Analysis of Edge Intelligent Frameworks and their Security Issues

Authors

  • Muhammad Waleed Department of Electronic Systems, Aalborg University Copenhagen, Denmark
  • Sokol Kosta Department of Electronic Systems, Aalborg University Copenhagen, Denmark
  • Knud Erik Skouby Department of Electronic Systems, Aalborg University Copenhagen, Denmark

DOI:

https://doi.org/10.13052/jmm1550-4646.1916

Keywords:

Wireless devices, IoT, edge intelligence, security and privacy

Abstract

Edge Intelligence has become increasingly popular and has already made its place to increase the overall system performance by reducing the burden of the cloud and the network. In edge intelligent frameworks, a massive amount of data generated are not provided to the central cloud, and data analysis is carried out at the edge. Edge intelligence IoT environments comprise heterogeneous devices that communicate over the network, making it essential to protect the data and users’ information. Through these edge frameworks, numerous users and devices take part in communication where the exchange of sensitive data occurs. Therefore, security in such frameworks is crucial and a key challenge for reliable communication. This paper performs an analysis of popular AI/ML applications toward edge intelligence focusing on highlighting the critical security and privacy concerns desired in such systems. After a thorough investigation, we show that although several promising edge intelligent frameworks have been developed to address energy and performance issues, they do not consider the security and privacy of the data as the researchers are more focused on the performance predicaments.

Downloads

Download data is not yet available.

Author Biographies

Muhammad Waleed, Department of Electronic Systems, Aalborg University Copenhagen, Denmark

Muhammad Waleed received the B.Sc. and M.Sc. degrees from the University of Engineering and Technology (UET) at Peshawar, Pakistan, in 2015 and 2017, respectively. He then joined the Trust Data Analytics and Management Lab as a Researcher in the Department of Information and Communication Engineering, Chosun University, South Korea. Further, he is currently working as a PhD fellow in the IoTalentum program under the Marie Skłodowska-Curie Actions (MCSA) fellowship in the Department of Electronic Systems at Aalborg University Copenhagen (AAU), Denmark. His research interests include cyber security, Internet of Things, machine learning, trust management, and network communication. He is also interested in future networks, particularly edge computing and mobile communication.

Sokol Kosta, Department of Electronic Systems, Aalborg University Copenhagen, Denmark

Sokol Kosta holds a BSc, MSc, and PhD in Computer Science from Sapienza University of Rome, Italy. He was a postdoctoral researcher with Sapienza University and a visiting researcher with HKUST in 2015. He is currently associate professor at the Department of Electronic Systems at Aalborg University Copenhagen. He has published in several top conferences and journals including IEEE Infocom, IEEE Communications Magazine, and IEEE Transactions on Mobile Computing. His research interests include networking, distributed systems, and mobile cloud computing.

Knud Erik Skouby, Department of Electronic Systems, Aalborg University Copenhagen, Denmark

Knud Erik Skouby is professor emeritus, Aalborg University. Founding director of the center for Communication, Media and Information Technologies, Aalborg University-Copenhagen (2007–17) – a center providing a focal point for multi-disciplinary research and training in applications of CMI. Has a career as a university teacher and within consultancy since 1972; focus on ICT since 1987. Working areas: Techno-economic Analyses; Development of mobile/wireless applications and services: Regulation of telecommunications. Project manager and partner in a number of international, European and Danish research projects. He has served on a number of public committees within telecom, IT and broadcasting. Further served as a member of boards of professional societies; as a member of organizing boards, evaluation committees, and invited speaker on international conferences; published a number of Danish and international articles, books, and conference proceedings. Member of EUs Economic and Social Council 1994–98. Past dep. Chair IEEE Denmark. Editor in chief of Nordic and Baltic Journal of Information and Communication Technologies (NBICT); Chair of WGA in Wireless World Research Forum.

References

Yu, Wei and Liang, Fan and He, Xiaofei and Hatcher, William Grant and Lu, Chao and Lin, Jie and Yang, Xinyu. A survey on the edge computing for the Internet of Things. IEEE access, (6):6900–6919, (2017), 10.1109/ACCESS.2017.2778504.

Sodhro, Ali Hassan and Pirbhulal, Sandeep and de Albuquerque, Victor Hugo C. Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications. IEEE Transactions on Industrial Informatics, 15(7):4235-4243, (2019), 10.1109/TII.2019.2902878.

Xu, Dianlei and Li, Tong and Li, Yong and Su, Xiang and Tarkoma, Sasu and Jiang, Tao and Crowcroft, Jon and Hui, Pan. Edge Intelligence: Empowering Intelligence to the Edge of Network. Proceedings of the IEEE, 109(11):1778-1837, (2019), 10.1109/JPROC.2021.3119950.

