Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure
Abstract
:1. Introduction
1.1. Evolution of Cyber Attacks
1.2. Contributions in This Paper
1.3. Organization of the Paper
2. Methodology for Review
2.1. Research Questions
- Question 1: What are the motivations of cyber attackers? To understand the motivations of the adversary, we searched for publications that detailed the modern economic structure of cybercrime. The cybercrime economy has been growing parallelly with the internet, enabling a robust network of adversarial actors and advanced persistent threats (APTs). Answering this question provides insight into the degree of sophistication of the adversary and the level of development.
- Question 2: What are the types of cyber attacks on critical infrastructures? To understand the technical aspects of a cyber attack, this question aims to list and describe the cyber attacks that can be used against critical infrastructures. Answering this question will help develop knowledge of cyber attacks and the means and mediums through which they can occur.
- Question 3: How does a cyber attack impact critical infrastructure and how does it affect citizens? To identify how critical infrastructure is impacted by cyber attacks, this question aims to create an understanding of the expected impacts that a cyber attack will have on critical infrastructure and gain some understanding of the effect it will have on citizens who depend on critical infrastructure services.
- Question 4: How many significant cyber attacks on critical infrastructure have occurred, and which critical infrastructures are targeted? By analyzing the records of significant cyber attacks to account for all of the attacks on the various critical infrastructures, this provides a perspective on the trend in cyber attacks and which CIs can be targeted.
- Question 5: What mitigation strategies are in use to mitigate the effects of a cyber attack? Answering this question will inform security operators about the various solutions that exist to enhance infrastructure protection against cyber attacks. If the mitigation strategies do not address all cyber attack techniques, they could help to identify areas that require future research.
2.2. Selection of Papers and Reports
- Directly reports the events of a historical cyber attack.
- Directly implements a study of attack detection and prevention.
- Describes the organizational aspects of cybercrime.
- Describes the implementable network technology practices for mitigation of cyber attacks.
3. Types of Cyber Attacks on Critical Infrastructure
3.1. Cybercrime Economy
Money Laundering, Theft, Black Markets, and Ransom
3.2. The Ransomware Cyber Attack
Supply Chain Ransomware
3.3. Denial of Service
3.3.1. Flooding in Mesh Networks
3.3.2. Incidents of Denial of Service Attack
3.4. Man-in-the-Middle
3.4.1. IP Spoofing
3.4.2. ARP Spoofing
3.4.3. DNS Spoofing
3.4.4. HTTPS/SSL Hijacking
3.5. Phishing and Remote Execution
3.5.1. Bulk Phishing
3.5.2. Spear Phishing
3.5.3. CEO Phishing and Whaling
3.5.4. Clone Phishing
3.5.5. Additional Phishing Tactics
3.6. False Data Attack (Parameter/Command Injection)
- Targeted constrained FDIA: In this type of attack, data are injected after clear analysis, with a known amount of data inserted to appear realistic.
- Targeted unconstrained FDIA: In this type of attack, the attacker attempts to corrupt the values of some variables, and those variables in turn corrupt the remaining dependent variables.
- Random FDIA: In this type of attack, data packets are randomly distributed without consideration of the real values.
3.6.1. Protocols without Encryption in the CI
3.6.2. Automatic Generator Control
3.6.3. Parameter Modification in Inverters
3.7. Worm and Trojan Malware
- An active selective random scan or sequential scan, in which the worm scans for vulnerable hosts.
- A hit-list scan, where the worm creates a target list and then searches for susceptible hosts.
- A routable scan, which utilizes information about a network to select and scan the IP address space [57].
The Stuxnet Worm
3.8. Trojan
4. Processes for Building Cyber Defenses
4.1. Reason to Train Personnel
4.2. Threat Matrix and Protection Development Process
4.3. Actions of a Defending Organization during a Cyber Attack
- Every node in the network must have lightweight cryptographic functions.
- Attack detection and mitigating actions are necessary and must be used throughout the smart grid.
- Cyber-security testbeds must be created for the purpose of investigating vulnerabilities in the infrastructure [72].
- The security of network protocols must be designed from the application layer to the MAC layer [19].
4.4. IT/OT Practices to Mitigate Malware
- Statistics: An algorithm that utilizes statistical analysis of sample characteristics to determine if the sample is malware.
- Blacklist: The system uses a list of malicious domain names or IP addresses known to be used by ransomware families to identify malware.
- Rule Driven System: A rule-based decision model that finds malware. Rules may include scores such as perceived threats, various threshold values, or rules compatible with malware detection engines.
- Machine Learning-based: Through the use of machine learning (ML) models created with a variety of analysis features, the system can identify malware. ML approaches may analyze instruction opcodes, application programming interface (API) calls, and dynamically linked libraries (DLLs) to build ML classifiers. Systems for detecting malware can identify patterns in the behavior of the malware program.
