analyse de fréquence). Antigena (qui était alors déployée en mode passif, et ne pouvait donc pas mettre en place d’actions autonomes) a immédiatement suggéré de bloquer le trafic C2 sur le port 443 et le scan interne parallèle sur le port 135.

Lorsque des activités de balisage ont ensuite été observées vers bywce.payment.refinedwebs[.]com, cette fois via HTTP vers /updateSoftwareVersion, Antigena a relevé son niveau de réponse en bloquant les autres canaux C2.

4. Lateral movement
Une fois qu'un attaquant a pris pied au sein d'une organisation, il commence à approfondir sa connaissance de l'ensemble du patrimoine numérique et de sa présence dans celui-ci. C'est ainsi qu'il trouvera et accédera aux fichiers qu'il tentera finalement d'exfiltrer et de chiffrer. Il commence par la reconnaissance : scanner le réseau, dresser une image des dispositifs qui le composent, identifier l'emplacement des actifs les plus précieux.
Then the attacker begins moving laterally. They infect more devices and look to escalate their privileges – for instance, by obtaining admin credentials – thereby increasing their control over the environment. Once they have obtained authority and presence within the digital estate, they can progress to the final stages of the attack.
Modern ransomware has built-in functions that allow it to search automatically for stored passwords and spread through the network. More sophisticated strains are designed to build themselves differently in different environments, so the signature is constantly changing and it’s harder to detect.
Legacy tools: A blunt response to known threats
Because they rely upon static rules and signatures, legacy solutions struggle to prevent lateral movement and privilege escalation without also impeding essential business operations. Whilst in theory, an organization leveraging firewalls and NAC internally with proper network segmentation and a perfect configuration could prevent cross-network lateral movement, maintaining a perfect balance between protective and disruptive controls is near impossible.
Some organizations rely on Intrusion Prevent Systems (IPS) to deny network traffic when known threats are detected in packets, but as with previous stages, novel malware will evade detection, and this requires the database to be constantly updated. These solutions also sit at the ingress/egress points, limiting their network visibility. An Intrusion Detection System (IDS) may sit out-of-line, but doesn’t have response capabilities.
Une approche auto-apprenante
Darktrace’s AI learns ‘self’ for the organization, enabling it to detect suspicious activity indicative of lateral movement, regardless of whether the attacker uses new infrastructure or ‘lives off the land’. Potential unusual activity that Darktrace detects includes unusual scanning activity, unusual SMB, RDP, and SSH activity. Other models that fire at this stage include:
- Suspicious Activity on High-Risk Device
- Numeric EXE in SMB Write
- New or Uncommon Service Control
Autonomous Response then takes targeted action to stop the threat at this stage, blocking anomalous connections, enforcing the infected device’s ‘pattern of life’, or enforcing the group ‘pattern of life’ – automatically clustering devices into peer groups and preventing a device from doing anything its peer group hasn’t done.
Where malicious behavior persists, and only if necessary, Darktrace will quarantine an infected device.
Real-world example: Unusual chain of RDP connections
At an organization in Singapore, one compromised server led to the creation of a botnet, which began moving laterally, predominantly by establishing chains of unusual RDP connections. The server then started making external SMB and RPC connections to rare endpoints on the Internet, in an attempt to find further vulnerable hosts.
Other lateral movement activities detected by Darktrace included the repeated failing attempts to access multiple internal devices over the SMB file-sharing protocol with a range of different usernames, implying brute-force network access attempts.

5. Data exfiltration
In the past, ransomware was simply about encrypting an operating system and network files.
In a modern attack, as organizations insure against malicious encryption by becoming increasingly diligent with data backups, threat actors have moved towards ‘double extortion’, where they exfiltrate key data and destroy backups before the encryption takes place. Exfiltrated data is used to blackmail organizations, with attackers threatening to publish sensitive information online or sell it on to the organization’s competitors if they are not paid.