ETSI, MECISG. Multi-access edge computing (MEC) framework and reference architecture. ETSI GS MEC 3 V2. Proceedings of the IEEE, (2019).

Kennedy, Jason and Varghese, Blesson and Reaño, Carlos. AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning. arXiv, (2021), 10.48550/ARXIV.2103.04930.

M. Satyanarayanan. The Emergence of Edge Computing. Computer, 50(1):30-39, (Jan. 2017), 10.1109/MC.2017.9.

Oseni, Ayodeji, Nour Moustafa, Helge Janicke, Peng Liu, Zahir Tari, and Athanasios Vasilakos. Security and Privacy for Artificial Intelligence: Opportunities and Challenges. arXiv preprint arXiv:2102.04661, (2021), 10.48550/ARXIV.2102.04661.

Sayed, Aya, Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, and Abbes Amira. Intelligent edge-based recommender system for internet of energy applications. IEEE Systems Journal, 1–10, (2021), 10.1109/jsyst.2021.3124793.

Han, Tao, Khan Muhammad, Tanveer Hussain, Jaime Lloret, and Sung Wook Baik. An efficient deep learning framework for intelligent energy management in IoT networks. IEEE Internet of Things Journal, 8(5): 3170–3179, (2020), 10.1109/JIOT.2020.3013306.

Liu, Yi, Chao Yang, Li Jiang, Shengli Xie, and Yan Zhang. Intelligent edge computing for IoT-based energy management in smart cities. IEEE network, 33(2): 111–117, (2019), 10.1109/MNET.2019.1800254.

Hossain, Md Mahmud, Maziar Fotouhi, and Ragib Hasan. Towards an analysis of security issues, challenges, and open problems in the internet of things. IEEE World Congress on Services, 21–28, (27 June-2 July 2015) New York, NY, USA, 10.1109/SERVICES.2015.12.

Frustaci, Mario, Pasquale Pace, Gianluca Aloi, and Giancarlo Fortino. Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet of Things, 5(4): 2483–2495, (2017), 10.1109/JIOT.2017.2767291.

Ari, Ado Adamou Abba, Olga Kengni Ngangmo, Chafiq Titouna, Ousmane Thiare, Alidou Mohamadou, and Abdelhak Mourad Gueroui. Enabling privacy and security in Cloud of Things: Architecture, applications security and privacy challenges. Applied Computing and Informatics, (2020), 10.1016/j.aci.2019.11.005.

Sun, PanJ un. Security and privacy protection in cloud computing: Discussions and challenges. Journal of Network and Computer Applications, 160, (2020), 102642, 10.1016/j.jnca.2020.102642.

Cao, Keyan, Yefan Liu, Gongjie Meng, and Qimeng Sun. An overview on edge computing research. IEEE access, 8: 85714–85728, (2020), 10.1109/ACCESS.2020.2991734.

Patel, Chintan and Doshi, Nishant. A Novel MQTT Security framework In Generic IoT Model. Procedia Computer Science 171, (2020), 10.1016/j.procs.2020.04.150.

Roopak, Monika, Gui Yun Tian, and Jonathon Chambers. Deep learning models for cyber security in IoT networks. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA, 0452-0457, (7–9 Jan. 2019), 10.1109/CCWC.2019.8666588.

Liu, Caiming, Yan Zhang, and Huaqiang Zhang. A novel approach to IoT security based on immunology. In 9th

International Conference on Computational Intelligence and Security, Emeishan, China, 771–775, (14-15 December 2013), 10.1109/CIS.2013.168.

Cui, Qimei, Zengbao Zhu, Wei Ni, Xiaofeng Tao, and Ping Zhang. Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems. IEEE Wireless Communications, 28(2): 78–85, (2021), 10.1109/MWC.001.2000325.

Ghazal, Taher M. Internet of Things with Artificial Intelligence for Health Care Security. Arabian Journal for Science and Engineering, 1–12, (2021), 10.1007/s13369-021-06083-8.

Neshenko, Nataliia, Elias Bou-Harb, Jorge Crichigno, Georges Kaddoum, and Nasir Ghani. Demystifying IoT security: An exhaustive survey on IoT vulnerabilities and a first empirical look on Internet-scale IoT exploitations. IEEE Communications Surveys and Tutorials, 21(3): 2702–2733, (2019), 10.1109/COMST.2019.2910750.