4.5. The Role of Attribution in Holding Attackers Accountable and Methods to Attribute Attack Network Traffic
- Logging and trace-back queries: Routers may log messages passing through their networks. Backward requests can go up a chain of routers and check if they have seen a previously seen message. As a result, messages that had not previously been classified as harmful can now be attributed. This requires that logging routers be placed in advance, which can lead to cost overruns, poor performance, and numerous other issues. Implementations can also give rise to privacy concerns.
- Input debugging: Defenders can provide an attack pattern as a query to nearby routers, and the router can then report any instances of noticing the pattern. Currently, some distributed denial-of-service (DDoS) attacks are defended against using this strategy. However, it is mostly reactive and only effective against attacks that continually stream data.
- Transmitted message modification: As communications are transferred, routers label them so that their path may be traced. This might affect network performance, increase bandwidth, or interfere with various authentication methods.
- Transmit separate messages: When routing a message, routers also transmit a different message to help with attribution.
- Reconfigure and observe network: Reconfigure the network and go back to a previous phase using the knowledge of what (if anything) changed. Large networks may find it challenging to implement this, and it could lead to new security flaws. On networks owned by others, “controlled flooding” is permitted, but it should only be utilized in specific situations because it could be seen as an attack on third parties.
- Host monitor functions:If a host does not already offer this information, querying functionality can be added (similar to “Query Hosts”). Without the owner’s consent, this is known as a “hack back,” and it calls for strong legal oversight. The information may become substantially less reliable if the host is under the control of an attacker, alerting the attacker.
- Match streams (e.g., via headers, content, timing): Determining which input streams correspond to which output streams involves keeping track of the data streams that are entering and leaving a network or host. This can aid in attribution without requiring knowledge of the network’s or host’s internal state. However, matching can be a challenging technical problem, particularly when dealing with delayed attacks and internal encryption.
- Exploit/force attacker self-identification: To identify the attacker, any information they may have sent, whether on purpose or accidentally, can be used. In some cases, the defender may be able to force the attacker to submit this information. However, many of these techniques rely on extremely technical and specialized approaches (such as beacons, web bugs, cookies, and watermarking) that are easily defeated once an attacker becomes aware of them. When this technique succeeds, it can directly reveal the attacker regardless of how well they have concealed themselves.
- Honeypot/Honeynet: As decoy systems, honeypots and honeynets are used by defenders to bait attackers. Zombie traps (compromised and maliciously controlled computers) and honeynets can instantly reveal any zombies attempting to access the network. However, honeypots and honeynets can only attribute attacks that pass through them, necessitating extensive experience in monitoring and analysis.
- Intrusion detection systems: These systems should be positioned as close as possible to potential attackers (instead of near the defended assets). The placements of the IDSs (which should be close to the attackers) will determine how effective this strategy is. IDSs are notorious for producing many false positives and false negatives, so this strategy frequently necessitates intensive monitoring.
- Filtering of Ingress: Messages can be filtered so that specific links only allow them through if they fulfill particular criteria that make attribution easier. The information for attribution is contained in the message itself, which has the advantage of being transparent to users and requiring no extra storage. The technique’s main limitation is that it can only be used to attribute the locations of internal attacks and often only provides a range of potential attribution values, not a specific location or identity. Frequently, there must be several possible routes for a message to take, leading to uncertainties that reduce the technique’s potency. Network ingress filtering mandates that every message entering a network has a source address in an acceptable range for that network entry point. Using the existing transmission control protocol (TCP)/IP infrastructure, network ingress filtering for IP can be developed and scaled incrementally (one network at a time). The implementation of network ingress filtering by virtually all of the network’s entrance points is necessary for a given network to be successful.
- Spoof prevention: Improving the resistance of protocols or their implementation against fabricated information can significantly reduce the need for examining intermediate systems. However, frequently protocols and/or implementations are difficult to modify to achieve this.
- Secure hosts/routers: The aim is to limit the number of trustworthy intermediate systems that an attacker can access. Although perfect security is unrealistic and this does not accomplish attribution, it simply makes the problem easier to address. This is nevertheless necessary for computer security.
- Surveil Attacker: Direct surveillance of potential or known attackers can prevent advanced attacker strategies. However, this requires prior knowledge of the identity of the expected attacker, and some attackers are very challenging to surveil.
- Employ reverse flow: Data being sent back to the attacker should be marked specifically, and intermediate systems should be able to identify these markings. This can be tracked by stepping stones but requires reverse flow detectors and may be prevented by encryption.
- Combine techniques: Combine multiple approaches. Although it will typically cost more to accomplish, this has a higher chance of success than any other strategy. Special attention must be paid when merging strategies because there is limited expertise in doing so.
