Abstract
Zero trust architecture (ZTA) is a paradigm shift in how we protect data, stay connected and access resources. ZTA is non-perimeter-based defence, which has been emerging as a promising revolution in the cyber security field. It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system. However, ZTA automation and orchestration, towards seamless deployment on real-world networks, has been limited to be reviewed in the existing literature. In this paper, we first identify the bottlenecks, discuss the background of ZTA and compare it with traditional perimeter-based security architectures. More importantly, we provide an in-depth analysis of state-of-the-art AI techniques that have the potential in the automation and orchestration of ZTA. Overall, in this review paper, we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.
Article PDF
Similar content being viewed by others
References
M. Campbell. Beyond zero trust: Trust is a vulnerability. Computer, vol.53, no. 10, pp. 110–113, 2020. DOI: https://doi.org/10.1109/MC.2020.3011081.
S. Rose, O. Borchert, S. Mitchell, S. Connelly. Zero trust architecture. Gaithersburg, USA: NIST Special Publication 800-207, 2020. DOI: https://doi.org/10.6028/NIST.SP.800-207.
A. A. Barakabitze, A. Ahmad, R. Mijumbi, A. Hines. 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, vol.167, Article number 106984, 2020. DOI: https://doi.org/10.1016/j.comnet.2019.106984.
P. J. Sun. Security and privacy protection in cloud computing: Discussions and challenges. Journal of Network and Computer Applications, vol. 160, Article number 102642, 2020. DOI: https://doi.org/10.1016/j.jnca.2020.102642.
D. A. E. Haddon. 9 — Zero trust networks, the concepts, the strategies, and the reality. Strategy, Leadership, and AI in the Cyber Ecosystem, H. Jahankhani, L. M. O’Dell, G. Bowen, D. Hagan, A. Jamal, Eds., Amsterdam, The Netherlands: Academic Press, pp. 195–216, 2021. DOI: https://doi.org/10.1016/B978-0-12-821442-8.00001-X.
D. Nicholson. Blurring the boundaries between networking and it security. Network Security, vol.2018, no. 1, pp. 11–13, 2018. DOI: https://doi.org/10.1016/S1353-4858(18)30007-2.
A. Kerman, O. Borchert, S. Rose, A. Tan. Implementing a zero trust architecture. National Institute of Standards and Technology (NIST) special publication 1800-35E, 2020.
Z. Zaheer, H. Chang, S. Mukherjee, J. Van Der Merwe. eZTrust: Network-independent zero-trust perimeterization for microservices. In Proceedings of the ACM Symposium on SDN Research, San Jose, USA, pp. 49–61, 2019. DOI: https://doi.org/10.1145/3314148.3314349.
Z. A. Collier, J. Sarkis. The zero trust supply chain: Managing supply chain risk in the absence of trust. International Journal of Production Research, vol.59, no. 11, pp. 3430–3445, 2021. DOI: https://doi.org/10.1080/00207543.2021.1884311.
A. Wylde. Zero trust: Never trust, always verify. In Proceedings of International Conference on Cyber Situational Awareness, Data Analytics and Assessment, Dublin, Ireland, 2021. DOI: https://doi.org/10.1109/CyberSA52016.2021.9478244.
C. Katsis, F. Cicala, D. Thomsen, N. Ringo, E. Bertino. Can I reach you? Do I need to? New semantics in security policy specification and testing. In Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, pp. 165–174, 2021. DOI: https://doi.org/10.1145/3450569.3463558.
X. S. Yan, H. J. Wang. Survey on zero-trust network security. In Proceedings of the 6th International Conference on Artificial Intelligence and Security, Hohhot, China, pp. 50–60, 2020. DOI: https://doi.org/10.1007/978-981-15-8083-3_5.
J. Kinyua, L. Awuah. AI/ML in security orchestration, automation and response: Future research directions. Intelligent Automation & Soft Computing, vol.285, no. 2, pp. 527–545, 2020. DOI: https://doi.org/10.32604/iasc.2021.016240.
E. Bertino, K. Brancik. Services for zero trust architectures-a research roadmap. In Proceedings of IEEE International Conference on Web Services, Chicago, USA, pp. 14–20, 2021. DOI: https://doi.org/10.1109/ICWS53863.2021.00016.
J. Kindervag. Build security into your network’s DNA: The zero trust network architecture, Technical Report 27, Forrester Research Inc., USA, 2010.
J. Kindervag, S. Balaouras. No more chewy centers: Introducing the zero trust model of information security. Forrester Research, vol. 3, 2010.