Tavallaee, Mahbod, Natalia Stakhanova, and Ali Akbar Ghorbani. Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(5): 516–524, (2010), 10.1109/TSMCC.2010.2048428.

Mahmoud, Rwan, Tasneem Yousuf, Fadi Aloul, and Imran Zualkernan. Internet of things (IoT) security: Current status, challenges and prospective measures. In 2015 10th international conference for internet technology and secured transactions (ICITST), London, UK, 336-341, (14–16 December 2015), 10.1109/ICITST.2015.7412116.

Singh, Jatinder, Christopher Millard, Chris Reed, Jennifer Cobbe, and Jon Crowcroft. Accountability in the IoT: Systems, law, and ways forward. Computer, 51(7): 54–65, (2018), 10.1109/MC.2018.3011052.

Zhao Ziming, Liu Fang, Cai Zhiping, Xiao Nong. Edge Computing: Platforms, Applications and Challenges. Journal of Computer Research and Development, 55(2): 327–337, (2018), 10.7544/issn1000-1239.2018.20170228.

Xuehai Hong, Yang Wang. Edge Computing Technology: Development and Countermeasures. Strategic Study of Chinese Academy of Engineering, 20(2): 20–26, (2018), 10.15302/J-SSCAE-2018.02.004.

Zhen, Penghua and Han, Yubing and Dong, Anming and Yu, Jiguo. CareEdge: A Lightweight Edge Intelligence Framework for ECG-Based Heartbeat Detection. Procedia Computer Science., 187, 329–334, (2021), 10.1016/j.procs.2021.04.070.

Gupta, Vishakha, Gavrilovska, Ada, Schwan, Karsten, Kharche, Harshvardhan, Tolia, Niraj, Talwar, Vanish and Ranganathan, Parthasarathy. GViM: GPU-Accelerated Virtual Machines. In Proceedings of the 3rd ACM Workshop on System-Level Virtualization for High Performance Computing, 17–24, (2009), 10.1145/1519138.1519141.

M. Oikawa et al. DS-CUDA: A Middleware to Use Many GPUs in the Cloud Environment. In SC Companion: High Performance Computing, Networking Storage and Analysis, Salt Lake City, UT, USA., 1207–1214, (10–16 November 2012), 10.1109/SC.Companion.2012.146.

T. Liang et al. GridCuda: A Grid-Enabled CUDA Programming Toolkit. IEEE Workshops of International Conference on Advanced Information Networking and Applications, Biopolis, Singapore., 141–146, (22–25 March 2011), 10.1109/WAINA.2011.82.

G. Giunta et al. A GPGPU Transparent Virtualization Component for High Performance Computing Clouds. In Euro-Par 2010 – Parallel Processing, Springer, Berlin, Heidelberg., 627, 379–391, (2010), 10.1007/978-3-642-15277-1-37.

Montella, Raffaele, Carmine Ferraro, Sokol Kosta, Valentina Pelliccia, and Giulio Giunta. Enabling android-based devices to high-end gpgpus. In International Conference on Algorithms and Architectures for Parallel Processing, 10048, 118–125. Springer, Cham, (2016), 10.1007/978-3-319-49583-5-9.

Y. Chen, S. Biookaghazadeh, and M. Zhao. Exploring the capabilities of mobile devices supporting deep learning. In Proc. 27th Int. Symp. High-Perform. Parallel Distrib. Comput., (Jun. 2018), pp. 17–18, 10.1145/3318216.3363316.

D. Li, T. Salonidis, N. V. Desai, and M. C. Chuah. DeepCham: Collaborative edge-mediated adaptive deep learning for mobile object recognition. In Proc. IEEE/ACM Symp. Edge Comput. (SEC), Washington, DC, USA., (27–28 October 2016), 64–76, 10.1109/SEC.2016.38.

T. Xing, S. S. Sandha, B. Balaji, S. Chakraborty, and M. Srivastava. Enabling edge devices that learn from each other: Cross modal training for activity recognition. In Proc. 1st Int. Workshop Edge Syst., Anal. Netw., (Jun. 2018), 37–42, 10.1145/3213344.3213351.

O. Valery, P. Liu, and J.-J. Wu. CPU/GPU collaboration techniques for transfer learning on mobile devices. In Proc. IEEE 23rd Int. Conf. Parallel Distrib. Syst. (ICPADS), Shenzhen, China., (Dec. 2017), pp. 477–484, 10.1109/ICPADS.2017.00069.

F. Shahmohammadi, A. Hosseini, C. E. King, and M. Sarrafzadeh. Smartwatch based activity recognition using active learning. In Proc. IEEE/ACM Int. Conf. Connected Health, Appl., Syst. Eng. Technol. (CHASE), (Jul. 2017), pp. 321–329, 10.1109/CHASE.2017.115.

