Abstract
Advanced Persistent Threats (APTs) can repeatedly threaten individuals, organisations and national targets, utilising varying tactics and methods to achieve their objectives. This study looks at six such threat groups, namely Shell_Crew, NetTraveler, ProjectSauron, CopyKittens, Volatile Cedar and Transparent Tribe, examines the methods used by each to traverse the cyber kill chain and highlights the array of capabilities that could be employed by adversary targets. Consideration for mitigation and active defence was then made with a view to preventing the effectiveness of the malicious campaigns. The study found that despite the complex nature of some adversaries, often straightforward methods could be employed at various levels in a networked environment to detract from the ability presented by some of the known threats.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M. Hopkins and A. Dehghantanha, “Exploit Kits: The production line of the Cybercrime economy?,” in 2015 2nd International Conference on Information Security and Cyber Forensics, InfoSec 2015, 2016.
S. Homayoun, A. Dehghantanha, M. Ahmadzadeh, S. Hashemi, and R. Khayami, “Know Abnormal, Find Evil: Frequent Pattern Mining for Ransomware Threat Hunting and Intelligence,” IEEE Trans. Emerg. Top. Comput., 2017.
S. Walker-Roberts, M. Hammoudeh, and A. Dehghantanha, “A Systematic Review of the Availability and Efficacy of Countermeasures to Internal Threats in Healthcare Critical Infrastructure,” IEEE Access, 2018.
H. Haddad Pajouh, R. Javidan, R. Khayami, D. Ali, and K.-K. R. Choo, “A Two-layer Dimension Reduction and Two-tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks,” IEEE Trans. Emerg. Top. Comput., pp. 1–1, 2016.
N. Milosevic, A. Dehghantanha, and K.-K. R. Choo, “Machine learning aided Android malware classification,” Comput. Electr. Eng., vol. 61, 2017.
A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning,” IEEE Trans. Sustain. Comput., pp. 1–1, 2018.
E. M. Hutchins, M. J. Cloppert, and R. M. Amin, “Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains.”
D. Kiwia, A. Dehghantanha, K.-K. R. Choo, and J. Slaughter, “A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence,” J. Comput. Sci., Nov. 2017.
H. Haddadpajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A Deep Recurrent Neural Network Based Approach for Internet of Things Malware Threat Hunting,” Futur. Gener. Comput. Syst., 2018.
S. Watson and A. Dehghantanha, “Digital forensics: the missing piece of the Internet of Things promise,” Comput. Fraud Secur., vol. 2016, no. 6, 2016.
M. Conti, A. Dehghantanha, K. Franke, and S. Watson, “Internet of Things Security and Forensics: Challenges and Opportunities,” Futur. Gener. Comput. Syst., Jul. 2017.
H. H. Pajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “Intelligent OS X malware threat detection with code inspection,” J. Comput. Virol. Hacking Tech., 2017.
M. Petraityte, A. Dehghantanha, and G. Epiphaniou, “A Model for Android and iOS Applications Risk Calculation: CVSS Analysis and Enhancement Using Case-Control Studies,” 2018, pp. 219–237.
H. Haughey, G. Epiphaniou, H. Al-Khateeb, and A. Dehghantanha, Adaptive traffic fingerprinting for darknet threat intelligence, vol. 70. 2018.
S. Caltagirone, A. Pendergast, and C. Betz, “The Diamond Model of Intrusion Analysis,” Threat Connect, vol. 298, no. 0704, pp. 1–61, 2013.
A. Lemay, J. Calvet, F. Menet, and J. M. Fernandez, “Survey of publicly available reports on advanced persistent threat actors,” Comput. Secur., vol. 72, pp. 26–59, Jan. 2018.
EMC/RSA, “RSA Incident Response - Emerging Threat Profile: Shell Crew,” no. January, pp. 1–42, 2014.
Kaspersky, “The NetTraveler (aka ‘Travnet’),” 2004.
S. Response and S. Page, “Security Response Backdoor . Remsec indicators of compromise,” pp. 1–13, 2016.
Clearsky, “CopyKittens Attack Group,” Minerva Labs LTD Clear. Cyber Secur., no. Nov, pp. 1–23, 2015.
T. Intelligence, “Volatile cedar,” 2015.
B. K. Baumgartner, “Cedar DGA Infrastructure Statistics :,” pp. 2–6, 2015.
D. Huss, “Operation Transparent Tribe - Threat Insight,” 2016.
Y. H. Chang and Singh Sudeep, “APT Group Sends Spear Phishing Emails to Indian Government Officials « APT Group Sends Spear Phishing Emails to Indian Government Officials | FireEye Inc,” FireEye, 2016.
A. Cook, H. Janicke, R. Smith, and L. Maglaras, “The industrial control system cyber defence triage process,” Comput. Secur., vol. 70, pp. 467–481, Sep. 2017.
Global Research and Analysis Team, “The ProjectSauron APT,” Kaspersky Lab, vol. 02, pp. 1–23, 2016.
O. Osanaiye, H. Cai, K.-K. R. Choo, A. Dehghantanha, Z. Xu, and M. Dlodlo, “Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing,” Eurasip J. Wirel. Commun. Netw., vol. 2016, no. 1, 2016.
A. Azmoodeh, A. Dehghantanha, M. Conti, and K.-K. R. Choo, “Detecting crypto-ransomware in IoT networks based on energy consumption footprint,” J. Ambient Intell. Humaniz. Comput., pp. 1–12, Aug. 2017.
A. Shalaginov, S. Banin, A. Dehghantanha, and K. Franke, Machine learning aided static malware analysis: A survey and tutorial, vol. 70. 2018.
O. M. K. Alhawi, J. Baldwin, and A. Dehghantanha, “Leveraging Machine Learning Techniques for Windows Ransomware Network Traffic Detection,” 2018, pp. 93–106.
S. Homayoun, M. Ahmadzadeh, S. Hashemi, A. Dehghantanha, and R. Khayami, “BoTShark: A Deep Learning Approach for Botnet Traffic Detection,” Springer, Cham, 2018, pp. 137–153.
J. Gill, I. Okere, H. HaddadPajouh, and A. Dehghantanha, Mobile forensics: A bibliometric analysis, vol. 70. 2018.
A. A. James Baldwin, Omar Alhawi, Simone Shaughnessy and A. Dehghantanha, Emerging from The Cloud: A Bibliometric Analysis of Cloud Forensics Studies. Cyber Threat Intelligence- Springer Book, 2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Taylor, P.J., Dargahi, T., Dehghantanha, A. (2019). Analysis of APT Actors Targeting IoT and Big Data Systems: Shell_Crew, NetTraveler, ProjectSauron, CopyKittens, Volatile Cedar and Transparent Tribe as a Case Study. In: Dehghantanha, A., Choo, KK. (eds) Handbook of Big Data and IoT Security. Springer, Cham. https://doi.org/10.1007/978-3-030-10543-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-10543-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10542-6
Online ISBN: 978-3-030-10543-3
eBook Packages: Computer ScienceComputer Science (R0)