The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
Abstract
:1. Introduction
- An in-depth analysis of the fog computing security challenges, big data privacy, and trust concerns was performed in relation to fog-based IoT along with their existing solutions and respective limitations.
- It enacts the securing of big data with a novel functionality-based fog architecture taxonomy to categorize threats and security challenges in fog-enabled IoT systems accompanied by fog-enabled IoT applications security requirements. This provides a comprehensive comparison of state-of-the-art contributions in the field according to their security service.
- Study of complementary interrelationship between blockchain and fog computing exploring blockchain-based solutions to cater privacy and security problems in fog paradigm along with the review of security requirements analysis of the fog-enabled IoT application with the combined blockchain.
2. Data-Intensive IoT Applications
2.1. Smart Homes
2.2. Smart Cities
2.3. Smart Healthcare
2.4. Smart Environment and Agriculture
2.5. Energy Conservation
3. Fog Computing Architecture
- Core-network and service layer
- Data center layer
- Device layer with inter and cross layer communication technologies
4. Fog-Enabled IoT Applications Security Requirements
5. Fog Computing Security Challenges
5.1. Core-Network and Service Level Challenges
5.1.1. Identity Verification
5.1.2. Access Control
5.1.3. Lightweight Protocol Design
5.1.4. Intrusion Detection Challenges
5.1.5. Trust Management
5.1.6. Privacy-Conserving Packet Forwarding
5.1.7. Rogue Fog Node Detection
5.2. Data Center Level Security Challenges
5.2.1. Data Identification, Aggregation and Integrity
5.2.2. Secure Content Distribution
5.2.3. Distributed Computation Challenges
5.2.4. Secure Big Data Analysis
5.2.5. Secure Computation
5.2.6. Verifiable Computation
5.3. Device Level Security Challenges
5.3.1. Confidentiality
- Authentication: As fog computing services are offered to an enormous number of end users through front end fog nodes, authentication becomes a critical issue. Fog nodes require authentication at different levels to ensure security in fog computing, as explained by Stojmenovic et al. [102]. The existing conventional PKI-based authentication is unable to overcome the security issue being less efficient and with lower scalability options. On the other hand, there exist simple, user-friendly and secure solutions to cater the authentication issues in a location limited channel while depending on physical contact in local ad-hoc wireless network [130]. Moreover, biometric authentication has emerged as an important technology when it comes to authentication in mobile computing, cloud computing and fog computing. Fingerprint authentication, touch-based authentication or face authentication are few examples [98].
- Privacy: Users are getting more concerned about the breaching to their private and sensitive information such as personal data, location or other information while using the cloud services, IoT or wireless networks. Therefore, it is the most crucial challenge to preserve the privacy in distributed fog environment, as the fog nodes operate at user’s end and must gather more sensitive data than the cloud. Several researchers have proposed various techniques to preserve the privacy in different setups such as wireless network, online social network [131], smart grid [132] and cloud [98,132].
- Identity privacy: IoT user’s identity must be protected and preserved from public and other IoT user to prevent impersonation attacks. Different pseudonym techniques [133,134,135] have been proposed to preserve identity privacy. However, periodic pseudonyms may lead to heavy computation cost in resource constraint IoT domain. Furthermore, group signature [134] and connection anonymization [136,137,138] techniques are also proposed for protecting identity privacy [94].
- Data privacy: The algorithms to preserve privacy in fog networks run between cloud and fog, but the fact that these algorithms utilize a huge amount of resources at the edge devices cannot be ignored. Fog nodes collect sensitive data that are generated through end devices and sensors [98]. At local gateways, homomorphic encryption can be employed without decryption to permit privacy-preserving collection [139]. Another technique that differential privacy [140] is employed in the case of statistical queries to ensure the privacy of uninformed data entries.
- Usage privacy: Fog computing comes with another very important concern of users’ usage pattern privacy. For instance, the smart meter in smart grids reads and collects a huge amount of data that are private to users, such as at what times user is unavailable at home, the consumption pattern, switching on and off certain appliances, etc.; such information is a threat to users’ privacy. Many researchers addressed privacy preserving techniques in smart metering [141,142,143]. It is unfortunate that these techniques cannot be employed in fog computing because of unavailability of a trusted third-party device to cater energy limitations. One approach to preserve privacy is to create fake tasks by fog client and send them to other nodes; in this way, the real tasks are hidden behind fake ones. However, this solution is inefficient in terms of cost and energy consumption. Therefore, an effective approach is to design a solution that divides the application in a way that ensures the usage of distributed resources minimizes the disclosing private information [98].
- Location privacy: The term location privacy denotes the privacy of the location of fog clients in fog computing. When a fog client divests the task to fog nodes, it assumes that those nodes are located nearby, and other nodes are distant, though this is not always the case. Moreover, the fog client may use several fog services at different locations, there are fair chances that the path trajectory may be disclosed to fog nodes. The location privacy is at risk as long as the fog client is attached to a person or device [98]. One way to hide the location of the fog clients is to obfuscate the fog client identity, so that even the fog node knows that client is nearby and still unable to locate it. Wei et al. [144] proposed several techniques to obfuscate the identity, one of which is to employ a trustworthy third party which may generate false identities for each fog client. In real scenarios, it is not necessary for a fog client to choose the nearest fog node, but even if it does so, the client may undergo some criteria such as reputation, latency or load balance to reach that node which may utilize more resources than usual. This can lead the node to have an idea of clients’ location but not in a precise manner. Gao et al. [145] proposed a method to preserve the privacy of client’s location in such situations.