5. Standards That Address Cyber Attacks
NIST Guidelines for Smart Grid Cybersecurity
6. Discussion of Results
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
SCADA | Supervisory Control and Data Acquisition |
DoS | Denial of Service |
CIA | Confidentiality, Integrity, and Authenticity |
DNP3 | Distributed Network Protocol 3 |
CI | Critical Infrastructure |
IT | Information Technology |
OT | Operational Technology |
RAAC | Ransomware-as-a-Corporation |
CSIS | Center for Strategic and International Studies |
VPN | Virtual Private Network |
MITM | Man-in-the-Middle |
DNS | Domain Name System |
TCP | Transmission Control Protocol |
SSL | Secure Socket Layer |
FDIA | False Data Injection Attack |
ICMP | Internet Control Message Protocol |
CWE | Common Weakness Enumeration |
ARP | Access Resolution Protocol |
SOC | Security Operating Center |
APT | Advanced Persistent Threat |
References
- Liang, G.; Weller, S.R.; Zhao, J.; Luo, F.; Dong, Z.Y. The 2015 Ukraine Blackout: Implications for False Data Injection Attacks. IEEE Trans. Power Syst. 2017, 32, 3317–3318. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, P.; Ma, L. Denial of service attack and defense method on load frequency control system. J. Frankl. Inst. 2019, 356, 8625–8645. [Google Scholar] [CrossRef]
- Kumar, S.; Kumar, H.; Gunnam, G.R. Security Integrity of Data Collection from Smart Electric Meter under a Cyber Attack. In Proceedings of the 2019 2nd International Conference on Data Intelligence and Security (ICDIS), Island, TX, USA, 28–30 June 2019; pp. 9–13. [Google Scholar] [CrossRef]
- Wei, L.; Sundararajan, A.; Sarwat, A.I.; Biswas, S.; Ibrahim, E. A distributed intelligent framework for electricity theft detection using benford’s law and stackelberg game. In Proceedings of the 2017 Resilience Week (RWS), Wilmington, DE, USA, 18–22 September 2017; pp. 5–11. [Google Scholar] [CrossRef]
- Huang, K.; Siegel, M.; Madnick, S. Systematically Understanding the Cyber Attack Business: A Survey. ACM Comput. Surv. 2018, 51, 70. [Google Scholar] [CrossRef]
- Tufail, S.; Parvez, I.; Batool, S.; Sarwat, A. A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid. Energies 2021, 14, 5894. [Google Scholar] [CrossRef]
- Tufail, S.; Batool, S.; Sarwat, A.I. False data injection impact analysis in ai-based smart grid. In Proceedings of the SoutheastCon 2021, Atlanta, GA, USA, 10–13 March 2021; pp. 1–7. [Google Scholar]
- Riggs, H.; Tufail, S.; Khan, M.; Parvez, I.; Sarwat, A.I. Detection of False Data Injection of PV Production. In Proceedings of the 2021 IEEE Green Technologies Conference (GreenTech), Virtual Conference, 7–9 April 2021; pp. 7–12. [Google Scholar] [CrossRef]
- Olowu, T.O.; Dharmasena, S.; Hernandez, A.; Sarwat, A. Impact Analysis of Cyber Attacks on Smart Grid: A Review and Case Study. In New Research Directions in Solar Energy Technologies; Tyagi, H., Chakraborty, P.R., Powar, S., Agarwal, A.K., Eds.; Springer: Singapore, 2021; pp. 31–51. [Google Scholar] [CrossRef]
- Olowu, T.O.; Dharmasena, S.; Jafari, H.; Sarwat, A. Investigation of False Data Injection Attacks on Smart Inverter Settings. In Proceedings of the 2020 IEEE CyberPELS (CyberPELS), Miami, FL, USA, 13 October 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Sarwat, A.I.; Sundararajan, A.; Parvez, I.; Moghaddami, M.; Moghadasi, A. Toward a Smart City of Interdependent Critical Infrastructure Networks. In Sustainable Interdependent Networks: From Theory to Application; Springer International Publishing: Cham, Switzerland, 2018; pp. 21–45. [Google Scholar] [CrossRef]
- Cyber Security & Infrastructure Security Agency; Critical Infrastructure Sectors. Available online: https://www.cisa.gov/topics/critical-infrastructure-security-and-resilience/critical-infrastructure-sectors (accessed on 9 January 2023).
- Kovacevic, A. Cyber Attacks on Critical Infrastructure: Review and Challenges. In Handbook of Research on Digital Crime; IGI Global: Hershey, PA, USA, 2015; pp. 1–18. [Google Scholar]
- Robert, M.; Lee, M.J.; Assante, T.C. Analysis of the Cyber Attack on the Ukrainian Power Grid. Available online: https://www.eisac.com/s/ (accessed on 2 February 2022).
- Uma, M.; Padmavathi, G. A Survey on Various Cyber Attacks and their Classification. Int. J. Netw. Secur. 2013, 15, 390–396. [Google Scholar]
- Oz, H.; Aris, A.; Levi, A.; Uluagac, A.S. A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions. ACM Comput. Surv. 2022, 54, 238. [Google Scholar] [CrossRef]
- Mohammadhassani, A.; Teymouri, A.; Mehrizi-Sani, A.; Tehrani, K. Performance evaluation of an inverter-based microgrid under cyberattacks. In Proceedings of the 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE), Budapest, Hungary, 2–4 June 2020; pp. 211–216. [Google Scholar]
- Significant Cyber Incidents. Center for Strategic & International Studies. Available online: https://www.csis.org/ (accessed on 4 December 2022).
