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Auris APT Group

From EverybodyWiki Bios & Wiki

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Auris is a sophisticated Advanced Persistent Threat (APT) group that has recently emerged in the cybersecurity landscape. APTs are highly sophisticated and potent security threats that are organized, knowledgeable, and motivated to achieve their objectives against targeted organizations over a prolonged period.[1]. Auris stands out from other cybercriminal organizations due to its unique approach to extortion. Upon receiving the ransom payment, Auris offers proof of data deletion[2].

The term "advanced persistent threat" refers to a targeted and very sophisticated cyber attack[3]. APTs are characterized by their ability to establish a persistent foothold in the target system and remain undetected for a long period of time[4]. They often leverage zero-day vulnerabilities or infer a user's cryptographic key to completely compromise a system without detection[5]. Auris specifically focuses on targeting e-commerce platforms and extracting valuable personal information from unsuspecting victims [6].

One of the distinguishing factors of Auris is its approach to extortion. After receiving the ransom payment, Auris provides proof of data deletion. This tactic sets them apart from other cybercriminal organizations that may not offer such proof or may not honor their promises after receiving the payment[7].

To detect and mitigate APTs, various approaches have been proposed, including the use of machine learning techniques. Machine learning can improve the early detection of APT attacks by analyzing patterns and anomalies in the attack life cycle[8]. Additionally, intelligent systems and algorithms, such as decision trees, random forests, support vector machines, and logistic regression, have been applied to accurately detect and classify complex APT signatures[9].

The impact of APTs extends beyond traditional IT systems. Industrial Control Systems (ICS) are also vulnerable to APT attacks, which can have destructive cyber-physical effects. Defenders of ICS must consider the cost-efficiency of implementing defensive measures to create an optimal defense against APTs[10].

In summary, Auris is a sophisticated APT group that targets e-commerce platforms and extracts valuable personal information. What sets Auris apart is its unique approach to extortion, offering proof of data deletion upon receiving the ransom payment. APTs, in general, are highly sophisticated and persistent threats that can compromise systems without detection. Various approaches, including machine learning techniques, have been proposed to detect and mitigate APT attacks. The impact of APTs extends to industrial control systems, where defenders must consider cost-effective defensive measures.

Background

Auris primarily targets e-commerce platforms due to the vast amount of personal data available on such platforms, which presents a lucrative opportunity for the group to conduct various forms of identity theft, fraud, and further criminal activities[11]. E-commerce platforms typically store extensive personal information about their users, including names, addresses, contact details, and financial data [12]. This trove of data makes e-commerce platforms attractive targets for cybercriminals like Auris.

The motivations behind targeting e-commerce platforms can vary. One possible motivation is financial gain. By stealing personal information from e-commerce platforms, Auris can engage in identity theft and fraud, which can result in monetary benefits for the group[13]. Another motivation could be the acquisition of valuable data for other purposes, such as selling the data on the dark web or using it for targeted phishing attacks[14]. Additionally, Auris may target e-commerce platforms as a means of disrupting or undermining the operations of these platforms, potentially for ideological or political reasons[15].

The specific techniques and tactics used by Auris to target e-commerce platforms may vary. However, one common technique used by cybercriminals is Distributed Denial of Service (DDoS) attacks[16]. These attacks involve overwhelming a target website or server with a flood of traffic, rendering it inaccessible to legitimate users. DDoS attacks can disrupt the operations of e-commerce platforms, causing financial losses and damaging their reputation[17].

It is important to note that the motivations and techniques of cybercriminal groups like Auris are constantly evolving. As new technologies and vulnerabilities emerge, cybercriminals adapt their tactics to exploit these changes. Therefore, it is crucial for e-commerce platforms to continuously update their security measures and stay vigilant against emerging threats.

In conclusion, Auris primarily targets e-commerce platforms due to the vast amount of personal data available on these platforms. The motivations behind targeting e-commerce platforms can include financial gain, acquisition of valuable data, and disruption of operations. Techniques such as DDoS attacks may be employed by cybercriminals to target e-commerce platforms. To mitigate the risks posed by groups like Auris, e-commerce platforms must prioritize robust cybersecurity measures and stay updated on emerging threats.