5.3.2. Light-Weight Trust Management
6. Blockchain: A Versatile Security Solution
Blockchain and Fog-Enabled IoT Systems
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Zio, E. Critical Infrastructures Vulnerability and Risk Analysis. Eur. J. Secur. Res. 2016, 1, 97–114. [Google Scholar] [CrossRef]
- Baker, T.; Asim, M.; MacDermott, Á.; Iqbal, F.; Kamoun, F.; Shah, B.; Alfandi, O.; Hammoudeh, M. A secure fog-based platform for SCADA-based IoT critical infrastructure. Softw. Pract. Exp. 2019. [Google Scholar] [CrossRef]
- Georgakopoulos, D.; Jayaraman, P.; Fazia, M.; Villari, M.; Ranjan, R. Internet of Things and Edge Cloud Computing Roadmap for Manufacturing. IEEE Cloud Comput. 2016, 4, 66–73. [Google Scholar] [CrossRef]
- Sajid, A.; Abbas, H.; Saleem, K. Cloud-assisted iot-based scada systems security: A review of the state of the art and future challenges. IEEE Acc. 2016, 4, 1375–1384. [Google Scholar] [CrossRef]
- Kröger, W. Critical infrastructures at risk: A need for a new conceptual approach and extended analytical tools. Reliab. Eng. Syst. Saf. 2008, 93, 1781–1787. [Google Scholar] [CrossRef]
- Granic, I.; Lamey, A.V. The self-organization of the Internet and changing modes of thought. New Ideas Psychol. 2000, 18, 93–107. [Google Scholar] [CrossRef]
- Kelly, D.; Hammoudeh, M. Optimisation of the public key encryption infrastructure for the internet of things. In Proceedings of the 2nd International Conference on Future Networks and Distributed Systems, Amman, Jordan, 26–27 June 2018; p. 45. [Google Scholar]
- Ni, J.; Zhang, K.; Lin, X.; Shen, X. Securing fog computing for internet of things applications: Challenges and solutions. IEEE Commun. Surv. Tutor. 2017, 20, 601–628. [Google Scholar] [CrossRef]
- Abbas, N.; Asim, M.; Tariq, N.; Baker, T.; Abbas, S. A Mechanism for Securing IoT-enabled Applications at the Fog Layer. J. Sens. Actuator Netw. 2019, 8, 16. [Google Scholar] [CrossRef]
- Networking, C.V. Cisco Global Cloud Index: Forecast and Methodology, 2014–2019; White Paper; Cisco: San Jose, CA, USA, 2013. [Google Scholar]
- Zeng, X.; Garg, S.; Strazdins, P.; Jayaraman, P.; Georgakopoulos, D.; Ranjan, R. IOTSim: A simulator for analysing IoT applications. J. Syst. Archit. 2017, 72, 93–107. [Google Scholar] [CrossRef]
- Ma, Y.; Wang, L.; Liu, P.; Ranjan, R. Towards building a data-intensive index for big data computing—A case study of Remote Sensing data processing. Inf. Sci. 2015, 319, 171–188. [Google Scholar] [CrossRef]
- Pàmies-Estrems, D.; Kaaniche, N.; Laurent, M.; Castellà-Roca, J.; Garcia-Alfaro, J. Lifelogging protection scheme for internet-based personal assistants. In Data Privacy Management, Cryptocurrencies and Blockchain Technology; Springer: Berlin, Germany, 2018; pp. 431–440. [Google Scholar]
- Liang, K.; Zhao, L.; Chu, X.; Chen, H.H. An integrated architecture for software defined and virtualized radio access networks with fog computing. IEEE Netw. 2017, 31, 80–87. [Google Scholar] [CrossRef]
- Almeida, V.A.; Doneda, D.; Monteiro, M. Governance challenges for the Internet of Things. IEEE Internet Comput. 2015, 19, 56–59. [Google Scholar] [CrossRef]
- Roman, R.; Lopez, J.; Mambo, M. Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Gener. Comput. Syst. 2018, 78, 680–698. [Google Scholar] [CrossRef]
- Granjal, J.; Monteiro, E.; Silva, J.S. Security for the internet of things: A survey of existing protocols and open research issues. IEEE Commun. Surv. Tutor. 2015, 17, 1294–1312. [Google Scholar] [CrossRef]
- Zhang, K.; Liang, X.; Lu, R.; Yang, K.; Shen, X.S. Exploiting mobile social behaviors for sybil detection. In Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM), Hong Kong, China, 26 April–1 May 2015; pp. 271–279. [Google Scholar]
- Zhou, J.; Cao, Z.; Dong, X.; Lin, X.; Vasilakos, A.V. Securing m-healthcare social networks: Challenges, countermeasures and future directions. IEEE Wirel. Commun. 2013, 20, 12–21. [Google Scholar] [CrossRef]
- Lyu, L.; Jin, J.; Rajasegarar, S.; He, X.; Palaniswami, M. Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering. IEEE Internet Things J. 2017, 4, 1174–1184. [Google Scholar] [CrossRef]
- Ghafir, I.; Prenosil, V.; Hammoudeh, M.; Baker, T.; Jabbar, S.; Khalid, S.; Jaf, S. BotDet: A System for Real Time Botnet Command and Control Traffic Detection. IEEE Acc. 2018, 6, 38947–38958. [Google Scholar] [CrossRef]
- Nepal, S.; Ranjan, R.; Choo, K.K.R. Trustworthy Processing of Healthcare Big Data in Hybrid Clouds. IEEE Cloud Comput. 2015, 2, 78–84. [Google Scholar] [CrossRef]
- Luong, N.C.; Hoang, D.T.; Wang, P.; Niyato, D.; Kim, D.I.; Han, Z. Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey. IEEE Commun. Surv. Tutor. 2016, 18, 2546–2590. [Google Scholar] [CrossRef]
- Farahani, B.; Firouzi, F.; Chang, V.; Badaroglu, M.; Constant, N.; Mankodiya, K. Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Gener. Comput. Syst. 2018, 78, 659–676. [Google Scholar] [CrossRef]
- Ni, J.; Lin, X.; Zhang, K.; Yu, Y.; Shen, X.S. Device-invisible two-factor authenticated key agreement protocol for BYOD. In Proceedings of the 2016 IEEE/CIC International Conference on Communications in China (ICCC), Chengdu, China, 27–29 July 2016; pp. 1–6. [Google Scholar]
- Jogunola, O.; Ikpehai, A.; Anoh, K.; Adebisi, B.; Hammoudeh, M.; Son, S.Y.; Harris, G. State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System. Energies 2017, 10, 2106. [Google Scholar] [CrossRef]
- Vieira, K.; Schulter, A.; Westphall, C.; Westphall, C. Intrusion detection for grid and cloud computing. It Prof. 2010, 12, 38–43. [Google Scholar] [CrossRef]
- Jogunola, O.; Ikpehai, A.; Anoh, K.; Adebisi, B.; Hammoudeh, M.; Gacanin, H.; Harris, G. Comparative Analysis of P2P Architectures for Energy Trading and Sharing. Energies 2018, 11, 62. [Google Scholar] [CrossRef]
- Byers, C.C. Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled iot networks. IEEE Commun. Mag. 2017, 55, 14–20. [Google Scholar] [CrossRef]