Modern ransomware variants also look for cloud file storage repositories such as Box, Dropbox, and others.
Many of these incidents aren’t public, because if IP is stolen, organizations are not always legally required to disclose it. However, in the case of customer data, organizations are obligated by law to disclose the incident and face the additional burden of compliance files – and we’ve seen these mount in recent years (Marriot, $23.8 million; British Airways, $26 million; Equifax, $575 million). There’s also the reputational blow associated with having to inform customers that a data breach has occurred.
Legacy tools: The same old story
For those that have been following, the narrative by now will sound familiar: to stop a ransomware attack at this stage, most defenses rely on either pre-programmed definitions of 'bad' or have rules constructed to combat different scenarios put organizations in a risky, never-ending game of cat and mouse.
A firewall and proxy might block connections based on pre-programmed policies based on specific endpoints or data volumes, but it’s likely an attacker will ‘live off the land’ and utilize a service that is generally allowed by the business.
The effectiveness of these tools will vary according to data volumes: they might be effective for ‘smash and grab’ attacks using known malware, and without employing any defense evasion techniques, but are unlikely to spot ‘low and slow’ exfiltration and novel or sophisticated strains.
On the other hand, because by nature it involves a break from expected behavior, even less conspicuous, low and slow data exfiltration is detected by Darktrace and stopped with Autonomous Response. No confidential files are lost, and attackers are unable to extort a ransom payment through blackmail.
Real-world example: Unusual chain of RDP connections
It becomes more difficult to find examples of Antigena stopping ransomware at these later stages, as the threat is usually contained before it gets this far. This is the double-edged sword of effective security – early containment makes for bad storytelling! However, we can see the effects of a double extortion ransomware attack on an energy company in Canada. The organization had the Enterprise Immune System but no Antigena, and without anyone actively monitoring Darktrace’s AI detections, the attack was allowed to unfold.
The attacker managed to connect to an internal file server and download 1.95TB of data. The device was also seen downloading Rclone software – an open-source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000. As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.
Read the full details of the attack
It should be noted that the exact order of the stages 3–5 above is not set in stone, and varies according to attack. Sometimes data is exfiltrated and then there is further lateral movement, and additional C2 beaconing. This entire period is known as the ‘dwell time’. Sometimes it takes place over only a few days, other times attackers may persist for months, slowly gathering more intel and exfiltrating data in a ‘low and slow’ fashion so as to avoid detection from rule-based tools that are configured to flag any single data transfer over a certain threshold. Only through a holistic understanding of malicious activity over time can a technology spot this level of activity and allow the security team to remove the threat before it reaches the latter and most damaging stages of ransomware.
6. Data encryption
Using either symmetric encryption, asymmetric encryption, or a combination of the two, attackers attempt to render as much data unusable in the organization’s network as they can before the attack is detected.
As the attackers alone have access to the relevant decryption keys, they are now in total control of what happens to the organization’s data.
Pre-programmed response and disruption
There are many families of tools that claim to stop encryption in this manner, but each contain blind spots which enable a sophisticated attacker to evade detection at this crucial stage. Where they do take action, it is often highly disruptive, causing major shutdowns and preventing a business from continuing its usual operations.
Internal firewalls prevent clients from accessing servers, so once an attacker has penetrated to servers using any of the techniques outlined above, they have complete freedom to act as they want.
Similarly, antivirus tools look only for known malware. If the malware has not been detected until this point, it is highly unlikely the antivirus will act here.
Stopping encryption autonomously
Even if familiar tools and methods are used to conduct it, Autonomous Response can enforce the normal ‘pattern of life’ for devices attempting encryption, without using static rules or signatures. This action can be taken independently or via integrations with native security controls, maximizing the return on other security investments. With a targeted Autonomous Response, normal business operations can continue while encryption is prevented.
7. Ransom note
It is important to note that in the stages before encryption, this ransomware attack is not yet “ransomware”. Only at this stage does it gets its name.