S. Flutura et al. DrinkWatch: A mobile wellbeing application based on interactive and cooperative machine learning. In Proc. Int. Conf. Digit. Health, (Apr. 2018), pp. 65–74, doi.org/10.1145/3194658.3194666.

Y. Huang et al. Task scheduling with optimized transmission time in collaborative cloud-edge learning. In Proc. 27th

Int. Conf. Comput. Commun. Netw. (ICCCN), 30 July-2 Aug, Hangzhou, China (Jul. 2018), pp. 1–9, 10.1109/ICCCN.2018.8487352.

O. Valery, P. Liu, and J.-J. Wu. Low precision deep learning training on mobile heterogeneous platform. In Proc. 26th Euromicro Int. Conf. Parallel, Distrib. Netw.-Based Process. (PDP), Cambridge, UK, (21–23 March 2018), 10.1109/PDP2018.2018.00023.

G. W. Burr, S. Ambrogio, P. Narayanan, H. Tsai, C. Mackin, and A. Chen. Accelerating deep neural networks with analog memory devices In Proc. China Semiconductor Technol. Int. Conf. (CSTIC), Shanghai, China (Mar. 2019), pp. 149–152, 10.1109/CSTIC.2019.8755642.

V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar. Federated multi-task learning. in Proc. Adv. Neural Inf. Process. Syst., (2017), pp. 4424–4434, 10.48550/ARXIV.1705.1046.

Zhao Ziming, Liu Fang, Cai Zhiping, Xiao Nong. Edge Computing: Platforms, Applications and Challenges. Journal of Computer Research and Development., (2018), 55(2): 327–337, 10.7544/issn1000-1239.2018.20170228.

Xuehai Hong, Yang Wang. Edge Computing Technology: Development and Countermeasures. Strategic Study of Chinese Academy of Engineering. (2018), 20(2): 20–26, 10.15302/J-SSCAE-2018.02.004.

J. Mandebi Mbongue et al. FPGA Virtualization in Cloud-Based Infrastructures Over Virtio. in IEEE 36th International Conference on Computer Design, Orlando, FL, USA. (7–10 Oct. 2018), pp. 242–245, 10.1109/ICCD.2018.00044.

Theodorou, Sophocles, and Nicolas Sklavos. Blockchain-based security and privacy in smart cities. In Smart Cities Cybersecurity and Privacy. pp. 21–37. Elsevier,(2019). 10.1016/B978-0-12-815032-0.00003-2.

Anwar, Raja Waseem, Anazida Zainal, Fatma Outay, Ansar Yasar, and Saleem Iqbal. BTEM: Belief based trust evaluation mechanism for Wireless Sensor Networks. Future Generation Computer Systems. 96 (2019): 605–616, 10.1016/j.future.2019.02.004.

Qureshi, Kashif Naseer, Muhammad Moghees Idrees, Jaime Lloret, and Ignacio Bosch. Self-assessment based clustering data dissemination for sparse and dense traffic conditions for internet of vehicles. IEEE Access. 8 (2020): 10363–10372, 10.1109/ACCESS.2020.2964530.

J. Wang, J. Zhang, W. Bao, X. Zhu, B. Cao, and P. S. Yu. Not just privacy: Improving performance of private deep learning in mobile cloud. in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, (2018), pp. 2407–2416, 10.48550/arXiv.1809.03428.

Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang. A secure federated transfer learning framework. IEEE Intell. Syst., vol. 35, no. 4, pp. 70–82, (Jul./Aug. 2020), 10.1109/MIS.2020.2988525.

K. Wei et al. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans. Inf. Forensics Security., vol. 15, pp. 3454–3469, (2020), 10.1109/TIFS.2020.2988575.

K. Cheng et al. SecureBoost: A lossless federated learning framework. arXiv:1901.08755. [Online]., 10.48550/arXiv.1901.08755.

N. Akhtar and A. Mian. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access,, vol. 6, pp. 14410–14430, (2018), 10.48550/arXiv.1801.00553.

Downloads

Published

2022-09-15

How to Cite

Waleed, M. ., Kosta, S. ., & Skouby, K. E. . (2022). Analysis of Edge Intelligent Frameworks and their Security Issues. Journal of Mobile Multimedia, 19(01), 117–134. https://doi.org/10.13052/jmm1550-4646.1916

Issue

2023: Vol 19 Iss 1

Section

WWRF

Most read articles by the same author(s)