- Mushunuri, V.; Kattepur, A.; Rath, H.K.; Simha, A. Resource optimization in fog enabled IoT deployments. In Proceedings of the 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), Valencia, Spain, 8–11 May 2017; pp. 6–13. [Google Scholar]
- Charalampidis, P.; Tragos, E.; Fragkiadakis, A. A fog-enabled IoT platform for efficient management and data collection. In Proceedings of the 2017 IEEE 22nd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Lund, Sweden, 19–21 June 2017; pp. 1–6. [Google Scholar]
- Azimi, I.; Anzanpour, A.; Rahmani, A.M.; Pahikkala, T.; Levorato, M.; Liljeberg, P.; Dutt, N. HiCH: Hierarchical fog-assisted computing architecture for healthcare IoT. ACM Trans. Embed. Comput. Syst. (TECS) 2017, 16, 174. [Google Scholar] [CrossRef]
- Gazis, V. A Survey of Standards for Machine-to-Machine and the Internet of Things. IEEE Commun. Surv. Tutor. 2017, 19, 482–511. [Google Scholar] [CrossRef]
- Kim, J.T. Requirement of security for IoT application based on gateway system. Communications 2015, 9, 201–208. [Google Scholar] [CrossRef]
- Agustin, J.P.C.; Jacinto, J.H.; Limjoco, W.J.R.; Pedrasa, J.R.I. IPv6 routing protocol for low-power and lossy networks implementation in network simulator—3. In Proceedings of the TENCON 2017-2017 IEEE Region 10 Conference, Penang, Malaysia, 5–8 November 2017; pp. 3129–3134. [Google Scholar]
- Baig, Z.A.; Szewczyk, P.; Valli, C.; Rabadia, P.; Hannay, P.; Chernyshev, M.; Johnstone, M.; Kerai, P.; Ibrahim, A.; Sansurooah, K.; et al. Future challenges for smart cities: Cyber-security and digital forensics. Digit. Investig. 2017, 22, 3–13. [Google Scholar] [CrossRef]
- Liu, C.; Qiu, J. Study on a Secure Wireless Data Communication in Internet of Things Applications. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 2015, 15, 18. [Google Scholar]
- Chandrasekhar, S.; Singhal, M. Efficient and scalable query authentication for cloud-based storage systems with multiple data sources. IEEE Trans. Serv. Comput. 2017, 10, 520–533. [Google Scholar] [CrossRef]
- Daneva, M.; Lazarov, B. Requirements for smart cities: Results from a systematic review of literature. In Proceedings of the 2018 12th International Conference on Research Challenges in Information Science (RCIS), Nantes, France, 29–31 May 2018; pp. 1–6. [Google Scholar]
- Hui, T.K.; Sherratt, R.S.; Sánchez, D.D. Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies. Future Gener. Comput. Syst. 2017, 76, 358–369. [Google Scholar] [CrossRef]
- Khan, Z.; Pervez, Z.; Abbasi, A.G. Towards a secure service provisioning framework in a smart city environment. Future Gener. Comput. Syst. 2017, 77, 112–135. [Google Scholar] [CrossRef]
- Sundaravadivel, P.; Kougianos, E.; Mohanty, S.P.; Ganapathiraju, M.K. Everything you wanted to know about smart health care: Evaluating the different technologies and components of the internet of things for better health. IEEE Consum. Electron. Mag. 2018, 7, 18–28. [Google Scholar] [CrossRef]
- Terzi, D.S.; Arslan, B.; Sagiroglu, S. Smart grid security evaluation with a big data use case. In Proceedings of the 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), Doha, Qatar, 10–12 April 2018; pp. 1–6. [Google Scholar]
- Wu, F.; Xu, L.; Kumari, S.; Li, X.; Shen, J.; Choo, K.K.R.; Wazid, M.; Das, A.K. An efficient authentication and key agreement scheme for multi-gateway wireless sensor networks in IoT deployment. J. Netw. Comput. Appl. 2017, 89, 72–85. [Google Scholar] [CrossRef]
- Hussain, R.; Abdullah, I. Review of Different Encryptionand Decryption Techniques Used for Security and Privacy of IoT in Different Applications. In Proceedings of the 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 12–15 August 2018; pp. 293–297. [Google Scholar]
- Arış, A.; Oktuğ, S.F.; Voigt, T. Security of Internet of Things for a Reliable Internet of Services. In Autonomous Control for a Reliable Internet of Services; Springer: Berlin, Germany, 2018; pp. 337–370. [Google Scholar]
- Mishra, A.K.; Tripathy, A.K.; Puthal, D.; Yang, L.T. Analytical Model for Sybil Attack Phases in Internet of Things. IEEE Internet Things J. 2018, 6, 379–387. [Google Scholar] [CrossRef]
- Fadele, A.A.; Othman, M.; Hashem, I.A.T.; Yaqoob, I.; Imran, M.; Shoaib, M. A novel countermeasure technique for reactive jamming attack in internet of things. Multimed. Tools Appl. 2018, 1–22. [Google Scholar] [CrossRef]
- Alaba, F.A.; Othman, M.; Hashem, I.A.T.; Alotaibi, F. Internet of Things security: A survey. J. Netw. Comput. Appl. 2017, 88, 10–28. [Google Scholar] [CrossRef]
- Liang, L.; Zheng, K.; Sheng, Q.; Wang, W.; Fu, R.; Huang, X. A Denial of Service Attack Method for IoT System in Photovoltaic Energy System. In Proceedings of the International Conference on Network and System Security, Hong Kong, China, 27–29 August 2017; pp. 613–622. [Google Scholar]
- Amin, R.; Kumar, N.; Biswas, G.; Iqbal, R.; Chang, V. A light weight authentication protocol for IoT-enabled devices in distributed Cloud Computing environment. Future Gener. Comput. Syst. 2018, 78, 1005–1019. [Google Scholar] [CrossRef]
- Lin, X.; Ni, J.; Shen, X.S. Summary and Future Directions. In Privacy-Enhancing Fog Computing and Its Applications; Springer: Berlin, Germany, 2018; pp. 87–89. [Google Scholar]
- Giri, D.; Borah, S.; Pradhan, R. Approaches and Measures to Detect Wormhole Attack in Wireless Sensor Networks: A Survey. In Advances in Communication, Devices and Networking; Springer: Berlin, Germany, 2018; pp. 855–864. [Google Scholar]
- Airehrour, D.; Gutierrez, J.A.; Ray, S.K. SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things. Future Gener. Comput. Syst. 2018, 93, 860–876. [Google Scholar] [CrossRef]
- Huang, C.; Liu, D.; Ni, J.; Lu, R.; Shen, X. Reliable and Privacy-Preserving Selective Data Aggregation for Fog-Based IoT. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; pp. 1–6. [Google Scholar]