A ransom note is deployed. The attackers request payment in return for a decryption key and threaten the release of sensitive exfiltrated data. The organization must decide whether to pay the ransom or lose their data, possibly to their competition or the public. The average demand made by ransomware threat actors rose in 2021 to $5.3 million, with meat processing company JBS paying out $11 million and DarkSide receiving over $90 million in Bitcoin payments following the Colonial Pipeline incident.
All of the stages up until this point represent a typical, traditional ransomware attack. But ransomware is shifting from indiscriminate encryption of devices to attackers targeting business disruption in general, using multiple techniques to hold their victims to ransom. Additional methods of extortion include not only data exfiltration, but corporate domain hijack, deletion or encryption of backups, attacks against systems close to industrial control systems, targeting company VIPs… the list goes on.
Sometimes, attackers will just skip straight from stage 2 to 6 and jump straight to extortion. Darktrace recently stopped an email attack which showed an attacker bypassing the hard work and attempting to jump straight to extortion in an email. The attacker claimed to have compromised the organization’s sensitive data, requesting payment in bitcoin for its same return. Whether or not the claims were true, this attack shows that encryption is not always necessary for extortion, and this type of harassment exists in multiple forms.

As with the email example we explored in the first post of this series, Antigena Email was able to step in and stop this email where other email tools would have let it through, stopping this potentially costly extortion attempt.
Whether through encryption or some other kind of blackmail, the message is the same every time. Pay up, or else. At this stage, it’s too late to start thinking about any of the options described above that were available to the organization, that would have stopped the attack in its earliest stages. There is only one dilemma. “To pay or not to pay” – that is the question.
Often, people believe their payment troubles are over after the ransom payment stage, but unfortunately, it’s just beginning to scratch the surface…
8. Clean-up
Efforts are made to try to secure the vulnerabilities which allowed the attack to happen initially – the organization should be conscious that approximately 80% of ransomware victims will in fact be targeted again in the future.
Legacy tools largely fail to shed light on the vulnerabilities which allowed the initial breach. Like searching for a needle in an incomplete haystack, security teams will struggle to find useful information within the limited logs offered by firewalls and IDSs. Antivirus solutions may reveal some known malware but fail to spot novel attack vectors.
With Darktrace’s Cyber AI Analyst, organizations are given full visibility over every stage of the attack, across all coverage areas of their digital estate, taking the mystery out of ransomware attacks. They are also able to see the actions that would have been taken to halt the attack by Autonomous Response.
9. Recovery
The organization begins attempts to return its digital environment to order. Even if it has paid for a decryption key, many files may remain encrypted or corrupted. Beyond the costs of the ransom payment, network shutdowns, business disruption, remediation efforts, and PR setbacks all incur hefty financial losses.
The victim organization may also suffer additional reputation costs, with 66% of victims reporting a significant loss of revenue following a ransomware attack, and 32% reporting losing C-level talent as a direct result from ransomware.
Conclusion
While the high-level stages described above are common in most ransomware attacks, the minute you start looking at the details, you realize every ransomware attack is different.
As many targeted ransomware attacks come through ransomware affiliates, the Tools, Techniques and Procedures (TTPs) displayed during intrusions vary widely, even when the same ransomware malware is used. This means that even comparing two different ransomware attacks using the same ransomware family, you are likely to encounter completely different TTPs. This makes it impossible to predict what tomorrow’s ransomware will look like.
C'est le clou du cercueil pour les outils traditionnels qui sont basés sur des données d'attaques historiques. Les exemples ci-dessus démontrent que la technologie d'auto-apprentissage et la réponse autonome sont les seules solutions permettant d'arrêter les ransomwares à chaque étape, par le biais de la messagerie et du réseau.
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Cloud
Darktrace Integrates Self-Learning AI with Amazon Security Lake to Support Security Investigations
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Darktrace has deepened its relationship with AWS by integrating its detection and response capabilities with Amazon Security Lake.