- Gunduz, M.Z.; Das, R. Cyber-security on smart grid: Threats and potential solutions. Comput. Netw. 2020, 169, 107094. [Google Scholar] [CrossRef]
- Worlds Largest Meat Processing Company Hit by Cyber Attack (JBS). BBC. 2 June 2021. Available online: https://www.bbc.com/news/world-us-canada-57318965 (accessed on 7 July 2021).
- Ransomware on the Rise in Critical Infrastructure Sector. JD Supra. 13 May 2021. Available online: https://www.jdsupra.com/legalnews/ransomware-on-the-rise-in-critical-1687319/ (accessed on 4 December 2022).
- The Curious Case of the Baltimore Ransomware Attack: What You Need to Know. Heimdal Security Blog. 8 September 2020. Available online: https://heimdalsecurity.com/blog/baltimore-ransomware (accessed on 14 November 2022).
- WannaCry Ransomware Attack Summary. Data Protection Report. 17 May 2017. Available online: https://www.dataprotectionreport.com/2017/05/wannacry-ransomware-attack-summary/ (accessed on 5 December 2022).
- Chokshi, N. Hackers Are Holding Baltimore Hostage: How They Struck and What’s Next. The New York Times. 22 May 2019. Available online: https://www.nytimes.com/2019/05/22/us/baltimore-ransomware.html (accessed on 5 December 2022).
- ‘Number of Days’ before Systems back Working—HSE 2021. Section: News. ProteusCyber. 17 May 2021. Available online: https://proteuscyber.com/it/privacy-database/news/4482-number-of-days-before-systems-back-working-hse (accessed on 22 July 2022).
- Hanna, Y.; Cebe, M.; Mercan, S.; Akkaya, K. Efficient Group-Key Management for Low-bandwidth Smart Grid Networks. In Proceedings of the 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aachen, Germany, 25–28 October 2021; pp. 188–193. [Google Scholar]
- Zhi, Y.; Fu, Z.; Sun, X.; Yu, J. Security and privacy issues of UAV: A survey. Mob. Netw. Appl. 2020, 25, 95–101. [Google Scholar] [CrossRef]
- Newaz, A.I.; Sikder, A.K.; Rahman, M.A.; Uluagac, A.S. A survey on security and privacy issues in modern healthcare systems: Attacks and defenses. ACM Trans. Comput. Healthc. 2021, 2, 1–44. [Google Scholar] [CrossRef]
- Al-rimy, B.A.S.; Maarof, M.A.; Shaid, S.Z.M. Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions. Comput. Secur. 2018, 74, 144–166. [Google Scholar] [CrossRef]
- Menn, J. Kaseya ransomware attack sets off race to hack service providers -researchers. Reuters, 4 August 2021. [Google Scholar]
- Lau, F.; Rubin, S.; Smith, M.; Trajkovic, L. Distributed denial of service attacks. In Proceedings of the 2000 IEEE International Conference on Systems, Man and Cybernetics, Nashville, TN, USA, 8–11 October 2000; Volume 3, pp. 2275–2280. [Google Scholar] [CrossRef]
- Parvez, I.; Islam, A.; Kaleem, F. A key management-based two-level encryption method for AMI. In Proceedings of the 2014 IEEE PES General Meeting|Conference & Exposition, National Harbor, MD, USA, 27–31 July 2014; pp. 1–5. [Google Scholar]
- Thomas, M.S.; Ali, I.; Gupta, N. A secure way of exchanging the secret keys in advanced metering infrastructure. In Proceedings of the 2012 IEEE International Conference on Power System Technology (POWERCON), Auckland, New Zealand, 30 October–2 November 2012; pp. 1–7. [Google Scholar]
- Zhang, F.; Mahler, M.; Li, Q. Flooding attacks against secure time-critical communications in the power grid. In Proceedings of the 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany, 23–27 October 2017; pp. 449–454. [Google Scholar]
- Lu, Z.; Wang, W.; Wang, C. Modeling, evaluation and detection of jamming attacks in time-critical wireless applications. IEEE Trans. Mob. Comput. 2013, 13, 1746–1759. [Google Scholar] [CrossRef]
- DiChristopher; Tom, K.F. An Alarmingly Simple Cyberattack Hit Electrical Systems Serving LA and Salt Lake, but Power Never Went Down. 2019. Section: Cybersecurity. Available online: https://finance.yahoo.com/news/alarmingly-simple-cyberattack-hit-electrical-193034191.html (accessed on 4 December 2022).