- Singh, C.R. Analysis of Router Poisoning using network attacks. Int. Res. J. Eng. Technol. (IRJET) 2018, 5, 775–780. [Google Scholar]
- Zarpelão, B.B.; Miani, R.S.; Kawakani, C.T.; de Alvarenga, S.C. A survey of intrusion detection in Internet of Things. J. Netw. Comput. Appl. 2017, 84, 25–37. [Google Scholar] [CrossRef]
- Jain, A.; Jain, S. A Survey on Miscellaneous Attacks and Countermeasures for RPL Routing Protocol in IoT. In Emerging Technologies in Data Mining and Information Security; Springer: Berlin, Germany, 2019; pp. 611–620. [Google Scholar]
- Aman, M.N.; Sikdar, B.; Chua, K.C.; Ali, A. Low Power Data Integrity in IoT Systems. IEEE Internet Things J. 2018, 5, 3102–3113. [Google Scholar] [CrossRef]
- Lu, Y.; Da Xu, L. Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics. IEEE Internet Things J. 2018. [Google Scholar] [CrossRef]
- Zhang, P.; Nagarajan, S.G.; Nevat, I. Secure Location of Things (SLOT): Mitigating Localization Spoofing Attacks in the Internet of Things. IEEE Internet Things J. 2017, 4, 2199–2206. [Google Scholar] [CrossRef]
- Park, J.H.; Kim, H.J.; Sung, M.H.; Lee, D.H. Public key broadcast encryption schemes with shorter transmissions. IEEE Trans. Broadcast. 2008, 54, 401–411. [Google Scholar] [CrossRef]
- Quercia, D.; Hailes, S. Sybil attacks against mobile users: Friends and foes to the rescue. In Proceedings of the 2010 Proceedings IEEE INFOCOM, San Diego, CA, USA, 14–19 March 2010; pp. 1–5. [Google Scholar]
- Alrawais, A.; Alhothaily, A.; Hu, C.; Cheng, X. Fog computing for the internet of things: Security and privacy issues. IEEE Internet Comput. 2017, 21, 34–42. [Google Scholar] [CrossRef]
- Mukherjee, M.; Matam, R.; Shu, L.; Maglaras, L.; Ferrag, M.A.; Choudhury, N.; Kumar, V. Security and privacy in fog computing: Challenges. IEEE Acc. 2017, 5, 19293–19304. [Google Scholar] [CrossRef]
- Svoboda, J.; Ghafir, I.; Prenosil, V. Network monitoring approaches: An overview. Int. J. Adv. Comput. Netw. Secur. 2015, 5, 88–93. [Google Scholar]
- Choo, K.K.R. Cloud computing: Challenges and future directions. In Trends and Issues in Crime and Criminal Justice; Australian Institute of Criminology: Canberra, Australia, 2010; p. 1. [Google Scholar]
- Landau, S. Highlights from making sense of Snowden, part II: What’s significant in the NSA revelations. IEEE Secur. Priv. 2014, 12, 62–64. [Google Scholar] [CrossRef]
- Yi, S.; Li, C.; Li, Q. A survey of fog computing: Concepts, applications and issues. In Proceedings of the 2015 Workshop on Mobile Big Data, Hangzhou, China, 21 June 2015; pp. 37–42. [Google Scholar]
- Hao, Z.; Tang, Y.; Zhang, Y.; Novak, E.; Carter, N.; Li, Q. SMOC: A secure mobile cloud computing platform. In Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM), Hong Kong, China, 26 April–1 May 2015; pp. 2668–2676. [Google Scholar]
- Thota, C.; Sundarasekar, R.; Manogaran, G.; Varatharajan, R.; Priyan, M. Centralized fog computing security platform for IoT and cloud in healthcare system. In Fog Computing: Breakthroughs in Research and Practice; IGI Global: Hershey, PA, USA, 2018; pp. 365–378. [Google Scholar]
- Mandlekar, V.G.; Mahale, V.; Sancheti, S.S.; Rais, M.S. Survey on Fog Computing Mitigating Data Theft Attacks in Cloud. Int. J. Innov. Res. Comput. Sci. Technol. 2014, 2, 13–16. [Google Scholar]
- Sandhu, R.; Sohal, A.S.; Sood, S.K. Identification of malicious edge devices in fog computing environments. Inf. Secur. J. Glob. Perspect. 2017, 26, 213–228. [Google Scholar] [CrossRef]
- Zhang, T.; Antunes, H.; Aggarwal, S. Defending connected vehicles against malware: Challenges and a solution framework. IEEE Internet Things J. 2014, 1, 10–21. [Google Scholar] [CrossRef]
- Chiang, M.; Zhang, T. Fog and IoT: An overview of research opportunities. IEEE Internet Things J. 2016, 3, 854–864. [Google Scholar] [CrossRef]
- Li, C.; Qin, Z.; Novak, E.; Li, Q. Securing SDN Infrastructure of IoT–Fog Networks From MitM Attacks. IEEE Internet Things J. 2017, 4, 1156–1164. [Google Scholar] [CrossRef]
- Blaze, M.; Bleumer, G.; Strauss, M. Divertible Protocols and Atomic Proxy Cryptography; Springer: Berlin, Germany, 1998; pp. 127–144. [Google Scholar]
- van Dijk, M.; Gentry, C.; Halevi, S.; Vaikuntanathan, V. Fully Homomorphic Encryption over the Integers. In Advances in Cryptology—EUROCRYPT 2010; Gilbert, H., Ed.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 24–43. [Google Scholar]
- Choi, S.G.; Katz, J.; Kumaresan, R.; Cid, C. Multi-client non-interactive verifiable computation. In Theory of Cryptography; Springer: Berlin, Germany, 2013; pp. 499–518. [Google Scholar]
- Salonikias, S.; Mavridis, I.; Gritzalis, D. Access control issues in utilizing fog computing for transport infrastructure. In Proceedings of the International Conference on Critical Information Infrastructures Security, Berlin, Germany, 5–7 October 2015; pp. 15–26. [Google Scholar]
- Smart, N.P.; Vercauteren, F. Fully homomorphic encryption with relatively small key and ciphertext sizes. In Proceedings of the International Workshop on Public Key Cryptography, Paris, France, 26–28 May 2010; pp. 420–443. [Google Scholar]
- Belguith, S.; Kaaniche, N.; Laurent, M.; Jemai, A.; Attia, R. Phoabe: Securely outsourcing multi-authority attribute based encryption with policy hidden for cloud assisted iot. Comput. Netw. 2018, 133, 141–156. [Google Scholar] [CrossRef]
- Belguith, S.; Kaaniche, N.; Russello, G. Lightweight attribute-based encryption supporting access policy update for cloud assisted IoT. In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications-Volume 1: SECRYPT, Porto, Portugal, 26–28 July 2018; pp. 135–146. [Google Scholar]
- Final Lightweight Cryptography Status Report, European Network of Excellence in Cryptology II D.SYM.12. 2012. Available online: http://www.ecrypt.eu.org/ecrypt2/documents/D.SYM.12.pdf (accessed on 5 December 2018).