This development will allow mutual customers to seamlessly combine Darktrace AI’s bespoke understanding of their organization with the Threat Intelligence offered by other security tools, and investigate all of their alerts in one central location.
This integration will improve the value security teams get from both products, streamlining analyst workflows and improving their ability to detect and respond to the full spectrum of known and unknown cyber-threats.
How Darktrace and Amazon Security Lake augment security teams
Amazon Security Lake is a newly-released service that automatically centralizes an organization’s security data from cloud, on-premises, and custom sources into a customer owned purpose-built data lake. Both Darktrace and Amazon Security Lake support the Open Cybersecurity Schema Framework (OCSF), an open standard to simplify, combine, and analyze security logs.
Customers can store security logs, events, alerts, and other relevant data generated by various AWS services and security tools. By consolidating security data in a central lake, organizations can gain a holistic view of their security posture, perform advanced analytics, detect anomalies and open investigations to improve their security practices.
With Darktrace DETECT and RESPOND AI engines covering all assets across IT, OT, network, endpoint, IoT, email and cloud, organizations can augment the value of their security data lakes by feeding Darktrace’s rich and context-aware datapoints to Amazon Security Lake.
Amazon Security Lake empowers security teams to improve the protection of your digital estate:
- Quick and painless data normalization
- Fast-tracks ability to investigate, triage and respond to security events
- Broader visibility aids more effective decision-making
- Surfaces and prioritizes anomalies for further investigation
- Single interface for seamless data management
How will Darktrace customers benefit?
Across the Cyber AI Loop, all Darktrace solutions have been architected with AWS best practices in mind. With this integration, Darktrace is bringing together its understanding of ‘self’ for every organization with the centralized data visibility of the Amazon Security Lake. Darktrace’s unique approach to cyber security, powered by groundbreaking AI research, delivers a superior dataset based on a deep and interconnected understanding of the enterprise.
Where other cyber security solutions are trained to identify threats based on historical attack data and techniques, Darktrace DETECT gains a bespoke understanding of every digital environment, continuously analyzing users, assets, devices and the complex relationships between them. Our AI analyzes thousands of metrics to reveal subtle deviations that may signal an evolving issue – even unknown techniques and novel malware. It distinguishes between malicious and benign behavior, identifying harmful activity that typically goes unnoticed. This rich dataset is fed into RESPOND, which takes precise action to neutralize threats against any and every asset, no matter where data resides.
Both DETECT and RESPOND are supported by Darktrace Self-Learning AI, which provides full, real-time visibility into an organization’s systems and data. This always-on threat analysis already makes humans better at cyber security, improving decisions and outcomes based on total visibility of the digital ecosystem, supporting human performance with AI coverage and empowering security teams to proactively protect critical assets.
Converting Darktrace alerts to the Amazon Security Lake Open Cybersecurity Schema Framework (OCSF) supplies the Security Operations Center (SOC) and incident response team with contextualized data, empowering them to accelerate their investigation, triage and response to potential cyber threats.
Darktrace is available for purchase on the AWS Marketplace.
Learn more about how Darktrace provides full-coverage, AI-powered cloud security for AWS, or see how our customers use Darktrace in their AWS cloud environments.

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A l'intérieur du SOC
Tracking the Hive: Darktrace’s Detection of a Hive Ransomware-as-Service


The threat of ransomware continues to be a constant concern for security teams across the cyber threat landscape. With the growing popularity of Ransomware-as-a-Service (RaaS), it is becoming more and more accessible for even inexperienced of would-be attackers. As a result of this low barrier to entry, the volume of ransomware attacks is expected to increase significantly.