Initial Compromise

Auris employs a range of sophisticated techniques to gain initial access to targeted e-commerce platforms. One common technique used by Auris is spear-phishing emails containing malicious attachments or links to exploit vulnerabilities in the target's systems[18]. These emails are designed to appear legitimate and trick the recipient into interacting with the malicious elements. Once an unsuspecting victim interacts with these elements, the attackers gain a foothold within the target's network.

Another technique used by Auris is the exploitation of vulnerabilities in the target's systems. This can involve identifying and exploiting software vulnerabilities or misconfigurations in the target's network infrastructure. By exploiting these vulnerabilities, Auris can gain unauthorized access to the target's network and establish a foothold[19].

In addition to these techniques, Auris may also employ social engineering tactics to gain initial access. Social engineering involves manipulating individuals into divulging sensitive information or performing actions that compromise the security of the target's systems. This can include techniques such as impersonating trusted individuals or organizations, conducting pretexting or baiting attacks, or exploiting human psychology to manipulate individuals into taking actions that benefit the attacker[20].

Once Auris has gained initial access to the target's network, they can then proceed to carry out further attacks and exfiltrate sensitive data. It is important for organizations to implement robust security measures to protect against these initial compromise techniques. This can include training employees to recognize and report phishing attempts, regularly patching and updating software to address vulnerabilities, and implementing strong access controls and network segmentation to limit the impact of a successful initial compromise[21].

Data Extraction

Data extraction is a crucial step in the process of infiltrating an e-commerce platform. Auris, the infiltrator, utilizes advanced techniques and custom-built malware to gain access to user databases and other relevant repositories [22]. Once inside, Auris employs various tactics such as privilege escalation, lateral movement, and persistence to extract personal information and sensitive data[23].

One example of a tactic used by Auris is privilege escalation. This technique involves gaining higher levels of access and privileges within the system, allowing the infiltrator to bypass security measures and access restricted data[24]. By escalating privileges, Auris can navigate through the system and extract personal information from user databases.

Lateral movement is another tactic employed by Auris. This technique involves moving laterally within the system, from one compromised host to another, in order to gain access to different repositories and databases[25]. By moving laterally, Auris can locate and extract personal information from various sources, increasing the amount of data obtained.

Persistence is a key aspect of Auris' data extraction process. This tactic involves establishing a long-term presence within the system, ensuring continued access to sensitive data[26]. By maintaining persistence, Auris can continuously extract personal information over an extended period of time, maximizing the amount of data obtained.

Proof of Data Deletion

Auris, a cybercriminal group, stands out from other groups due to their unique approach to extortion. After successfully stealing personal data, Auris contacts the victim organization and demands a ransom in exchange for not selling or further exploiting the stolen information. What sets Auris apart is their offer of providing proof of data deletion upon receiving the ransom payment[27].

The concept of providing proof of data deletion is not commonly seen in cybercriminal activities. Typically, cybercriminals steal data and use it for various malicious purposes, such as selling it on the dark web or using it for identity theft. However, Auris takes a different approach by assuring the victim organization that the stolen data will be deleted upon payment of the ransom[28].

This unique approach by Auris may be a strategy to build trust with the victim organization. By offering proof of data deletion, they provide a sense of security to the organization, knowing that their sensitive information will not be further exploited. This may increase the likelihood of the victim organization paying the ransom, as they have some assurance that their data will not be used against them[29].

The proof of data deletion can take various forms. It could involve providing evidence that the stolen data has been securely deleted from the cybercriminals' systems or demonstrating that the data has been irreversibly encrypted and can only be decrypted by the victim organization upon payment of the ransom. The specific method used by Auris to provide proof of data deletion is not mentioned in the references provided[30][31].

It is important to note that dealing with cybercriminals, including Auris, is illegal and unethical. Organizations should prioritize implementing robust cybersecurity measures to prevent data breaches and protect sensitive information. This includes regularly updating security systems, training employees on cybersecurity best practices, and conducting vulnerability assessments and penetration testing to identify and address potential weaknesses in the organization's network[32].

In conclusion, Auris distinguishes itself from other cybercriminal groups by offering proof of data deletion upon receiving a ransom payment. This unique approach may be a strategy to build trust with victim organizations and increase the likelihood of ransom payment. However, it is crucial for organizations to prioritize cybersecurity measures to prevent data breaches and protect sensitive information. Dealing with cybercriminals is illegal and unethical, and organizations should focus on proactive security measures rather than relying on the promises of cybercriminals.