- Arshad, J.; Townend, P.; Xu, J. An abstract model for integrated intrusion detection and severity analysis for clouds. Int. J. Cloud Appl. Comput. (IJCAC) 2011, 1, 1–16. [Google Scholar] [CrossRef]
- Hamad, H.; Al-Hoby, M. Managing intrusion detection as a service in cloud networks. Int. J. Comput. Appl. 2012, 41. [Google Scholar] [CrossRef]
- Houmansadr, A.; Zonouz, S.A.; Berthier, R. A cloud-based intrusion detection and response system for mobile phones. In Proceedings of the 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops (DSN-W), Hong Kong, China, 27–30 June 2011; pp. 31–32. [Google Scholar]
- Jain, A.K.; Tokekar, V.; Shrivastava, S. Security Enhancement in MANETs Using Fuzzy-Based Trust Computation Against Black Hole Attacks. In Information and Communication Technology; Springer: Berlin, Germany, 2018; pp. 39–47. [Google Scholar]
- Liang, X.; Lin, X.; Shen, X.S. Enabling trustworthy service evaluation in service-oriented mobile social networks. IEEE Trans. Parallel Distrib. Syst. 2014, 25, 310–320. [Google Scholar] [CrossRef]
- Yu, H.; Shen, Z.; Leung, C.; Miao, C.; Lesser, V.R. A survey of multi-agent trust management systems. IEEE Acc. 2013, 1, 35–50. [Google Scholar]
- Nitti, M.; Girau, R.; Atzori, L. Trustworthiness Management in the Social Internet of Things. IEEE Trans. Knowl. Data Eng. 2014, 26, 1253–1266. [Google Scholar] [CrossRef]
- Wei, Z.; Tang, H.; Yu, F.R.; Wang, M.; Mason, P. Security enhancements for mobile ad hoc networks with trust management using uncertain reasoning. IEEE Trans. Veh. Technol. 2014, 63, 4647–4658. [Google Scholar] [CrossRef]
- Su, Z.; Biennier, F.; Lv, Z.; Peng, Y.; Song, H.; Miao, J. Toward architectural and protocol-level foundation for end-to-end trustworthiness in Cloud/Fog computing. IEEE Trans. Big Data 2017. [Google Scholar] [CrossRef]
- Zhou, J.; Cao, Z.; Dong, X.; Vasilakos, A.V. Security and privacy for cloud-based IoT: Challenges. IEEE Commun. Mag. 2017, 55, 26–33. [Google Scholar] [CrossRef]
- Canetti, R.; Hohenberger, S. Chosen-ciphertext secure proxy re-encryption. In Proceedings of the 14th ACM Conference on Computer and Communications Security, Alexandria, VA, USA, 29 October–2 November 2007; pp. 185–194. [Google Scholar]
- Sahai, A.; Waters, B. Fuzzy identity-based encryption. In Proceedings of the Annual International Conference on the Theory and Applications of Cryptographic Techniques, Aarhus, Denmark, 22–26 May 2005; pp. 457–473. [Google Scholar]
- Goyal, V.; Pandey, O.; Sahai, A.; Waters, B. Attribute-based encryption for fine-grained access control of encrypted data. In Proceedings of the 13th ACM Conference on Computer and Communications Security, Alexandria, VA, USA, 30 October–3 November 2006; pp. 89–98. [Google Scholar]
- Yi, S.; Qin, Z.; Li, Q. Security and privacy issues of fog computing: A survey. In Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, Qufu, China, 10–12 August 2015; pp. 685–695. [Google Scholar]
- Klaedtke, F.; Karame, G.O.; Bifulco, R.; Cui, H. Access control for SDN controllers. In Proceedings of the Third Workshop on Hot Topics in Software Defined Networking, Chicago, IL, USA, 22 August 2014; pp. 219–220. [Google Scholar]
- Aravazhi, A.; Sarathi, P. Secure Routing In Wireless Sensor Networks Via Pomdps. In Proceedings of the IJCAI, Stockholm, Sweden, 13–19 July 2018; pp. 2617–2623. [Google Scholar]
- Sun, B.; Li, D. A Comprehensive Trust-Aware Routing Protocol with Multi-Attributes for WSNs. IEEE Acc. 2018, 6, 4725–4741. [Google Scholar] [CrossRef]
- Stojmenovic, I.; Wen, S. The fog computing paradigm: Scenarios and security issues. In Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS), Warsaw, Poland, 7–10 September 2014; pp. 1–8. [Google Scholar]
- Ghafir, I.; Prenosil, V. Malicious file hash detection and drive-by download attacks. In Proceedings of the Second International Conference on Computer and Communication Technologies; Springer: Berlin, Germany, 2016; pp. 661–669. [Google Scholar]
- Paillier, P. Public-key cryptosystems based on composite degree residuosity classes. In Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques, Prague, Czech Republic, 2–6 May 1999; pp. 223–238. [Google Scholar]
- Boneh, D.; Goh, E.J.; Nissim, K. Evaluating 2-DNF formulas on ciphertexts. In Proceedings of the Theory of Cryptography Conference, Cambridge, MA, USA, 10–12 February 2005; pp. 325–341. [Google Scholar]
- Lu, R.; Heung, K.; Lashkari, A.H.; Ghorbani, A.A. A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Acc. 2017, 5, 3302–3312. [Google Scholar] [CrossRef]
- Rizomiliotis, P.; Gritzalis, S. ORAM based forward privacy preserving dynamic searchable symmetric encryption schemes. In Proceedings of the 2015 ACM Workshop on Cloud Computing Security Workshop, Denver, CO, USA, 16 October 2015; ACM: New York, NY, USA, 2015; pp. 65–76. [Google Scholar]
- Naveed, M.; Prabhakaran, M.; Gunter, C.A. Dynamic searchable encryption via blind storage. In Proceedings of the 2014 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA, 18–21 May 2014; pp. 639–654. [Google Scholar]
- Yang, X.; Yin, F.; Tang, X. A Fine-Grained and Privacy-Preserving Query Scheme for Fog Computing-Enhanced Location-Based Service. Sensors 2017, 17, 1611. [Google Scholar] [CrossRef]
- Boneh, D.; Di Crescenzo, G.; Ostrovsky, R.; Persiano, G. Public key encryption with keyword search. In Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques, Interlaken, Switzerland, 2–6 May 2004; pp. 506–522. [Google Scholar]
- Iovino, V.; Persiano, G. Hidden-vector encryption with groups of prime order. In Proceedings of the International Conference on Pairing-Based Cryptography, Egham, UK, 1–3 September 2008; pp. 75–88. [Google Scholar]