C. Cunningham. The Zero Trust Extended (ZTX) Ecosystem, Combridge, UK: Forrester Research, Inc., 2018.
R. Ward, B. Beyer. BeyondCorp: A new approach to enterprise security. Usenix Login, vol.39, no.6, pp.6–11, 2014.
B. Osborn, J. McWilliams, B. Beyer, M. Saltonstall. BeyondCorp: Design to deployment at Google. Login, vol.41, no. 1, pp. 28–34, 2016.
L. Cittadini, B. Spear, B. Beyer, M. Saltonstall. BeyondCorp: The access proxy. Security, vol.41, no.4, pp. 28–33, 2016.
J. Peck, B. Beyer, C. Beske, M. Saltonstall. Migrating to BeyondCorp: Maintaining productivity while improving security. Summer, vol.42, no. 2, pp.49–55, 2017.
V. M. Escobedo, F. Zyzniewski, B. A. E. Beyer, M. Saltonstall. BeyondCorp: The user experience. Login, vol. 42, no. 3, pp. 38–43, 2017.
M. Janosko, H. King, B. A. E. Beyer, M. Saltonstall. Beyondcorp 6: Building a healthy fleet. Login, vol.43, no.3, pp. 26–64, 2018.
S. Teerakanok, T. Uehara, A. Inomata. Migrating to zero trust architecture: Reviews and challenges. Security and Communication Networks, vol.2021, Article number 9947347, 2021. DOI: https://doi.org/10.1155/2021/9947347.
C. Buck, C. Olenberger, A. Schweizer, F. Völter, T. Eymann. Never trust, always verify: A multivocal literature review on current knowledge and research gaps of zerotrust. Computers & Security, vol.110, Article number 102436, 2021. DOI: https://doi.org/10.1016/j.cose.2021.102436.
L. Alevizos, V. T. Ta, M. H. Eiza. Augmenting zero trust architecture to endpoints using blockchain: A state-of-the-art review. Security and Privacy, vol.5, no. 1, Article number e191, 2022. DOI: https://doi.org/10.1002/spy2.191.
N. F. Syed, S. W. Shah, A. Shaghaghi, A. Anwar, Z. Baig, R. Doss. Zero trust architecture (ZTA): A comprehensive survey. IEEE Access, vol.10, pp. 57143–57179, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3174679.
Y. H. He, D. C. Huang, L. Chen, Y. Ni, X. J. Ma. A survey on zero trust architecture: Challenges and future trends. Wireless Communications and Mobile Computing, vol.2022, Article number 6476274, 2022. DOI: https://doi.org/10.1155/2022/6476274.
J. M. Pittman, S. Alaee, C. Crosby, T. Honey, G. M. Schaefer. Towards a model for zero trust data. American Journal of Science & Engineering, vol.3, no. 1, pp. 18–24, 2022. DOI: https://doi.org/10.15864/ajse.3103.
S. Sarkar, G. Choudhary, S. K. Shandilya, A. Hussain, H. Kim. Security of zero trust networks in cloud computing: A comparative review. Sustainability, vol. 14, no. 18, Article number 11213, 2022. DOI: https://doi.org/10.3390/sul41811213.
E. Gilman, D. Barth. Zero Trust Networks: Building Secure Systems in Untrusted Networks, O’Reilly Media, Inc., 2017.
J. Garbis, J. W. Chapman. Zero Trust Security: An Enterprise Guide, Berkeley, USA: Apress, 2021.
S. Dhar, I. Bose. Securing IoT devices using zero trust and blockchain. Journal of Organizational Computing and Electronic Commerce, vol.31, no. 1, pp. 18–34, 2021. DOI: https://doi.org/10.1080/10919392.2020.1831870.
L. F. Huang. The firewall technology study of network perimeter security. In Proceedings of IEEE Asia-Pacific Services Computing Conference, Guilin, China, pp. 410–413, 2012. DOI: https://doi.org/10.1109/APSCC.2012.23.
S. Splaine. Testing Web Security: Assessing the Security of Web Sites and Applications, Indianapolis, USA: Wiley, 2002.
K. Dadheech, A. Choudhary, G. Bhatia. De-militarized zone: A next level to network security. In Proceedings of Second International Conference on Inventive Communication and Computational Technologies, Coimbatore, India, pp. 595–600, 2018. DOI: https://doi.org/10.1109/ICICCT.2018.8473328.