What’s more, RaaS is a highly tailorable market in which buyers can choose from varied kits and features to use in their ransomware deployments meaning attacks will rarely behave the same. To effectively detect and safeguard against these differentiations, it is crucial to implement security measures that put the emphasis on detecting anomalies and focusing on deviations in expected behavior, rather than relying on depreciated indicators of compromise (IoC) lists or playbooks that focus on attack chains unable to keep pace with the increasing speed of ransomware evolution.
In early 2022, Darktrace DETECT/Network™ identified several instances of Hive ransomware on the networks of multiple customers. Using its anomaly-based detection, Darktrace was able to successfully detect the attacks and multiple stages of the kill chain, including command and control (C2) activity, lateral movement, data exfiltration, and ultimately data encryption and the writing of ransom notes.
Hive Ransomware
Hive ransomware is a relatively new strain that was first observed in the wild in June 2021. It is known to target a variety of industries including healthcare, energy providers, and retailers, and has reportedly attacked over 1,500 organizations, collecting more than USD 100m in ransom payments [1].
Hive is distributed via a RaaS model where its developers update and maintain the code, in return for a percentage of the eventual ransom payment, while users (or affiliates) are given the tools to carry out attacks using a highly sophisticated and complex malware they would otherwise be unable to use. Hive uses typical tactics, techniques and procedures (TTPs) associated with ransomware, though they do vary depending on the Hive affiliate carrying out the attack.
In most cases a double extortion attack is carried out, whereby data is first exfiltrated and then encrypted before a ransom demand is made. This gives attackers extra leverage as victims are at risk of having their sensitive data leaked to the public on websites such as the ‘HiveLeaks’ TOR website.
Attack Timeline
Owing to the highly customizable nature of RaaS, the tactics and methods employed by Hive actors are expected to differ on a case-by-case basis. Nonetheless in the majority of Hive ransomware incidents identified on Darktrace customer environments, Darktrace DETECT observed the following general attack stages and features. This is possibly indicative of the attacks originating from the same threat actor(s) or from a widely sold batch with a particular configuration to a variety of actors.

Initial Access
Although Hive actors are known to gain initial access to networks through multiple different vectors, the two primary methods reported by security researchers are the exploitation of Microsoft Exchange vulnerabilities, or the distribution of phishing emails with malicious attachments [2][3].
In the early stages of one Hive ransomware attack observed on the network of a Darktrace customer, for example, Darktrace detected a device connecting to the rare external location 23.81.246[.]84, with a PowerShell user agent via HTTP. During this connection, the device attempted to download an executable file named “file.exe”. It is possible that the file was initially accessed and delivered via a phishing email; however, as Darktrace/Email was not enabled at the time of the attack, this was outside of Darktrace’s purview. Fortunately, the connection failed the proxy authentication was thus blocked as seen in the packet capture (PCAP) in Figure 2.
Shortly after this attempted download, the same device started to receive a high volume of incoming SSL connections from a rare external endpoint, namely 146.70.87[.]132. Darktrace logged that this endpoint was using an SSL certificate signed by Go Daddy CA, an easily obtainable and accessible SSL certificate, and that the increase in incoming SSL connections from this endpoint was unusual behavior for this device.
It is likely that this highly anomalous activity detected by Darktrace indicates when the ransomware attack began, likely initial payload download.
Darktrace DETECT models:
- Anomalous Connection / Powershell to Rare External
- Anomalous Server Activity / New Internet Facing System

C2 Beaconing
Following the successful initial access, Hive actors begin to establish their C2 infrastructure on infected networks through numerous connections to C2 servers, and the download of additional stagers.
On customer networks infected by Hive ransomware, Darktrace identified devices initiating a high volume of connections to multiple rare endpoints. This very likely represented C2 beaconing to the attacker’s infrastructure. In one particular example, further open-source intelligence (OSINT) investigation revealed that these endpoints were associated with Cobalt Strike.