Cryptographic Protocols

To provide proof of data deletion, Auris has implemented a set of cryptographic protocols that ensure the irreversible deletion of the stolen data. These protocols employ advanced encryption algorithms and hashing techniques to generate unique keys and ensure the secure erasure of the compromised information.

One example of a cryptographic protocol that can be used for data deletion is the use of MD5 hashing and advanced encryption standard (AES) algorithms. Purple Robot, for instance, anonymizes personally identifiable and sensitive information before storage and transmission using these algorithms [33]. This ensures that the data is securely encrypted and cannot be easily accessed or recovered.

In cloud computing environments, where the management of data and services may not be fully trustworthy, ensuring the integrity of data storage is crucial. Propose a solution that allows a third-party auditor (TPA) to verify the integrity of dynamic data stored in the cloud [34]. This approach eliminates the involvement of the client and enables the auditing of data integrity, including deletion operations.

In the context of secure storage and deletion verification for microgrid data, propose a scheme that combines blockchain and edge computing [35]. This scheme preprocesses power data using edge computing, encrypts the data using a hybrid encryption method, and stores the deletion proof in a secure storage chain built on the blockchain. This ensures the safe storage, efficient deletion, and verifiability of microgrid data.

Researchers have also proposed schemes for data deletion using overwriting technology. Propose a cloud data deletion scheme called "Proof of erasability" (PoE), which uses random patterns to overwrite the disk and returns the same pattern as the deletion proof[36]. This allows the data owner to verify the successful deletion of the data.

In the field of quantum cryptography, Broadbent and Islam introduce a cryptographic notion called certified deletion, which enables a classical verifier to be convinced that a quantum ciphertext has been deleted by an untrusted party [37]. This notion can be augmented with fully homomorphic encryption (FHE) to enable an untrusted quantum server to compute on encrypted data and simultaneously prove data deletion to a client.

To ensure the transparency and verifiability of the data deletion process, propose a cryptographic solution that allows a user to verify the correct implementation of encryption and key deletion operations inside a Trusted Platform Module (TPM) without accessing its source code[38]. This solution is based on a "trust-but-verify" paradigm, which enhances the user's confidence in the data deletion process.

In summary, there are various cryptographic protocols and techniques that can be employed to ensure the secure and verifiable deletion of data. These include encryption algorithms, hashing techniques, blockchain technology, overwriting methods, and quantum cryptography. By implementing these protocols, Auris can provide a digital certificate as proof to the victim organization, confirming that the stolen data has been permanently deleted.

Notable Incidents

REDACTED

In March 2021, Auris executed a significant attack against a prominent e-commerce platform, compromising millions of user accounts. The group successfully extracted extensive personal information and subsequently contacted the platform's operators, demanding a substantial ransom. After receiving the ransom payment, Auris provided a digital certificate as proof of data deletion, ensuring that the stolen information would not be further exploited.

REDACTED

Another notable incident involving Auris occurred in December 2021, targeting a well-known online retailer. The group successfully infiltrated the retailer's network and exfiltrated a significant amount of personal customer data. They subsequently reached out to the retailer, leveraging the threat of data exposure to extort a considerable ransom. As promised, after receiving the payment, Auris provided evidence of the data's permanent deletion.

Mitigation and Response

To combat the activities of Auris, organizations must adopt a multi-faceted approach that prioritizes robust cybersecurity measures and implements comprehensive incident response plans. This approach should include the following strategies:

  1. Robust Cybersecurity Measures: Organizations should implement regular software patching, network segmentation, intrusion detection systems, and employee training on identifying and avoiding phishing attempts[39].
  2. Offline Backups and Incident Response Plans: It is crucial for organizations to maintain offline backups of critical data and develop comprehensive incident response plans. These plans should outline the steps to be taken in the event of a successful attack, including containment, eradication, and recovery[40].
  3. Engagement with Cybersecurity Professionals and Threat Intelligence Providers: Organizations can benefit from engaging with cybersecurity professionals and threat intelligence providers to detect and mitigate potential Auris attacks. These experts can provide valuable insights and guidance on emerging threats and effective mitigation strategies[41].
  4. Machine Learning and Artificial Intelligence: Machine learning algorithms can be applied to enhance the intelligence and capabilities of cybersecurity systems. These algorithms can analyze large amounts of data and identify patterns and anomalies that may indicate a potential Auris attack. By leveraging machine learning, organizations can improve their ability to detect and respond to threats[42].
  5. Collaboration and Information Sharing: Collaboration among organizations and information sharing within the cybersecurity community can help in detecting and mitigating Auris attacks. By sharing information about new threats, attack techniques, and vulnerabilities, organizations can collectively strengthen their defenses[43].
  6. Addressing Human Factors: Stress, burnout, and security fatigue among cybersecurity professionals can negatively impact their performance and increase the risk of successful attacks. Organizations should prioritize the well-being of their cybersecurity teams and implement measures to mitigate these human factors, such as workload management, training, and support[44]

By adopting these strategies, organizations can enhance their ability to combat the activities of Auris and minimize the impact of successful attacks. It is important to continuously update and adapt these strategies as new threats emerge and technologies evolve.

Conclusion

The emergence of Auris as an APT group targeting e-commerce platforms signifies a notable evolution in cybercriminal tactics. Their focus on extracting personal information and offering proof of data deletion upon receiving ransom payment highlights their sophistication and adaptability. As organizations strive to protect sensitive data and thwart Auris' efforts, collaboration between cybersecurity experts, law enforcement agencies, and affected entities becomes increasingly crucial to safeguarding the digital landscape.