E. S. Hosney, I. T. A. Halim, A. H. Yousef. An artificial intelligence approach for deploying zero trust architecture (ZTA). In Proceedings of 5th International Conference on Computing and Informatics, New Cairo, Egypt, pp. 343–350, 2022. DOI: https://doi.org/10.1109/ICCI54321.2022.9756117.
M. Saleem, M. R. Warsi, S. Islam. Secure information processing for multimedia forensics using zero-trust security model for large scale data analytics in SaaS cloud computing environment. Journal of Information Security and Applications, vol.72, Article number 103389, 2023. DOI: https://doi.org/10.1016/j.jisa.2022.103389.
J. W. Wang, X. Y. Jing, Z. Yan, Y. L. Fu, W. Pedrycz, L. T. Yang. A survey on trust evaluation based on machine learning. ACM Computing Surveys, vol.53, no.5, Article number 107, 2020. DOI: https://doi.org/10.1145/3408292.
H. Lin, S. Garg, J. Hu, X. D. Wang, J. Piran, M. S. Hossain. Data fusion and transfer learning empowered granular trust evaluation for internet of things. Information Fusion, vol.78, pp. 149–157, 2022. DOI: https://doi.org/10.1016/j.inffus.2021.09.001.
N. C. Luong, D. T. Hoang, S. M. Gong, D. Niyato, P. Wang, Y. C. Liang, D. I. Kim. Applications of deep reinforcement learning in communications and networking: A survey. IEEE Communications Surveys & Tutorials, vol.21, no.4, pp.3133–3174, 2019. DOI: https://doi.org/10.1109/COMST.2019.2916583.
R. S. Sandhu, P. Samarati. Access control: Principle and practice. IEEE Communications Magazine, vol.32, no.9, pp. 40–48, 1994. DOI: https://doi.org/10.1109/35.312842.
S. Ravidas, A. Lekidis, F. Paci, N. Zannone. Access control in internet-of-things: A survey. Journal of Network and Computer Applications, vol.144, pp. 79–101, 2019. DOI: https://doi.org/10.1016/j.jnca.2019.06.017.
A. Ouaddah, H. Mousannif, A. A. Elkalam, A. A. Ouahman. Access control in the internet of things: Big challenges and new opportunities. Computer Networks, vol.112, pp. 237–262, 2017. DOI: https://doi.org/10.1016/j.comnet.2016.11.007.
Y. H. Zhang, R. H. Deng, S. M. Xu, J. F. Sun, Q. Li, D. Zheng. Attribute-based encryption for cloud computing access control: A survey. ACM Computing Surveys, vol.53, no.4, Article number 83, 2020. DOI: https://doi.org/10.1145/3398036.
L. Zhou, C. H. Su, Z. Li, Z. Liu, G. P. Hancke. Automatic fine-grained access control in SCADA by machine learning. Future Generation Computer Systems, vol.93, pp. 548–559, 2019. DOI: https://doi.org/10.1016/j.future.2018.04.043.
K. Bibi, S. Naz, A. Rehman. Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities. Multimedia Tools and Applications, vol. 79, no. 1–2, pp. 289–340, 2020. DOI: https://doi.org/10.1007/s11042-019-08022-0.
R. Ryu, S. Yeom, S. H. Kim, D. Herbert. Continuous multimodal biometric authentication schemes: A systematic review. IEEE Access, vol.9, pp.34541–34557, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3061589.
K. S. Germain, F. Kragh. Mobile physical-layer authentication using channel state information and conditional recurrent neural networks. In Proceedings of the 93rd IEEE Vehicular Technology Conference, Helsinki, Finland, pp. 1–6, 2021. DOI: https://doi.org/10.1109/VTC2021-Spring51267.2021.9448652.
N. Xie, Z. Y. Li, H. J. Tan. A survey of physical-layer authentication in wireless communications. IEEE Communications Surveys & Tutorials, vol.23, no. 1, pp. 282–310, 2021. DOI: https://doi.org/10.1109/COMST.2020.3042188.
M. Conti, T. Dargahi, A. Dehghantanha. Cyber threat intelligence: Challenges and opportunities. Cyber Threat Intelligence, A. Dehghantanha, M. Conti, T. Dargahi, Eds., Cham, Switzerland: Springer, pp. 1–6, 2018. DOI: https://doi.org/10.1007/978-3-319-73951-9_l.
X. J. Liao, K. Yuan, X. F. Wang, Z. Li, L. Y. Xing, R. Beyah. Acing the IOC game: Toward automatic discovery and analysis of open-source cyber threat intelligence. In Proceedings of ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, pp. 755–766, 2016. DOI: https://doi.org/10.1145/2976749.2978315.