- Mallik, A.; Ahsan, A.; Shahadat, M.M.Z.; Tsou, J.C. Understanding Man-in-the-middle-attack through Survey of Literature. Indones. J. Comput. Eng. Des. 2019, 1, 44–56. [Google Scholar] [CrossRef]
- Psiaki, M.L.; Humphreys, T.E. GNSS Spoofing and Detection. Proc. IEEE 2016, 104, 1258–1270. [Google Scholar] [CrossRef]
- Schuckers, S.A. Spoofing and anti-spoofing measures. Inf. Secur. Tech. Rep. 2002, 7, 56–62. [Google Scholar] [CrossRef]
- ARP Poisoning. Available online: https://www.radware.com/security/ddos-knowledge-center/ddospedia/arp-poisoning/ (accessed on 17 November 2021).
- Conti, M.; Dragoni, N.; Lesyk, V. A survey of man in the middle attacks. IEEE Commun. Surv. Tutor. 2016, 18, 2027–2051. [Google Scholar] [CrossRef]
- Callegati, F.; Cerroni, W.; Ramilli, M. Man-in-the-Middle Attack to the HTTPS Protocol. IEEE Secur. Priv. 2009, 7, 78–81. [Google Scholar] [CrossRef]
- Cheng, K.; Gao, M.; Guo, R. Analysis and research on HTTPS hijacking attacks. In Proceedings of the 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing, Austin, TX, USA, 24–25 April 2010; Volume 2, pp. 223–226. [Google Scholar]
- What Is HTTPS Spoofing MitM? Secret Double Octopus. Available online: https://doubleoctopus.com/security-wiki/threats-and-tools/https-spoofing/ (accessed on 17 November 2021).
- Verizon Data Breach Investigations Report. Verizon. 2019. Available online: https://www.verizon.com/business/resources/reports/dbir/ (accessed on 17 November 2021).
- Josh Fruhlinger. CSO Online. Equifax Data Breach: What Happened, Who Was Affected, What Was the Impact? Available online: https://www.csoonline.com/article/3444488/equifax-data-breach-faq-what-happened-who-was-affected-what-was-the-impact.html (accessed on 7 May 2022).
- Four Members of China’s Military Indicted over Massive Equifax Breach. Wall Street Journal. 11 February 2020. Available online: https://www.wsj.com/articles/four-members-of-china-s-military-indicted-for-massive-equifax-breach-11581346824 (accessed on 7 May 2022).
- Stavroulakis, P.; Stamp, M. Handbook of Information and Communication Security; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2010. [Google Scholar]
- IC3. Cyber Crime Report; Federal Bureau of Investigation, Internet Crime Complaint Center: Washington, DC, USA, 2020. [Google Scholar]
- Lanna Deamer. The DDoS Threat for Energy and Utility Companies. ElectronicSpecifier. 19 January 2018. Available online: https://www.electronicspecifier.com/products/cyber-security/the-ddos-threat-for-energy-and-utility-companies (accessed on 17 November 2021).
- Spearphishing via Service, Technique T1194—Enterprise|MITRE ATT&CK®. Available online: https://attack.mitre.org/techniques/T1566/003/ (accessed on 14 July 2022).
- Alkhalil, Z.; Hewage, C.; Nawaf, L.; Khan, I. Phishing Attacks: A Recent Comprehensive Study and a New Anatomy. Front. Comput. Sci. 2021, 3, 563060. [Google Scholar] [CrossRef]
- Vishing. In Proceedings of the 5th Annual Conference on Information Security Curriculum Development, Kennesaw, GA, USA, 26–27 September 2008.
- Static detection of cross-site scripting vulnerabilities. In Proceedings of the 2008 ACM/IEEE 30th International Conference on Software Engineering, Leipzig, Germany, 10–18 May 2008.
- Sundararajan, A.; Chavan, A.; Saleem, D.; Sarwat, A.I. A Survey of Protocol-Level Challenges and Solutions for Distributed Energy Resource Cyber-Physical Security. Energies 2018, 11, 2360. [Google Scholar] [CrossRef]
- Ameli, A.; Hooshyar, A.; El-Saadany, E.F.; Youssef, A.M. Attack detection and identification for automatic generation control systems. IEEE Trans. Power Syst. 2018, 33, 4760–4774. [Google Scholar] [CrossRef]
- Pratama, A.; Rafrastara, F.A. Computer worm classification. Int. J. Comput. Sci. Inf. Secur. 2012, 10, 21. [Google Scholar]
- Nissim, N.; Moskovitch, R.; Rokach, L.; Elovici, Y. Detecting unknown computer worm activity via support vector machines and active learning. Pattern Anal. Appl. 2012, 15, 459–475. [Google Scholar] [CrossRef]
- Kerr, P.K.; Rollins, J.; Theohary, C.A. The Stuxnet Computer Worm: Harbinger of an Emerging Warfare Capability; Congressional Research Service: Washington, DC, USA, 2010. [Google Scholar]
- Chandel, S.; Yu, S.; Yitian, T.; Zhili, Z.; Yusheng, H. Endpoint protection: Measuring the effectiveness of remediation technologies and methodologies for insider threat. In Proceedings of the 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (Cyberc), Guilin, China, 17–19 October 2019; pp. 81–89. [Google Scholar]