Darktrace DETECT models:
- Anomalous Connection / Multiple Connections to New External TCP
- Anomalous Server Activity / Anomalous External Activity from Critical Network Device
- Compromise / High Volume of Connections with Beacon Score
- Compromise / Sustained SSL or HTTP Increase
- Compromise / Suspicious HTTP Beacons to Dotted Quad
- Compromise / SSL or HTTP Beacon
- Device / Lateral Movement and C2 Activity
Internal Reconnaissance, Lateral Movement and Privilege Escalation
After C2 infrastructure has been established, Hive actors typically begin to uninstall antivirus products in an attempt to remain undetected on the network [3]. They also perform internal reconnaissance to look for vulnerabilities and open channels and attempt to move laterally throughout the network.
Amid the C2 connections, Darktrace was able to detect network scanning activity associated with the attack when a device on one customer network was observed initiating an unusually high volume of connections to other internal devices. A critical network device was also seen writing an executable file “mimikatz.exe” via SMB which appears to be the Mimikatz attack tool commonly used for credential harvesting.
There were also several detections of lateral movement attempts via RDP and DCE-RPC where the attackers successfully authenticated using an “Administrator” credential. In one instance, a device was also observed performing ITaskScheduler activity. This service is used to remotely control tasks running on machines and is commonly observed as part of malicious lateral movement activity. Darktrace DETECT understood that the above activity represented a deviation from the devices’ normal pattern of behavior and the following models were breached:
Darktrace DETECT models:
- Anomalous Connection / Anomalous DRSGetNCChanges Operation
- Anomalous Connection / New or Uncommon Service Control
- Anomalous Connection / Unusual Admin RDP Session
- Anomalous Connection / Unusual SMB Version 1 Connectivity
- Compliance / SMB Drive Write
- Device / Anomalous ITaskScheduler Activity
- Device / Attack and Recon Tools
- Device / Attack and Recon Tools In SMB
- Device / EXE Files Distributed to Multiple Devices
- Device / Suspicious Network Scan Activity
- Device / Increase in New RPC Services
- User / New Admin Credentials on Server
Exfiltration de données
At this stage of the attack, Hive actors have been known to carry out data exfiltration activity on infected networks using a variety of different methods. The Cybersecurity & Infrastructure Security Agency (CISA) reported that “Hive actors exfiltrate data likely using a combination of Rclone and the cloud storage service Mega[.]nz” [4]. Darktrace DETECT identified an example of this when a device on one customer network was observed making HTTP connections to endpoints related to Mega, including “w.apa.mega.co[.]nz”, with the user agent “rclone/v1.57.0” with at least 3 GiB of data being transferred externally (Figure 3). The same device was also observed transferring at least 3.6 GiB of data via SSL to the rare external IP, 158.51.85[.]157.

In another case, a device was observed uploading over 16 GiB of data to a rare external endpoint 93.115.27[.]71 over SSH. The endpoint in question was seen in earlier beaconing activity suggesting that this was likely an exfiltration event.
However, Hive ransomware, like any other RaaS kit, can differ greatly in its techniques and features, and it is important to note that data exfiltration may not always be present in a Hive ransomware attack. In one incident detected by Darktrace, there were no signs of any data leaving the customer environment, indicating data exfiltration was not part of the Hive actor’s objectives.
Darktrace DETECT models:
- Anomalous Connection / Data Sent to Rare Domain
- Anomalous Connection / Lots of New Connections
- Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
- Anomalous Connection / Suspicious Self-Signed SSL
- Anomalous Connection / Uncommon 1 GiB Outbound
- Device / New User Agent and New IP
- Unusual Activity / Unusual External Data to New Endpoints
- Unusual Activity / Unusual External Data Transfer
- Unusual Activity / Enhanced Unusual External Data Transfer
Ransomware Deployment
In the final stage of a typical Hive ransomware attack, the ransomware payload is deployed and begins to encrypt files on infected devices. On one customer network, Darktrace detected several devices connecting to domain controllers (DC) to read a file named “xxx.exe”. Several sources have linked this file name with the Hive ransomware payload [5].