A. Tundis, S. Ruppert, M. Mühlhäuser. On the automated assessment of open-source cyber threat intelligence sources. In Proceedings of the 20th International Conference on Computational Science, Amsterdam, The Netherlands, pp. 453–467, 2020. DOI: https://doi.org/10.1007/978-3-030-50417-5_34.
P. Gao, X. Y. Liu, E. Choi, B. Soman, C. Mishra, K. Farris, D. Song. A system for automated open-source threat intelligence gathering and management. In Proceedings of International Conference on Management of Data, pp. 2716–2720, 2021. DOI: https://doi.org/10.1145/3448016.3452745.
G. Cascavilla, D. A. Tamburri, W. J. Van Den Heuvel. Cybercrime threat intelligence: A systematic multi-vocal literature review. Computers & Security, vol. 105, Article number 102258, 2021. DOI: https://doi.org/10.1016/j.cose.2021.102258.
T. D. Wagner, K. Mahbub, E. Palomar, A. E. Abdallah. Cyber threat intelligence sharing: Survey and research directions. Computers & Security, vol.87, Article number 101589, 2019. DOI: https://doi.org/10.1016/j.cose.2019.101589.
S. K. Anand, S. Kumar. Experimental comparisons of clustering approaches for data representation. ACM Computing Surveys, vol.55, no.3, Article number 45, 2022. DOI: https://doi.org/10.1145/3490384.
M. Khader, G. Al-Naymat. Density-based algorithms for big data clustering using MapReduce framework: A comprehensive study. ACM Computing Surveys, vol.53, no. 5, Article number 93, 2020. DOI: https://doi.org/10.1145/3403951.
F. L. Gewers, G. R. Ferreira, H. F. De Arruda, F. N. Silva, C. H. Comin, D. R. Amancio, L. D. F. Costa. Principal component analysis: A natural approach to data exploration. ACM Computing Surveys, vol.54, no.4, Article number 70, 2021. DOI: https://doi.org/10.1145/3447755.
X. R. Wang, J. Yang, Q. Y. Wang, C. X. Su. Threat intelligence relationship extraction based on distant supervision and reinforcement learning. In Proceedings of the 32nd International Conference on Software Engineering and Knowledge Engineering, pp. 572–576, 2020.
M. Sewak, S. K. Sahay, H. Rathore. Deep reinforcement learning for cybersecurity threat detection and protection: A review. In Proceedings of the 9th International Conference on Secure Knowledge Management In Artificial Intelligence Era, San Antonio, USA, pp. 51–72, 2021. DOI: https://doi.org/10.1007/978-3-030-97532-6_4.
J. Soldani, A. Brogi. Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Computing Surveys, vol.55, no.3, Article number 59, 2022. DOI: https://doi.org/10.1145/3501297.
M. Landauer, F. Skopik, M. Wurzenberger, A. Rauber. System log clustering approaches for cyber security applications: A survey. Computers & Security, vol.92, Article number 101739, 2020. DOI: https://doi.org/10.1016/j.cose.2020.101739.
R. Chalapathy, S. Chawla. Deep learning for anomaly detection: A survey, [Online], Available:https://arxiv.org/abs/1901.03407, 2019.
G. E. I. Selim, E. E. D. Hemdan, A. M. Shehata, N. A. El-Fishawy. Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms. Multimedia Tools and Applications, vol.80, no.8, pp. 12619–12640, 2021. DOI: https://doi.org/10.1007/s11042-020-10354-1.
J. Kindervag. No More Chewy Centers: The Zero Trust Model of Information Security, Cambridge, UK: Forrester Research Inc., 2016.
K. Zhao, L. Pan. A machine learning based trust evaluation framework for online social networks. In Proceedings of the 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Beijing, China, pp. 69–74, 2014. DOI: https://doi.org/10.1109/TrustCom.2014.13.
X. Chen, Y. Y. Yuan, L. L. Lu, J. C. Yang. A multidimensional trust evaluation framework for online social networks based on machine learning. IEEE Access, vol. 7, pp. 175499–175513, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2957779.
X. Chen, Y. Y. Yuan, M. A. Orgun. Using Bayesian networks with hidden variables for identifying trustworthy users in social networks. Journal of Information Science, vol. 46, no. 5, pp. 600–615, 2020. DOI: https://doi.org/10.1177/0165551519857590.