- Clark, J.; Leblanc, S.; Knight, S. Risks associated with USB hardware trojan devices used by insiders. In Proceedings of the 2011 IEEE International Systems Conference, Montreal, QC, Canada, 4–7 April 2011; pp. 201–208. [Google Scholar]
- Kaspersky, J. BlackEnergy APT Attacks in Ukraine; Kaspersky Co.: Moscow, Russia, 2015. [Google Scholar]
- Bhunia, S.; Hsiao, M.S.; Banga, M.; Narasimhan, S. Hardware Trojan attacks: Threat analysis and countermeasures. Proc. IEEE 2014, 102, 1229–1247. [Google Scholar] [CrossRef]
- Konstantinou, C.; Keliris, A.; Maniatakos, M. Taxonomy of firmware trojans in smart grid devices. In Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17–21 July 2016; pp. 1–5. [Google Scholar]
- Miller, B.; Rowe, D. A Survey SCADA of and Critical Infrastructure Incidents. In Proceedings of the Proceedings of the 1st Annual Conference on Research in Information Technology; New York, NY, USA, 4–7 October 2012, pp. 51–56. [CrossRef]
- Maglaras, L.A.; Kim, K.H.; Janicke, H.; Ferrag, M.A.; Rallis, S.; Fragkou, P.; Maglaras, A.; Cruz, T.J. Cyber security of critical infrastructures. ICT Express 2018, 4, 42–45. [Google Scholar] [CrossRef]
- Alcaide, J.I.; Llave, R.G. Critical infrastructures cybersecurity and the maritime sector. Transp. Res. Procedia 2020, 45, 547–554. [Google Scholar] [CrossRef]
- Liu, X.; Qian, C.; Hatcher, W.G.; Xu, H.; Liao, W.; Yu, W. Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities. IEEE Access 2019, 7, 79523–79544. [Google Scholar] [CrossRef]
- Sundararajan, A.; Wei, L.; Khan, T.; Sarwat, A.I.; Rodrigo, D. A Tri-Modular Framework to Minimize Smart Grid Cyber-Attack Cognitive Gap in Utility Control Centers. In Proceedings of the 2018 Resilience Week (RWS), Denver, CO, USA, 20–23 August 2018; pp. 117–123. [Google Scholar] [CrossRef]
- MITRE ATTCK. ATTCK Matrix for Enterprise. 2022. Available online: https://attack.mitre.org (accessed on 5 November 2022).
- Kifayat, K.; Merabti, M.; Younis, Y.A. Secure Cloud Computing for Critical Infrastructure: A Survey. In Proceedings of the 14th Annual Post Graduate Symposium on The Convergence of Telecommunications, Networking and Broadcasting, Liverpool, UK, 24–25 June 2013; pp. 599–610. [Google Scholar]
- Ozgur, U.; Nair, H.T.; Sundararajan, A.; Akkaya, K.; Sarwat, A.I. An efficient MQTT framework for control and protection of networked cyber-physical systems. In Proceedings of the 2017 IEEE Conference on Communications and Network Security (CNS), Las Vegas, NV, USA, 9–11 October 2017; pp. 421–426. [Google Scholar] [CrossRef]
- Stopel, D.; Boger, Z.; Moskovitch, R.; Shahar, Y.; Elovici, Y. Application of artificial neural networks techniques to computer worm detection. In Proceedings of the The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada, 16–21 July 2006; pp. 2362–2369. [Google Scholar]
- Sundararajan, A.; Khan, T.; Aburub, H.; Sarwat, A.I.; Rahman, S. A Tri-Modular Human-on-the-Loop Framework for Intelligent Smart Grid Cyber-Attack Visualization. In Proceedings of the SoutheastCon 2018, St. Petersburg, FL, USA, 19–22 April 2018; pp. 1–8. [Google Scholar] [CrossRef]
- Sundararajan, A.; Riggs, H.; Jeewani, A.; Sarwat, A.I. Cluster-based Module to Manage Smart Grid Data for an Enhanced Situation Awareness: A Case Study. In Proceedings of the 2019 Resilience Week (RWS), San Antonio, TX, USA, 4–7 November 2019; Volume 1, pp. 81–87. [Google Scholar] [CrossRef]
- Kunta, H.; Induri, B.; Bourgeois, A.G.; Maimon, D.; Ashok, A. Towards an Experimental Testbed to Study CyberWorm Behaviors in Large Scale Networks. In Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization, London, UK, 25 September 2020; pp. 120–121. [Google Scholar]
- Fuentes-García, M.; Camacho, J.; Maciá-Fernández, G. Present and future of network security monitoring. IEEE Access 2021, 9, 112744–112760. [Google Scholar] [CrossRef]
- Nisioti, A.; Mylonas, A.; Yoo, P.D.; Katos, V. From intrusion detection to attacker attribution: A comprehensive survey of unsupervised methods. IEEE Commun. Surv. Tutor. 2018, 20, 3369–3388. [Google Scholar] [CrossRef]
- David, A.; Wheeler, G.N.L. Techniques for Cyber Attack Attribution; IDA Paper P-3792; Institute for Defense Analyses: Alexandria, VA, USA, 2003. [Google Scholar]
- Delbert Tran. The Law of Attribution: Rules for Attribution the Source of a Cyber-Attack. Available online: https://yjolt.org/law-attribution-rules-attributing-source-cyber-attack (accessed on 22 May 2022).