References

  1. ↑ Ahmad, Atif; Webb, Jeb; Desouza, Kevin C.; Boorman, James (2019). "Strategically-motivated advanced persistent threat: Definition, process, tactics and a disinformation model of counterattack". Computers & Security. 86: 402–418. arXiv:2103.15005. doi:10.1016/j.cose.2019.07.001. Unknown parameter |s2cid= ignored (help)
  2. ↑ Pawlick, Jeffrey; Farhang, Sadegh; Zhu, Quanyan (2015). "Flip the Cloud: Cyber-Physical Signaling Games in the Presence of Advanced Persistent Threats". Decision and Game Theory for Security. Lecture Notes in Computer Science. 9406. pp. 289–308. arXiv:1507.00576. doi:10.1007/978-3-319-25594-1_16. ISBN 978-3-319-25593-4. Search this book on
  3. ↑ Quintero-Bonilla, Santiago; Martín Del Rey, Angel (2020). "A New Proposal on the Advanced Persistent Threat: A Survey". Applied Sciences. 10 (11): 3874. doi:10.3390/app10113874.
  4. ↑ De Melo e Silva, Alessandra; Costa Gondim, João José; De Oliveira Albuquerque, Robson; García Villalba, Luis Javier (2020). "A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence". Future Internet. 12 (6): 108. doi:10.3390/fi12060108.
  5. ↑ Pawlick, Jeffrey; Farhang, Sadegh; Zhu, Quanyan (2015). "Flip the Cloud: Cyber-Physical Signaling Games in the Presence of Advanced Persistent Threats". Decision and Game Theory for Security. Lecture Notes in Computer Science. 9406. pp. 289–308. arXiv:1507.00576. doi:10.1007/978-3-319-25594-1_16. ISBN 978-3-319-25593-4. Search this book on
  6. ↑ Pawlick, Jeffrey; Farhang, Sadegh; Zhu, Quanyan (2015). "Flip the Cloud: Cyber-Physical Signaling Games in the Presence of Advanced Persistent Threats". Decision and Game Theory for Security. Lecture Notes in Computer Science. 9406. pp. 289–308. arXiv:1507.00576. doi:10.1007/978-3-319-25594-1_16. ISBN 978-3-319-25593-4. Search this book on
  7. ↑ Pawlick, Jeffrey; Farhang, Sadegh; Zhu, Quanyan (2015). "Flip the Cloud: Cyber-Physical Signaling Games in the Presence of Advanced Persistent Threats". Decision and Game Theory for Security. Lecture Notes in Computer Science. 9406. pp. 289–308. arXiv:1507.00576. doi:10.1007/978-3-319-25594-1_16. ISBN 978-3-319-25593-4. Search this book on
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  15. ↑ Gönç, Deniz (2022). "An Evaluation of Cyber Threat Taxonomies in the framework of Cyber Activism". Bilgi ve İletişim Teknolojileri Dergisi. 4 (2): 171–196. doi:10.53694/bited.1112219. Unknown parameter |s2cid= ignored (help)
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  17. ↑ Gönç, Deniz (2022). "An Evaluation of Cyber Threat Taxonomies in the framework of Cyber Activism". Bilgi ve İletişim Teknolojileri Dergisi. 4 (2): 171–196. doi:10.53694/bited.1112219. Unknown parameter |s2cid= ignored (help)
  18. ↑ Zuroff, David C.; Mongrain, Myriam; Santor, Darcy A. (2004). "Conceptualizing and Measuring Personality Vulnerability to Depression: Comment on Coyne and Whiffen (1995)". Psychological Bulletin. 130 (3): 489–511. doi:10.1037/0033-2909.130.3.489. PMID 15122935.
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  22. ↑ Ruiz-Gaitán, Alba; Moret, Ana M.; Tasias-Pitarch, María; Aleixandre-López, Ana I.; Martínez-Morel, Héctor; Calabuig, Eva; Salavert-Lletí, Miguel; Ramírez, Paula; López-Hontangas, José L.; Hagen, Ferry; Meis, Jacques F.; Mollar-Maseres, Juan; Pemán, Javier (2018). "An outbreak due to Candida auris with prolonged colonisation and candidaemia in a tertiary care European hospital". Mycoses. 61 (7): 498–505. doi:10.1111/myc.12781. PMID 29655180. Unknown parameter |s2cid= ignored (help)
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  27. ↑ Lepak, Alexander J.; Zhao, Miao; Berkow, Elizabeth L.; Lockhart, Shawn R.; Andes, David R. (2017). "Pharmacodynamic Optimization for Treatment of Invasive Candida auris Infection". Antimicrobial Agents and Chemotherapy. 61 (8). doi:10.1128/aac.00791-17. PMC 5527602. PMID 28584152.
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  30. ↑ Lepak, Alexander J.; Zhao, Miao; Berkow, Elizabeth L.; Lockhart, Shawn R.; Andes, David R. (2017). "Pharmacodynamic Optimization for Treatment of Invasive Candida auris Infection". Antimicrobial Agents and Chemotherapy. 61 (8). doi:10.1128/aac.00791-17. PMC 5527602. PMID 28584152.
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  32. ↑ Ullah, Faheem; Edwards, Matthew; Ramdhany, Rajiv; Chitchyan, Ruzanna; Babar, M. Ali; Rashid, Awais (2018). "Data exfiltration: A review of external attack vectors and countermeasures" (PDF). Journal of Network and Computer Applications. 101: 18–54. doi:10.1016/j.jnca.2017.10.016.