Y. J. Wang. The trust value calculating for social network based on machine learning. In Proceedings of the 9th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, pp. 133–136, 2017. DOI: https://doi.org/10.1109/IHMSC.2017.145.
H. El-Sayed, H. A. Ignatious, P. Kulkarni, S. Bouktif. Machine learning based trust management framework for vehicular networks. Vehicular Communications, vol.25, Article number 100256, 2020. DOI: https://doi.org/10.1016/j.vehcom.2020.100256.
W. Ma, X. Wang, M. S. Hu, Q. L. Zhou. Machine learning empowered trust evaluation method for IoT devices. IEEE Access, vol.9, pp.65066–65077, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3076118.
M. P. Lokhande, D. D. Patil. Trust computation model for IoT devices using machine learning techniques. In Proceeding of the 1st Doctoral Symposium on Natural Computing Research, pp. 195–205, 2021. DOI: https://doi.org/10.1007/978-981-33-4073-2_20.
W. Y. Zhang, B. Wu, Y. Liu. Cluster-level trust prediction based on multi-modal social networks. Neurocomputing, vol.210, pp. 206–216, 2016. DOI: https://doi.org/10.1016/j.neucom.2016.01.108.
M. Mishra, G. S. Gupta, X. Gui. Trust-based cluster head selection using the k-means algorithm for wireless sensor networks. In Proceedings of International Conference on Smart Systems and Inventive Technology, Tirunelveli, India, pp. 819–825, 2019. DOI: https://doi.org/10.1109/ICSSIT46314.2019.8987888.
L. Yang, Y. Z. Lu, S. X. Yang, Y. C. Zhong, T. Guo, Z. F. Liang. An evolutionary game-based secure clustering protocol with fuzzy trust evaluation and outlier detection for wireless sensor networks. IEEE Sensors Journal, vol.21, no. 12, pp. 13935–13947, 2021. DOI: https://doi.org/10.1109/JSEN.2021.3070689.
U. Jayasinghe, G. M. Lee, T. W. Um, Q. Shi. Machine learning based trust computational model for IoT services. IEEE Transactions on Sustainable Computing, vol.4, no. 1, pp.39–52, 2019. DOI: https://doi.org/10.1109/TSUSC.2018.2839623.
G. J. Han, Y. He, J. F. Jiang, N. Wang, M. Guizani, J. A. Ansere. A synergetic trust model based on SVM in underwater acoustic sensor networks. IEEE Transactions on Vehicular Technology, vol.68, no. 11, pp. 11239–11247, 2019. DOI: https://doi.org/10.1109/TVT.2019.2939179.
S. Sagar, A. Mahmood, Q. Z. Sheng, W. E. Zhang. Trust computational heuristic for social internet of things: A machine learning-based approach. In Proceedings of IEEE International Conference on Communications, Dublin, Ireland, 2020. DOI: https://doi.org/10.1109/ICC40277.2020.9148767.
H. Mayadunna, L. Rupasinghe. A trust evaluation model for online social networks. In Proceedings of National Information Technology Conference, Colombo, Sri Lanka, 2018. DOI: https://doi.org/10.1109/NITC.2018.8550080.
J. J. Guo, X. H. Li, Z. Q. Liu, J. F. Ma, C. Yang, J. W. Zhang, D. P. Wu. TROVE: A context-awareness trust model for VANETs using reinforcement learning. IEEE Internet of Things Journal, vol.7, no. 7, pp. 6647–6662, 2020. DOI: https://doi.org/10.1109/JIOT.2020.2975084.
Y. He, G. J. Han, J. F. Jiang, H. Wang, M. Martínez-García. A trust update mechanism based on reinforcement learning in underwater acoustic sensor networks. IEEE Transactions on Mobile Computing, vol.21, no.3, pp. 811–821, 2022. DOI: https://doi.org/10.1109/TMC.2020.3020313.
Y. Y. Ren, W. Liu, A. F. Liu, T. Wang, A. Li. A privacy-protected intelligent crowdsourcing application of iot based on the reinforcement learning. Future Generation Computer Systems, vol.127, pp. 56–69, 2022. DOI: https://doi.org/10.1016/j.future.2021.09.003.
X. D. Zhuang, X. R. Tong. A local trust inferring algorithm based on reinforcement learning DoubleDQN in online social networks. In Proceedings of the 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Chengdu, China, pp. 1064–1069, 2020. DOI: https://doi.org/10.1109/CISP-BMEI51763.2020.9263509.