- Czerwinski, S.E.; Zhao, B.Y.; Hodes, T.D.; Joseph, A.D.; Katz, R.H. An architecture for a secure service discovery service. In Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, WA, USA, 15–19 August 1999; pp. 24–35. [Google Scholar]
- Papamanthou, C.; Shi, E.; Tamassia, R. Signatures of correct computation. In Theory of Cryptography; Springer: Berlin, Germany, 2013; pp. 222–242. [Google Scholar]
- Bello-Orgaz, G.; Jung, J.J.; Camacho, D. Social big data: Recent achievements and new challenges. Inf. Fusion 2016, 28, 45–59. [Google Scholar] [CrossRef]
- Gahi, Y.; Guennoun, M.; Mouftah, H.T. Big data analytics: Security and privacy challenges. In Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 27–30 June 2016; pp. 952–957. [Google Scholar]
- Li, L.; Lu, R.; Choo, K.K.R.; Datta, A.; Shao, J. Privacy-preserving-outsourced association rule mining on vertically partitioned databases. IEEE Trans. Inf. Forensics Secur. 2016, 11, 1847–1861. [Google Scholar] [CrossRef]
- Xu, L.; Wu, X.; Zhang, X. CL-PRE: A certificateless proxy re-encryption scheme for secure data sharing with public cloud. In Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security, Seoul, Korea, 2–4 May 2012; pp. 87–88. [Google Scholar]
- Kim, H.I.; Hong, S.; Chang, J.W. Hilbert curve-based cryptographic transformation scheme for spatial query processing on outsourced private data. Data Knowl. Eng. 2016, 104, 32–44. [Google Scholar] [CrossRef]
- Jang, M.; Yoon, M.; Chang, J.W. A privacy-aware query authentication index for database outsourcing. In Proceedings of the 2014 International Conference on Big Data and Smart Computing (BIGCOMP), Bangkok, Thailand, 15–17 January 2014; pp. 72–76. [Google Scholar]
- Matsumoto, T.; Kato, K.; Imai, H. Speeding up secret computations with insecure auxiliary devices. In Proceedings on Advances in Cryptology; Springer: New York, NY, USA, 1990; pp. 497–506. [Google Scholar]
- Cavallo, B.; Di Crescenzo, G.; Kahrobaei, D.; Shpilrain, V. Efficient and secure delegation of group exponentiation to a single server. In Proceedings of the International Workshop on Radio Frequency Identification: Security and Privacy Issues, New York, NY, USA, 23–24 June 2015; pp. 156–173. [Google Scholar]
- Girault, M.; Lefranc, D. Server-aided verification: Theory and practice. In Proceedings of the International Conference on the Theory and Application of Cryptology and Information Security, Chennai, India, 4–8 December 2005; pp. 605–623. [Google Scholar]
- Gennaro, R.; Gentry, C.; Parno, B. Non-interactive verifiable computing: Outsourcing computation to untrusted workers. In Proceedings of the Annual Cryptology Conference, Santa Barbara, CA, USA, 15–19 August 2010; pp. 465–482. [Google Scholar]
- Chung, K.M.; Kalai, Y.; Vadhan, S. Improved delegation of computation using fully homomorphic encryption. In Proceedings of the Annual Cryptology Conference, Santa Barbara, CA, USA, 15–19 August 2010; pp. 483–501. [Google Scholar]
- Parno, B.; Raykova, M.; Vaikuntanathan, V. How to delegate and verify in public: Verifiable computation from attribute-based encryption. In Proceedings of the Theory of Cryptography Conference, Tokyo, Japan, 3–6 March 2012; pp. 422–439. [Google Scholar]
- Khan, M.A.; Salah, K. IoT security: Review, blockchain solutions, and open challenges. Future Gener. Comput. Syst. 2018, 82, 395–411. [Google Scholar] [CrossRef]
- Ghafir, I.; Prenosil, V.; Hammoudeh, M.; Han, L.; Raza, U. Malicious ssl certificate detection: A step towards advanced persistent threat defence. In Proceedings of the International Conference on Future Networks and Distributed Systems, Cambridge, UK, 19–20 July 2017; p. 27. [Google Scholar]
- Walker-Roberts, S.; Hammoudeh, M.; Dehghantanha, A. A Systematic Review of the Availability and Efficacy of Countermeasures to Internal Threats in Healthcare Critical Infrastructure. IEEE Acc. 2018, 6, 25167–25177. [Google Scholar] [CrossRef]
- Diro, A.A.; Chilamkurti, N.; Kumar, N. Lightweight Cybersecurity Schemes Using Elliptic Curve Cryptography In Publish-Subscribe Fog Computing. Mob. Netw. Appl. 2017, 22, 848–858. [Google Scholar] [CrossRef]
- Balfanz, D.; Smetters, D.K.; Stewart, P.; Wong, H.C. Talking to Strangers: Authentication in Ad-Hoc Wireless Networks. In NDSS; Citeseer: University Park, PA, USA, 2002. [Google Scholar]
- McLaughlin, S.; McDaniel, P.; Aiello, W. Protecting consumer privacy from electric load monitoring. In Proceedings of the 18th ACM Conference on Computer and Communications Security, Chicago, IL, USA, 17–21 October 2011; pp. 87–98. [Google Scholar]
- Qin, Z.; Yi, S.; Li, Q.; Zamkov, D. Preserving secondary users’ privacy in cognitive radio networks. In Proceedings of the IEEE INFOCOM 2014—IEEE Conference on Computer Communications, Toronto, ON, USA, 27 April–2 May 2014; pp. 772–780. [Google Scholar]
- Lin, X.; Sun, X.; Wang, X.; Zhang, C.; Ho, P.H.; Shen, X. TSVC: Timed efficient and secure vehicular communications with privacy preserving. IEEE Trans. Wirel. Commun. 2008, 7, 4987–4998. [Google Scholar] [CrossRef]
- Lin, X.; Li, X. Achieving efficient cooperative message authentication in vehicular ad hoc networks. IEEE Trans. Veh. Technol. 2013, 62, 3339–3348. [Google Scholar]
- Zhu, H.; Lin, X.; Lu, R.; Fan, Y.; Shen, X. SMART: A Secure Multilayer Credit-Based Incentive Scheme for Delay-Tolerant Networks. IEEE Trans. Veh. Technol. 2009, 58, 4628–4639. [Google Scholar]
- Sen, J. Privacy preservation technologies in Internet of Things. arXiv preprint 2010, arXiv:1012.2177. [Google Scholar]
- Ghafir, I.; Svoboda, J.; Prenosil, V. Tor-based malware and Tor connection detection. In Proceedings of the International Conference on Frontiers of Communications, Networks and Applications, Kuala Lumpur, Malaysia, 3–5 November 2014; pp. 1–6. [Google Scholar]