- Skopik, F.; Pahi, T. Under false flag: Using technical artifacts for cyber attack attribution. Cybersecurity 2020, 3, 8. [Google Scholar] [CrossRef]
- Geers, K. The challenge of cyber attack deterrence. Comput. Law Secur. Rev. 2010, 26, 298–303. [Google Scholar] [CrossRef]
- Payne, C.N.; Finlay, L. Addressing Obstacles to Cyber-Attribution: A Model Based on State Response to Cyber-Attack. Georg. Wash. Int. Law Rev. 2017, 49, 535. [Google Scholar]
- Cichonski, P.; Millar, T.; Grance, T.; Scarfone, K. Computer Security Incident Handling Guide; NIST Special Publication; NIST: Gaithersburg, MD, USA, 2012; Volume 800, pp. 1–147. [Google Scholar]
- Sundararajan, A.; Khan, T.; Moghadasi, A.; Sarwat, A.I. Survey on synchrophasor data quality and cybersecurity challenges, and evaluation of their interdependencies. J. Mod. Power Syst. Clean Energy 2019, 7, 449–467. [Google Scholar] [CrossRef]
- Kemp, R. ISO 27018 and personal information in the cloud: First year scorecard. Comput. Law Secur. Rev. 2015, 31, 553–555. [Google Scholar] [CrossRef]
- Pillitteri, V.Y.; Brewer, T.L. Guidelines for Smart Grid Cybersecurity; NIST Special Publication; NIST: Gaithersburg, MD, USA, 2014. [Google Scholar]
Adversary Technique | Types of Cyber Attacks Used | CI | Impact on CI Operations |
---|---|---|---|
Initial Access, Execution, Lateral Movement, Impact | Phishing, Remote Desktop, BlackEnergy3 (FDIA) | Energy (2015–2016) | Opened breakers in substations in Ukraine, causing 230,000 customers to lose power. |
Initial Access, Execution, Impact | Ransomware | Energy (2021) | Fuel shortages for Southeast US with gas prices rising (9–16 cents per gallon) and 10,600 stations without gas. The Colonial Pipeline billing system shutdown for six days. |
Initial Access, Execution, Impact | Stuxnet Worm, Zero-day Vulnerabilities | IT (2009–2011) | Shutdown of uranium enrichment facilities in Natanz, Iran. |
Initial Access, Execution, Persistence, Collection | Trojan Laziok, reconnaissance malware | Energy (2014) | Gathered information from devices on the network that has vulnerabilities. |
Initial Access, Execution, Collection, Impact | Ransomware | Food (2021) | Data of customers, suppliers, and employees were stolen. Productivity was reduced, access to some systems was blocked. A USD 11 million ransom was paid. Operation servers were shutdown and operations halted. |
Initial Access, Execution, Impact | Ransomware | Healthcare (2021) | All Hospital appointments and radiology services were impacted, the ransomware affected Windows operating systems. The failure was experienced across national networks. |
Initial Access, Execution, Impact | Phishing and Ransomware (Roobinhood) | Financial (2019) | Trading services of exchange halted and maintained offline, as computer systems were maliciously encrypted. |
Initial Access, Execution, Collection | Phishing | Financial (Disclosed 2017) | The Equifax data breach resulted in the theft of personal data belonging to 140 million Americans and caused the company’s share price to drop by 13%. |
Initial Access, Execution, Persistence, Collection | Trojan Malware | Financial (2016) | The malware recorded debit cards and their pins from compromised ATM machines. Approximately USD 194,000 was stolen. |
Initial Access, Execution, Collection, Impact | WannaCry Ransomware Cryptoworm | Energy (2017) | Worldwide Microsoft Windows operating systems were ransomed using an older Windows systems vulnerability EternalBlue. |
Initial Access, Execution, Collection | Phishing, Ransomware | IT (2021) | The Accellion data breach and ransomware attack led to the theft of data in the Accellion data management service. |
Initial Access, Execution, Lateral Movement, Impair Process Control | Supply Chain Ransomware | IT (2021) | Over 1500 businesses and organizations halted operations. A software updater released by an IT company, operating as a managed service provider |
Initial Access, Execution, Inhibit Response Function, Impact | Ransomware | Municipal Services (2018) | Required over 5000 government computers to be shut down for 5 days to resolve the attack. Affected servers that were used to issue police warrants and employ new hiring processes, as well as official city complaints could not be submitted. |
Initial Access | Execution | Persistence | Privilege Escalation | Evasion | Discovery | Lateral Movement | Collection | Command and Control | Inhibit Response Function | Impair Process Control | Impact |
---|---|---|---|---|---|---|---|---|---|---|---|
Drive-by Compromise | Change Operating Mode | Hardcoded Credentials | Exploitation for Privilege Escalation | Change Operating Mode | Network Connection Enumeration | Default Credentials | Adversary-in-the-Middle | Commonly Used Port | Activate Firmware Update Mode | Brute Force I/O | Damage to Property |