S. A. Siddiqui, A. Mahmood, W. E. Zhang, Q. Z. Sheng. Machine learning based trust model for misbehaviour detection in internet-of-vehicles. In Proceedings of the 26th International Conference on Neural Information Processing, Sydney, Australia, pp. 512–520, 2019. DOI: https://doi.org/10.1007/978-3-030-36808-1_56.
M. Ghavipour, M. R. Meybodi. Trust propagation algorithm based on learning automata for inferring local trust in online social networks. Knowledge-Based Systems, vol.143, pp.307–316, 2018. DOI: https://doi.org/10.1016/j.knosys.2017.06.034.
M. Hammad, S. Z. Zhang, K. Q. Wang. A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Future Generation Computer Systems, vol.101, pp. 180–196, 2019. DOI: https://doi.org/10.1016/j.future.2019.06.008.
S. Aziz, M. U. Khan, Z. A. Choudhry, A. Aymin, A. Usman. ECG-based biometric authentication using empirical mode decomposition and support vector machines. In Proceedings of IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, Canada, pp. 906–912, 2019. DOI: https://doi.org/10.1109/IEMCON.2019.8936174.
Y. T. Li, H. L. Hu, Z. Q. Zhu, G. Zhou. SCANet: Sensor-based continuous authentication with two-stream convolutional neural networks. ACM Transactions on Sensor Networks, vol.16, no. 3, Article number 29, 2020. DOI: https://doi.org/10.1145/3397179.
H. Kong, L. Lu, J. D. Yu, Y. Y. Chen, F. L. Tang. Continuous authentication through finger gesture interaction for smart homes using WiFi. IEEE Transactions on Mobile Computing, vol.20, no. 11, pp.3148–3162, 2021. DOI: https://doi.org/10.1109/TMC.2020.2994955.
M. P. Centeno, A. Van Moorsel, S. Castruccio. Smartphone continuous authentication using deep learning autoencoders. In Proceedings of the 15th Annual Conference on Privacy, Security and Trust, Calgary, Canada, pp. 147–1478, 2017. DOI: https://doi.org/10.1109/PST.2017.00026.
K. Bicakci, O. Salman, Y. Uzunay, M. Tan. Analysis and evaluation of keystroke dynamics as a feature of contextual authentication. In Proceedings of International Conference on Information Security and Cryptology, Ankara, Turkey, pp. 11–17, 2020. DOI: https://doi.org/10.1109/ISCTURKEY51113.2020.9307967.
M. Smith-Creasey, F. A. Albalooshi, M. Rajarajan. Context awareness for improved continuous face authentication on mobile devices. In Proceedings of the 16th IEEE International Conference on Dependable, Autonomic and Secure Computing, the 16th International Conference on Pervasive Intelligence and Computing, the 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Athens, Greece, pp. 644–652, 2018. DOI: https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00115.
M. Abuhamad, T. Abuhmed, D. Mohaisen, D. H. Nyang. AUTosen: Deep-learning-based implicit continuous authentication using smartphone sensors. IEEE Internet of Things Journal, vol.7, no.6, pp.5008–5020, 2020. DOI: https://doi.org/10.1109/JIOT.2020.2975779.
S. Vhaduri, C. Poellabauer. Multi-modal biometric-based implicit authentication of wearable device users. IEEE Transactions on Information Forensics and Security, vol.14, no. 12, pp. 3116–3125, 2019. DOI: https://doi.org/10.1109/TIFS.2019.2911170.
X. Zhang, L. N. Yao, C. R. Huang, T. Gu, Z. Yang, Y. H. Liu. DeepKey: A multimodal biometric authentication system via deep decoding gaits and brainwaves. ACM Transactions on Intelligent Systems and Technology, vol.11, no. 4, Article number 49, 2020. DOI: https://doi.org/10.1145/3393619.
M. Hammad, Y. S. Liu, K. Q. Wang. Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access, vol.7, pp. 26527–26542, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2886573.
S. H. Choudhury, A. Kumar, S. H. Laskar. Biometrie authentication through unification of finger dorsal biometric traits. Information Sciences, vol.497, pp. 202–218, 2019. DOI: https://doi.org/10.1016/j.ins.2019.05.045.
D. Sivasankaran, M. Ragab, T. Sim, Y. Zick. Context-aware fusion for continuous biometric authentication. In Proceedings of International Conference on Biometrics, Gold Coast, Australia, pp. 233–240, 2018. DOI: https://doi.org/10.1109/ICB2018.2018.00043.