- Lu, R.; Lin, X.; Zhu, H.; Shen, X.; Preiss, B. Pi: A practical incentive protocol for delay tolerant networks. IEEE Trans. Wirel. Commun. 2010, 9, 1483–1493. [Google Scholar] [CrossRef]
- Lu, R.; Liang, X.; Li, X.; Lin, X.; Shen, X. Eppa: An efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans. Parallel Distrib. Syst. 2012, 23, 1621–1631. [Google Scholar]
- Dwork, C.; van Tilborg, H.; Jajodia, S. Differential Privacy. In Encyclopedia of Cryptography and Security; Springer: Berlin, Germany, 2011. [Google Scholar]
- Novak, E.; Li, Q. Near-pri: Private, proximity based location sharing. In Proceedings of the IEEE INFOCOM 2014—IEEE Conference on Computer Communications, Toronto, ON, USA, 27 April–2 May 2014; pp. 37–45. [Google Scholar]
- Chu, S.M.; Gong, M.; Li, D.S.; Yan, J.C.; Zhang, W.P. Privacy-Preserving Smart Metering. U.S. Patent App. 15/249,564, 1 March 2018. [Google Scholar]
- Rial, A.; Danezis, G. Privacy-preserving smart metering. In Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, Chicago, IL, USA, 17 October 2011; pp. 49–60. [Google Scholar]
- Wei, W.; Xu, F.; Li, Q. Mobishare: Flexible privacy-preserving location sharing in mobile online social networks. In Proceedings of the 2012 Proceedings IEEE INFOCOM, Orlando, FL, USA, 25–30 March 2012; pp. 2616–2620. [Google Scholar]
- Gao, Z.; Zhu, H.; Liu, Y.; Li, M.; Cao, Z. Location privacy in database-driven cognitive radio networks: Attacks and countermeasures. In Proceedings of the 2013 Proceedings IEEE INFOCOM, Turin, Italy, 14–19 April 2013; pp. 2751–2759. [Google Scholar]
- Khan, Z.A.; Ullrich, J.; Voyiatzis, A.G.; Herrmann, P. A Trust-Based Resilient Routing Mechanism for The Internet of Things. In Proceedings of the 12th International Conference on Availability, Reliability And Security—ARES ’17, Reggio Calabria, Italy, 29 August–1 September 2017. [Google Scholar] [CrossRef]
- Gong, P.; Chen, T.M.; Xu, Q. ETARP: An Energy Efficient Trust-Aware Routing Protocol For Wireless Sensor Networks. J. Sens. 2015, 1–10. [Google Scholar] [CrossRef]
- Outchakoucht, A.; Hamza, E.S.; Leory, J.P. Dynamic access control policy based on blockchain and machine learning for the internet of things. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2017, 8, 417–424. [Google Scholar] [CrossRef]
- Dorri, A.; Kanhere, S.S.; Jurdak, R. Towards an optimized blockchain for IoT. In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, Pittsburgh, PA, USA, 18–21 April 2017; pp. 173–178. [Google Scholar]
- Yue, X.; Wang, H.; Jin, D.; Li, M.; Jiang, W. Healthcare data gateways: Found healthcare intelligence on blockchain with novel privacy risk control. J. Med. Syst. 2016, 40, 218. [Google Scholar] [CrossRef]
- Dorri, A.; Kanhere, S.S.; Jurdak, R.; Gauravaram, P. Blockchain for IoT security and privacy: The case study of a smart home. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Big Island, HI, USA, 13–17 March 2017; pp. 618–623. [Google Scholar]
- Hammi, M.T.; Hammi, B.; Bellot, P.; Serhrouchni, A. Bubbles of Trust: A decentralized Blockchain-based authentication system for IoT. Comput. Secur. 2018, 78, 126–142. [Google Scholar] [CrossRef]
- Javaid, U.; Aman, M.N.; Sikdar, B. BlockPro: Blockchain based Data Provenance and Integrity for Secure IoT Environments. In Proceedings of the 1st Workshop on Blockchain-enabled Networked Sensor Systems, Shenzhen, China, 4–7 November 2018; pp. 13–18. [Google Scholar]
- Kshetri, N. Can blockchain strengthen the internet of things? IT Prof. 2017, 19, 68–72. [Google Scholar] [CrossRef]
- Kouicem, D.E.; Bouabdallah, A.; Lakhlef, H. Internet of things security: A top-down survey. Comput. Netw. 2018, 141, 199–221. [Google Scholar] [CrossRef]
- Conoscenti, M.; Vetro, A.; De Martin, J.C. Blockchain for the Internet of Things: A systematic literature review. In Proceedings of the 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), Agadir, Morocco, 29 November–2 December 2016; pp. 1–6. [Google Scholar]
- Li, X.; Jiang, P.; Chen, T.; Luo, X.; Wen, Q. A survey on the security of blockchain systems. Future Gener. Comput. Syst. 2017. [Google Scholar] [CrossRef]
- Reyna, A.; Martín, C.; Chen, J.; Soler, E.; Díaz, M. On blockchain and its integration with IoT. Challenges and opportunities. Future Gener. Comput. Syst. 2018, 88, 173–190. [Google Scholar] [CrossRef]
- Sharma, P.K.; Singh, S.; Jeong, Y.S.; Park, J.H. Distblocknet: A distributed blockchains-based secure sdn architecture for iot networks. IEEE Commun. Mag. 2017, 55, 78–85. [Google Scholar] [CrossRef]
- Brambilla, G.; Amoretti, M.; Zanichelli, F. Using Blockchain for Peer-to-Peer Proof-of-Location. arXiv 2016, arXiv:1607.00174. [Google Scholar]
- Hardjono, T.; Smith, N. Cloud-based commissioning of constrained devices using permissioned blockchains. In Proceedings of the 2nd ACM International Workshop on IoT Privacy, Trust, and Security, Xi’an, China, 30 May 2016; pp. 29–36. [Google Scholar]
- Nguyen, T.D.; Pham, H.A.; Thai, M.T. Leveraging Blockchain to Enhance Data Privacy in IoT-Based Applications. In Proceedings of the International Conference on Computational Social Networks, Paris, France, 29–30 October 2018; pp. 211–221. [Google Scholar]
- Mendez Mena, D.M.; Yang, B. Blockchain-Based Whitelisting for Consumer IoT Devices and Home Networks. In Proceedings of the 19th Annual SIG Conference on Information Technology Education. International World Wide Web Conferences Steering Committee, Lyon, France, 23–27 April 2018; pp. 7–12. [Google Scholar]
- Available online: https://blogs.cisco.com/innovation/blockchain-and-fog-made-for-each-other (accessed on 19 December 2018).
- Available online: http://www.embedded-computing.com/iot/redesigning-security-for-fog-computing-with-blockchain (accessed on 18 March 2019).