  33. ↑ Saeb, Sohrab; Zhang, Mi; Karr, Christopher J.; Schueller, Stephen M.; Corden, Marya E.; Kording, Konrad P.; Mohr, David C. (2015). "Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study". Journal of Medical Internet Research. 17 (7): e175. doi:10.2196/jmir.4273. PMC 4526997. PMID 26180009.
  34. ↑ Wang, Qian; Wang, Cong; Ren, Kui; Lou, Wenjing; Li, Jin (2011). "Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing". IEEE Transactions on Parallel and Distributed Systems. 22 (5): 847–859. doi:10.1109/tpds.2010.183. Unknown parameter |s2cid= ignored (help)
  35. ↑ Zhang, Lihua; Liu, Chunhui; Li, Boping; Fang, Haodong; Gu, Jinguang (2022). "A Secure Storage and Deletion Verification Scheme of Microgrid Data Based on Integrating Blockchain into Edge Computing". Electronics. 11 (23): 4033. doi:10.3390/electronics11234033.
  36. ↑ Tian, Junfeng; Bai, Ruxin; Zhang, Tianfeng (2022). "Supporting Dynamic Update Scheme for Assured Deletion Based-Multi-Copy Association Tree". doi:10.21203/rs.3.rs-1548271/v2.
  37. ↑ Poremba, Alexander (2022). "Quantum Proofs of Deletion for Learning with Errors". arXiv:2203.01610 [quant-ph].
  38. ↑ Hao, Feng; Clarke, Dylan; Zorzo, Avelino Francisco (2016). "Deleting Secret Data with Public Verifiability". IEEE Transactions on Dependable and Secure Computing. 13 (6): 617–629. doi:10.1109/tdsc.2015.2423684. hdl:10923/22151. Unknown parameter |s2cid= ignored (help)
  39. ↑ Chow, Nancy A.; Muñoz, José F.; Gade, Lalitha; Berkow, Elizabeth L.; Li, Xiao; Welsh, Rory M.; Forsberg, Kaitlin; Lockhart, Shawn R.; Adam, Rodney; Alanio, Alexandre; Alastruey-Izquierdo, Ana; Althawadi, Sahar; Araúz, Ana Belén; Ben-Ami, Ronen; Bharat, Amrita; Calvo, Belinda; Desnos-Ollivier, Marie; Escandón, Patricia; Gardam, Dianne; Gunturu, Revathi; Heath, Christopher H.; Kurzai, Oliver; Martin, Ronny; Litvintseva, Anastasia P.; Cuomo, Christina A. (2020). "Tracing the Evolutionary History and Global Expansion of Candida auris Using Population Genomic Analyses". mBio. 11 (2). doi:10.1128/mbio.03364-19. PMC 7188998 Check |pmc= value (help). PMID 32345637 Check |pmid= value (help).
  40. ↑ Chow, Nancy A.; Muñoz, José F.; Gade, Lalitha; Berkow, Elizabeth L.; Li, Xiao; Welsh, Rory M.; Forsberg, Kaitlin; Lockhart, Shawn R.; Adam, Rodney; Alanio, Alexandre; Alastruey-Izquierdo, Ana; Althawadi, Sahar; Araúz, Ana Belén; Ben-Ami, Ronen; Bharat, Amrita; Calvo, Belinda; Desnos-Ollivier, Marie; Escandón, Patricia; Gardam, Dianne; Gunturu, Revathi; Heath, Christopher H.; Kurzai, Oliver; Martin, Ronny; Litvintseva, Anastasia P.; Cuomo, Christina A. (2020). "Tracing the Evolutionary History and Global Expansion of Candida auris Using Population Genomic Analyses". mBio. 11 (2). doi:10.1128/mbio.03364-19. PMC 7188998 Check |pmc= value (help). PMID 32345637 Check |pmid= value (help).
  41. ↑ Chow, Nancy A.; Muñoz, José F.; Gade, Lalitha; Berkow, Elizabeth L.; Li, Xiao; Welsh, Rory M.; Forsberg, Kaitlin; Lockhart, Shawn R.; Adam, Rodney; Alanio, Alexandre; Alastruey-Izquierdo, Ana; Althawadi, Sahar; Araúz, Ana Belén; Ben-Ami, Ronen; Bharat, Amrita; Calvo, Belinda; Desnos-Ollivier, Marie; Escandón, Patricia; Gardam, Dianne; Gunturu, Revathi; Heath, Christopher H.; Kurzai, Oliver; Martin, Ronny; Litvintseva, Anastasia P.; Cuomo, Christina A. (2020). "Tracing the Evolutionary History and Global Expansion of Candida auris Using Population Genomic Analyses". mBio. 11 (2). doi:10.1128/mbio.03364-19. PMC 7188998 Check |pmc= value (help). PMID 32345637 Check |pmid= value (help).
  42. ↑ Sarker, Iqbal H. (2021). "Machine Learning: Algorithms, Real-World Applications and Research Directions". Sn Computer Science. 2 (3): 160. doi:10.1007/s42979-021-00592-x. PMC 7983091 Check |pmc= value (help). PMID 33778771 Check |pmid= value (help).
  43. ↑ Oriola, Oluwafemi; Adeyemo, Adesesan Barnabas; Papadaki, Maria; Kotzé, Eduan (2021). "A collaborative approach for national cybersecurity incident management". Information & Computer Security. 29 (3): 457–484. doi:10.1108/ics-02-2020-0027. Unknown parameter |s2cid= ignored (help)
  44. ↑ Nobles, Calvin (2022). "Stress, Burnout, and Security Fatigue in Cybersecurity: A Human Factors Problem". Holistica – Journal of Business and Public Administration. 13: 49–72. doi:10.2478/hjbpa-2022-0003. Unknown parameter |s2cid= ignored (help)


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