Y. Lin, X. L. Zhu, Z. G. Zheng, Z. Dou, R. L. Zhou. The individual identification method of wireless device based on dimensionality reduction and machine learning. The Journal of Supercomputing, vol.75, no.6, pp.3010–3027, 2019. DOI: https://doi.org/10.1007/s11227-017-2216-2.
G. X. Shen, J. Q. Zhang, A. Marshall, L. N. Peng, X. B. Wang. Radio frequency fingerprint identification for LoRa using spectrogram and CNN. In Proceedings of IEEE INFOCOM Conference on Computer Communications, Vancouver, Canada, 2021. DOI: https://doi.org/10.1109/IN-FOCOM42981.2021.9488793.
G. W. Qing, H. F. Wang, T. P. Zhang. Radio frequency fingerprinting identification for zigbee via lightweight CNN. Physical Communication, vol.44, Article number 101250, 2021. DOI: https://doi.org/10.1016/j.phycom.2020.101250.
Q. Wang, H. Li, D. Zhao, Z. Chen, S. Ye, J. S. Cai. Deep neural networks for CSI-based authentication. IEEE Access, vol.7, pp. 123026–123034, 2019. DOI: https://doi.org/10.1109/AC-CESS.2019.2938533.
J. Yoon, Y. Lee, E. Hwang. Machine learning-based physical layer authentication using neighborhood component analysis in MIMO wireless communications. In Proceedings of International Conference on Information and Communication Technology Convergence, Jeju Island, Republic of Korea, pp. 63–65, 2019. DOI: https://doi.org/10.1109/ICTC46691.2019.8939862.
K. S. Germain, F. Kragh. Physical-layer authentication using channel state information and machine learning. In Proceedings of the 14th International Conference on Signal Processing and Communication Systems, Adelaide, Australia, 2020. DOI: https://doi.org/10.1109/ICSPCS50536.2020.9310070.
M. K. Oh, S. Lee, Y. Kang. Wi-SUN device authentication using physical layer fingerprint. In Proceedings of International Conference on Information and Communication Technology Convergence, Jeju Island, Republic of Korea, pp. 160–162, 2021. DOI: https://doi.org/10.1109/ICTC52510.2021.9620899.
I. Deliu, C. Leichter, K. Franke. Collecting cyber threat intelligence from hacker forums via a two-stage, hybrid process using support vector machines and latent dirichlet allocation. In Proceedings of IEEE International Conference on Big Data, Seattle, USA, pp. 5008–5013, 2018. DOI: https://doi.org/10.1109/BigData.2018.8622469.
M. Kadoguchi, S. Hayashi, M. Hashimoto, A. Otsuka. Exploring the dark web for cyber threat intelligence using machine leaning. In Proceedings of IEEE International Conference on Intelligence and Security Informatics, Shenzhen, China, pp. 200–202, 2019. DOI: https://doi.org/10.1109/ISI.2019.8823360.
P. Koloveas, T. Chantzios, S. Alevizopoulou, S. Skiadopoulos, C. Tryfonopoulos. INTIME: A machine learning-based framework for gathering and leveraging web data to cyber-threat intelligence. Electronics, vol. 10, no. 7, Article number 818, 2021. DOI: https://doi.org/10.3390/electronics10070818.
L. M. Kristiansen, V. Agarwal, K. Franke, R. S. Shah. CTI-twitter: Gathering cyber threat intelligence from twitter using integrated supervised and unsupervised learning. In Proceedings of IEEE International Conference on Big Data, Atlanta, USA, pp. 2299–2308, 2020. DOI: https://doi.org/10.1109/BigData50022.2020.9378393.
M. M. Li, R. F. Zheng, L. Liu, P. Yang. Extraction of threat actions from threat-related articles using multi-label machine learning classification method. In Proceedings of the 2nd International Conference on Safety Produce Informatization, Chongqing, China, pp. 428–431, 2019. DOI: https://doi.org/10.1109/IICSPI48186.2019.9095885.
S. Xun, X. Y. Li, Y. L. Gao. AITI: An automatic identification model of threat intelligence based on convolutional neural network. In Proceedings of the 4th International Conference on Innovation in Artificial Intelligence, Xiamen China, pp. 20–24, 2020. DOI: https://doi.org/10.1145/3390557.3394305.