Exploit Public-Facing Application | Command-Line Interface | Modify Program | Hooking | Exploitation for Evasion | Network Sniffing | Exploitation of Remote Services | Automated Collection | Connection Proxy | Alarm Suppression | Modify Parameter | Denial of Control |
Exploitation of Remote Services | Execution through API | Module Firmware | Indicator Removal on Host | Remote System Discovery | Hardcoded Credentials | Data from Information Repositories | Standard Application Layer Protocol | Block Command Message | Module Firmware | Denial of View | |
External Remote Services | Graphical User Interface | Project File Infection | Masquerading | Remote System Information Discovery | Lateral Tool Transfer | Detect Operating Mode | Block Reporting Message | Spoof Reporting Message | Loss of Availability | ||
Internet Accessible Device | Hooking | System Firmware | Rootkit | Wireless Sniffing | Program Download | I/O Image | Block Serial COM | Unauthorized Command Message | Loss of Control | ||
Remote Services | Modify Controller Tasking | Valid Accounts | Spoof Reporting Message | Remote Services | Monitor Process State | Data Destruction | Loss of Productivity and Revenue | ||||
Replication Through Removable Media | Native API | Valid Accounts | Point & Tag Identification | Denial of Service | Loss of Protection | ||||||
Rogue Master | Scripting | Program Upload | Device Restart/Shutdown | Loss of Safety | |||||||
Spearphishing Attachment | User Execution | Screen Capture | Manipulate I/O Image | Loss of View | |||||||
Supply Chain Compromise | Wireless Sniffing | Modify Alarm Settings | Manipulation of Control | ||||||||
Transient Cyber Asset | Rootkit | Manipulation of View | |||||||||
Wireless Compromise | Service Stop | Theft of Operational Information | |||||||||
System Firmware |
Body | Standard | Core Contribution | Adversary Technique Mitigated |
---|---|---|---|
ISO/IEC | 27018:2019 | Provides security techniques and a code of practice for the protection of personally identifiable information in public clouds with guidelines based on ISO/IEC 27002. | Initial access, discovery, collection |
ISO/IEC | 27037:2012 | Secure techniques for identifying, collecting, and preservation of digital evidence, and will assist organizations in attributing blame based on digital evidence. | All Techniques |
ISO/IEC | 27040:2015 | Guidelines on the creation of a low-risk data management security system. This includes security for devices and media, applications, and services, and security relevant to end-users. | Initial access, lateral movement, inhibit response function, privilege escalation |
ISO | 22301 | A framework for organizations to be resilient and continue business operations during and after a cyber attack. Develops business continuation plans in the event of a disruption. | Inhibit response function, impair process control, impact |
ISO/IEC | 27001 | A framework for the implementation of secure corporate enterprise computer systems. Details of the implementation of security controls to manage risks. | All Techniques |
ISO/IEC | 27002 | Extends ISO/IEC 27001 standard with guidelines on best practices. Provides organizations with generic information security controls, including implementation guidance. Defined use for an information security management system based on ISO/IEC 27001. | All techniques |
ISO/IEC | 27031 | Information and communication technology guidelines for business continuity. Covering concepts and best practices. | - |
ISO/IEC | 27032 | Guidance on cybersecurity management system, and best practices for information security. | Lateral movement, inhibit response function, collection |
ISO/IEC | 27701 | Specifications for building a privacy information management system that is based on ISO 27001. Published to address a growing need for a framework for global data privacy. | Collection |
NIST | Cyber-security framework | Guideline for managing cybersecurity risks based on the existing best practices, guidelines, and standards provided in three components: core, implementation tiers, and profiles. | All techniques |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Riggs, H.; Tufail, S.; Parvez, I.; Tariq, M.; Khan, M.A.; Amir, A.; Vuda, K.V.; Sarwat, A.I. Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure. Sensors 2023, 23, 4060. https://doi.org/10.3390/s23084060
Riggs H, Tufail S, Parvez I, Tariq M, Khan MA, Amir A, Vuda KV, Sarwat AI. Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure. Sensors. 2023; 23(8):4060. https://doi.org/10.3390/s23084060
Chicago/Turabian StyleRiggs, Hugo, Shahid Tufail, Imtiaz Parvez, Mohd Tariq, Mohammed Aquib Khan, Asham Amir, Kedari Vineetha Vuda, and Arif I. Sarwat. 2023. "Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure" Sensors 23, no. 8: 4060. https://doi.org/10.3390/s23084060
APA StyleRiggs, H., Tufail, S., Parvez, I., Tariq, M., Khan, M. A., Amir, A., Vuda, K. V., & Sarwat, A. I. (2023). Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure. Sensors, 23(8), 4060. https://doi.org/10.3390/s23084060