In another example, Darktrace DETECT observed multiple devices downloading the executable files “nua64.exe” and “nua64.dll” from a rare external location, 194.156.90[.]25. OSINT investigation revealed that the files are associated with Hive ransomware.

Shortly after the download of this executable, multiple devices were observed performing an unusual amount of file encryption, appending randomly generated strings of characters to file extensions.
Although it has been reported that earlier versions of Hive ransomware encrypted files with a “.hive” extension [7], Darktrace observed across multiple customers that encrypted files had extensions that were partially-randomized, but consistently 20 characters long, matching the regular expression “[a-zA-Z0-9\-\_]{8}[\-\_]{1}[A-Za-z0-9\-\_]{11}”.

Following the successful encryption of files, Hive proceeds to drop a ransom note, named “HOW_TO_DECRYPT.txt”, into each affected directory. Typically, the ransom note will contain a link to Hive’s “sales department” and, in the event that exfiltration took place, a link to the “HiveLeaks” site, where attackers threaten to publish exfiltrated data if their demands are not met (Figure 6). In cases of Hive ransomware detected by Darktrace, multiple devices were observed attempting to contact “HiveLeaks” TOR domains, suggesting that endpoint users had followed links provided to them in ransom notes.

Examples of file extensions:
- 36C-AT9-_wm82GvBoCPC
- 36C-AT9--y6Z1G-RFHDT
- 36C-AT9-_x2x7FctFJ_q
- 36C-AT9-_zK16HRC3QiL
- 8KAIgoDP-wkQ5gnYGhrd
- kPemi_iF_11GRoa9vb29
- kPemi_iF_0RERIS1m7x8
- kPemi_iF_7u7e5zp6enp
- kPemi_iF_y4u7pB3d3f3
- U-9Xb0-k__T0U9NJPz-_
- U-9Xb0-k_6SkA8Njo5pa
- zm4RoSR1_5HMd_r4a5a9
Darktrace DETECT models:
- Anomalous Connection / SMB Enumeration
- Anomalous Connection / Sustained MIME Type Conversion
- Anomalous Connection / Unusual Admin SMB Session
- Anomalous File / Internal / Additional Extension Appended to SMB File
- Compliance / SMB Drive Write
- Compromise / Ransomware / Suspicious SMB Activity
- Compromise / Ransomware / Ransom or Offensive Words Written to SMB
- Compromise / Ransomware / Possible Ransom Note Write
- Compromise / High Priority Tor2Web
- Compromise / Tor2Web
- Device / EXE Files Distributed to Multiple Devices
Conclusion
As Hive ransomware attacks are carried out by different affiliates using varying deployment kits, the tactics employed tend to vary and new IoCs are regularly identified. Furthermore, in 2022 a new variant of Hive was written using the Rust programming language. This represented a major upgrade to Hive, improving its defense evasion techniques and making it even harder to detect [8].
Hive is just one of many RaaS offerings currently on the market, and this market is only expected to grow in usage and diversity of presentations. As ransomware becomes more accessible and easier to deploy it is essential for organizations to adopt efficient security measures to identify ransomware at the earliest possible stage.
Darktrace DETECT’s Self-Learning AI understands customer networks and learns the expected patterns of behavior across an organization’s digital estate. Using its anomaly-based detection Darktrace is able to identify emerging threats through the detection of unusual or unexpected behavior, without relying on rules and signatures, or known IoCs.
Credit to: Emily Megan Lim, Cyber Analyst, Hyeongyung Yeom, Senior Cyber Analyst & Analyst Team Lead.