- Mainelli, M. Blockchain will help us prove our identities in a digital world. Harv. Bus. Rev. 2017. [Google Scholar]
- Huh, S.; Cho, S.; Kim, S. Managing IoT devices using blockchain platform. In Proceedings of the 2017 19th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea, 19–22 February 2017; pp. 464–467. [Google Scholar]
- Christidis, K.; Devetsikiotis, M. Blockchains and smart contracts for the internet of things. IEEE Acc. 2016, 4, 2292–2303. [Google Scholar] [CrossRef]
- Li, Z.; Kang, J.; Yu, R.; Ye, D.; Deng, Q.; Zhang, Y. Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans. Ind. Inform. 2018, 14, 3690–3700. [Google Scholar] [CrossRef]
- Sharma, P.K.; Park, J.H. Blockchain based hybrid network architecture for the smart city. Future Gener. Comput. Syst. 2018, 86, 650–655. [Google Scholar] [CrossRef]
- Sharma, P.K.; Moon, S.Y.; Park, J.H. Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City. JIPS 2017, 13, 184–195. [Google Scholar]
- Samaniego, M.; Deters, R. Blockchain as a Service for IoT. In Proceedings of the 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, 15–18 December 2016; pp. 433–436. [Google Scholar]
Threat | Description |
---|---|
Forgery [44] | Fake identities and profiles, fake information to mislead the user. Saturate resource consumption through fake data. That is, in E-Health and home automation systems, one can easily fake their identifications and profiles to generate any attack. |
Tampering [45] | Degrading the efficiency of fog by dropping/delaying transmitting data. That is, energy conservation systems are responsible to collect the data related to electricity supply, consumption patterns, smart metering, pricing and other details. As the data are very critical, dropping or delaying the data may cause problems. |
Spamming [46] | Spreading redundant information which causes to consume resources unnecessarily. The attack generated on smart cities lies in this domain. |
Sybil [47] | Legitimate user personal information and manipulation of fake identities to take over the illegal control on fog resources. That is, in smart home and smart cities, legitimate user can manipulate the fake identities to take control of the network. |
Jamming [48] | Jam communication network by spreading burst if dummy data on the network. Any type of smart environment can be attacked by Jamming. |
Eavesdropping [49] | Capturing of transmitting packets and try to read the contents. Any type of smart environment can be a victim of these attacks. |
DoS [50] | Flooding of superfluous requests to fog nodes to disrupt the services for users. The data generated by smart cities and smart agriculture can be a victim of DoS and flooding attacks. |
Collusion | Acquiring unfair advantage through deceiving, misleading and defrauding legal entities by collusion of two or more parties. |
Man-In-The-Middle [50] | Involving between two parties and manipulate exchanged data between them. E-Health and Smart cities are the best fit examples. |
Impersonation [51] | Pretending the fake services as fog services to the users. |
Identity Privacy [52] | User personal information leakage such as phone number, visa number, etc. on a communication channel. |
Data Privacy [52] | Exposure of user data to unreliable parties considerably reaches to privacy leakage. Smart homes, smart cities and E-Health systems are commonly known victim of these types of attacks. |
Usage Privacy [52] | Leakage of services utilization pattern of users. |
Location Privacy [52] | Capturing user’s location information to expose or observe user moments. Smart homes, smart cities and E-Health systems are commonly known victims of these types of attacks. |
Attacks | Description |
---|---|
Wormhole [53] | Initial attacked node forms a path by colluding with other nodes to transfer malicious packets. The path formed among conspiring nodes is called wormhole. |
Blackhole [54] | A malicious node intervenes in route discovery to be a part of path. It then drops the packets instead of forwarding them. Some blackholes attack the received packets before forwarding. |
Greyhole [54] | A modified version of blackhole attacks. Data are dropped by the attacking node, but it tells the router that data are transmitted. This attack is difficult to detect by the router as it shows end-to-end connectivity. |
Selective forwarding [55] | Selective data packets that are required to be transmitted are dropped by nodes resulting in network performance degradation. |
Local repair [46] | Destabilizing the network and draining neighbor nodes battery by sending false link repair messages. It reduces packet delivery increases end-to-end delays. |
Route cache poisoning [56] | It involves the alteration of route tables by malicious nodes to poison route caches to other nodes. |
Sybil [47] | Assumptions of nodes to have multiple identities over the network to create confusion and disruption, which opens the opportunity for malicious nodes to operate. |
Sinkhole [57] | Malicious node pretends to be the optimal route to the destination node by sending false messages to the initiator node, thus after receiving traffic, it alters the routing and other data to complicate the topological structure of the network. |
Hello flood [58] | The attacker node broadcasts links to other nodes. The unsuspecting nodes accept that link and consider the attacker node to be the neighbor node. The unsuspecting node start sending packets that are actually wasted as the adversary node is far away and not the neighbor. This creates a routing loop within the network. |
Neighbor [54] | The neighbor node considers the attacking node (while broadcasting DIO messages with no DIO details) as a newly joined node, which could be a parent node, but this node is out of reach when the neighbor node tries to select it as a parent node. |
Version number [54] | The attacker node alters its version number in DIO messages and broadcasts to neighbor nodes. This results in routing loops in the network, which disrupt the network topology and deplete nodes energy resources. |
Modification [59] | Malicious nodes take advantage of no trust levels being measured in the ad-hoc networks to engage in discovering, altering and disrupting the routing in the network. This attack causes traffic redirection and DoS attacks by modifying the protocol messages. |
Fabrication [60] | Creates forged routing information using routing table overflow attacks, resource consumption and fake route error messages. |
Byzantine [61] | Aims to decline the network services; the attacker node selectively drops route packets, which create routing loops and send forward those route packets through non-optimal paths. |
Location spoofing [61] | Pretends to be the nearest destined node to disrupt normal network protocol operations. |
Challenges | Solution | Limitations |
---|---|---|
Identity Verification |
|
|
Access Control |
|
|
Lightweight protocol design |
|
|
Intrusion Detection |
|
|
Trust Management |
|
|
Privacy-conserving packet forwarding |
|
|
Rogue fog node detection |
|
|
Challenges | Solution | Limitations |
---|---|---|
Data identification, aggregation and integrity |
|
|
Secure data distribution |
|
|
Secure content distribution |
|
|
Secure big data analysis |
|
|
Secure computation |
|
|
Verifiable computation |
|
|
Challenges | Solution | Limitations |
---|---|---|
Confidentiality |
|
|
Light-weight trust management |
|
|
Description | Advantages |
---|---|
A distributed IoT network architecture consisting of an SDN base network using the blockchains technique [159] |
|
An efficient decentralized authentication mechanism based on the public blockchain, Ethereum to create secured virtual zones for secure communication [152] |
|
A lightweight BC-based hierarchical architecture for IoT that uses a centralized private Immutable Ledger and a distributed trust to reduce the block validation processing time [149] |
|
A decentralized network model based on blockchain approach for data preserving, data integrity and blocking of unregistered devices using Physical Unclonable Functions (PUFs) and Ethereum [153] |
|
A blockchain-based decentralized, infrastructure-independent proof-of-location technique for location trustworthiness and user privacy preservation [160] |
|
A cloud-based blockchain solution for identifying IoT devices manufacturing provenance while enforcing users privacy preservation using EPID (Enhanced Privacy Identity protocol) of Intel to incentivize IoT devices for data sharing [161] |
|
A blockchain-based scheme called Healthcare Data Gateway (HGD) architecture to enable patient to own, control and share their own data easily and securely without violating privacy [150] |
|
A blockchain-based security and privacy scheme for smart homes [151] |
|
A blockchain solution for preserving data privacy in Internet of Things using smart contracts along with a firmware scheme using blockchain for prevention of fraudulent data [162] |
|
A blockchain-based proof of concept for securing consumer/home-based IoT devices and the networks by using Ethereum [163] |
|
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Share and Cite
Tariq, N.; Asim, M.; Al-Obeidat, F.; Zubair Farooqi, M.; Baker, T.; Hammoudeh, M.; Ghafir, I. The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey. Sensors 2019, 19, 1788. https://doi.org/10.3390/s19081788
Tariq N, Asim M, Al-Obeidat F, Zubair Farooqi M, Baker T, Hammoudeh M, Ghafir I. The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey. Sensors. 2019; 19(8):1788. https://doi.org/10.3390/s19081788
Chicago/Turabian StyleTariq, Noshina, Muhammad Asim, Feras Al-Obeidat, Muhammad Zubair Farooqi, Thar Baker, Mohammad Hammoudeh, and Ibrahim Ghafir. 2019. "The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey" Sensors 19, no. 8: 1788. https://doi.org/10.3390/s19081788
APA StyleTariq, N., Asim, M., Al-Obeidat, F., Zubair Farooqi, M., Baker, T., Hammoudeh, M., & Ghafir, I. (2019). The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey. Sensors, 19(8), 1788. https://doi.org/10.3390/s19081788