Appendices
MITRE AT&CK Mapping
Reconnaissance
T1595.001 – Scanning IP Blocks
T1595.002 – Vulnerability Scanning
Resource Development
T1583.006 – Web Services
Initial Access
T1078 – Valid Accounts
T1190 – Exploit Public-Facing Application
T1200 – Hardware Additions
Execution
T1053.005 – Scheduled Task
T1059.001 – PowerShell
Persistence/Privilege Escalation
T1053.005 – Scheduled Task
T1078 – Valid Accounts
Defense Evasion
T1078 – Valid Accounts
T1207 – Rogue Domain Controller
T1550.002 – Pass the Hash
Discovery
T1018 – Remote System Discovery
T1046 – Network Service Discovery
T1083 – File and Directory Discovery
T1135 – Network Share Discovery
Lateral Movement
T1021.001 – Remote Desktop Protocol
T1021.002 – SMB/Windows Admin Shares
T1021.003 – Distributed Component Object Model
T1080 – Taint Shared Content
T1210 – Exploitation of Remote Services
T1550.002 – Pass the Hash
T1570 – Lateral Tool Transfer
Collection
T1185 – Man in the Browser
Command and Control
T1001 – Data Obfuscation
T1071 – Application Layer Protocol
T1071.001 – Web Protocols
T1090.003 – Multi-hop proxy
T1095 – Non-Application Layer Protocol
T1102.003 – One-Way Communication
T1571 – Non-Standard Port
Exfiltration
T1041 – Exfiltration Over C2 Channel
T1567.002 – Exfiltration to Cloud Storage
Impact
T1486 – Data Encrypted for Impact
T1489 – Service Stop
List of IoCs
23.81.246[.]84 - IP Address - Likely Malicious File Download Endpoint
146.70.87[.]132 - IP Address - Possible Ransomware Endpoint
5.199.162[.]220 - IP Address - C2 Endpoint
23.227.178[.]65 - IP Address - C2 Endpoint
46.166.161[.]68 - IP Address - C2 Endpoint
46.166.161[.]93 - IP Address - C2 Endpoint
93.115.25[.]139 - IP Address - C2 Endpoint
185.150.1117[.]189 - IP Address - C2 Endpoint
192.53.123[.]202 - IP Address - C2 Endpoint
209.133.223[.]164 - IP Address - Likely C2 Endpoint
cltrixworkspace1[.]com - Domain - C2 Endpoint
vpnupdaters[.]com - Domain - C2 Endpoint
93.115.27[.]71 - IP Address - Possible Exfiltration Endpoint
158.51.85[.]157 - IP Address - Possible Exfiltration Endpoint
w.api.mega.co[.]nz - Domain - Possible Exfiltration Endpoint
*.userstorage.mega.co[.]nz - Domain - Possible Exfiltration Endpoint
741cc67d2e75b6048e96db9d9e2e78bb9a327e87 - SHA1 Hash - Hive Ransomware File
2f9da37641b204ef2645661df9f075005e2295a5 - SHA1 Hash - Likely Hive Ransomware File
hiveleakdbtnp76ulyhi52eag6c6tyc3xw7ez7iqy6wc34gd2nekazyd[.]onion - TOR Domain - Likely Hive Endpoint
References
[1] https://www.justice.gov/opa/pr/us-department-justice-disrupts-hive-ransomware-variant
[2] https://www.varonis.com/blog/hive-ransomware-analysis
[3] https://www.trendmicro.com/vinfo/us/security/news/ransomware-spotlight/ransomware-spotlight-hive
[4]https://www.cisa.gov/news-events/cybersecurity-advisories/aa22-321a
[5] https://www.trendmicro.com/en_us/research/22/c/nokoyawa-ransomware-possibly-related-to-hive-.html
[6] https://www.virustotal.com/gui/file/60f6a63e366e6729e97949622abd9de6d7988bba66f85a4ac8a52f99d3cb4764/detection
[7] https://heimdalsecurity.com/blog/what-is-hive-ransomware/
[8] https://www.microsoft.com/en-us/security/blog/2022/07/05/hive-ransomware-gets-upgrades-in-rust/