B. Ampel, S. Samtani, H. Y. Zhu, S. Ullman, H. Chen. Labeling hacker exploits for proactive cyber threat intelligence: A deep transfer learning approach. In Proceedings of IEEE International Conference on Intelligence and Security Informatics, Arlington, USA, pp. 1–6, 2020. DOI: https://doi.org/10.1109/ISI49825.2020.9280548.
X. R. Wang, R. Chen, B. H. Song, J. Yang, Z. W. Jiang, X. Q. Zhang, X. M. Li, S. Q. Ao. A method for extracting unstructured threat intelligence based on dictionary template and reinforcement learning. In Proceedings of the 24th IEEE International Conference on Computer Supported Cooperative Work in Design, Dalian, China, pp. 262–267, 2021. DOI: https://doi.org/10.1109/CSCWD49262.2021.9437858.
M. Du, F. F. Li, G. N. Zheng, V. Srikumar. DeepLog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, Dallas, USA, pp. 1285–1298, 2017. DOI: https://doi.org/10.1145/3133956.3134015.
Y. M. Wang, Z. X. Ji. Design and implementation of a semi-supervised anomaly log detection model DDA. In Proceedings of International Conference on Computer Communication and Artificial Intelligence, Guangzhou, China, pp. 86–90, 2021. DOI: https://doi.org/10.1109/CCAI50917.2021.9447533.
S. Y. Lu, X. Wei, Y. D. Li, L. Q. Wang. Detecting anomaly in big data system logs using convolutional neural network. In Proceedings of the 16th IEEE International Conference on Dependable, Autonomic and Secure Computing, the 16th International Conference on Pervasive Intelligence and Computing, the 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Athens, Greece, pp. 151–158, 2018. DOI: https://doi.org/10.1109/DASC/PiCom/Data-Com/CyberSciTec.2018.00037.
A. Wadekar, T. Gupta, R. Vijan, F. Kazi. Hybrid CAE-VAE for unsupervised anomaly detection in log file systems. In Proceedings of the 10th International Conference on Computing, Communication and Networking Technologies, Kanpur, India, pp. 1–7, 2019. DOI: https://doi.org/10.1109/ICCCNT45670.2019.8944863.
Y. L. Yuan, S. S. Adhatarao, M. K. Lin, Y. C. Yuan, Z. L. Liu, X. M. Fu. ADA: Adaptive deep log anomaly detector. In Proceedings of IEEE INFOCOM Conference on Computer Communications, Toronto, Canada, pp. 2449–2458, 2020. DOI: https://doi.org/10.1109/INFOCOM41043.2020.9155487.
S. Bursic, V. Cuculo, A. D’Amelio. Anomaly detection from log files using unsupervised deep learning. In Proceedings of International Symposium on Formal Methods, Porto, Portugal, pp. 200–207, 2019. DOI: https://doi.org/10.1007/978-3-030-54994-7_15.
L. Yang, J. J. Chen, Z. Wang, W. J. Wang, J. J. Jiang, X. Y. Dong, W. B. Zhang. Semi-supervised log-based anomaly detection via probabilistic label estimation. In Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering, Madrid, Spain, pp. 1448–1460, 2021. DOI: https://doi.org/10.1109/ICSE43902.2021.00130.
S. Yen, M. Moh, T. S. Moh. CausalConvLSTM: Semi-supervised log anomaly detection through sequence modeling. In Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, Boca Raton, USA, pp. 1334–1341, 2019. DOI: https://doi.org/10.1109/ICMLA.2019.00217.
R. Chen, S. L. Zhang, D. W. Li, Y. Z. Zhang, F. R. Guo, W. B. Meng, D. Pei, Y. Z. Zhang, X. Chen, Y. Q. Liu. LogTransfer: Cross-system log anomaly detection for software systems with transfer learning. In Proceedings of the 31st IEEE International Symposium on Software Reliability Engineering, Coimbra, Portugal, pp. 37–47, 2020. DOI: https://doi.org/10.1109/ISSRE5003.2020.00013.
J. J. Chen, S. Y. Guo, W. C. Li, J. Shen, X. S. Qiu, S. J. Shao. Network abnormal behavior detection method based on affinity propagation. In Proceedings of the 6th International Conference on Artificial Intelligence and Security, Hohhot, China, pp. 582–591, 2020. DOI: https://doi.org/10.1007/978-981-15-8086-4_55.
H. H. Peng, W. Wang. Detecting masqueraders by profiling user behaviors. In Proceedings of the 8th International Conference on Instrumentation & Measurement, Computer, Communication and Control, Harbin, China, pp. 454–458, 2018. DOI: https://doi.org/10.1109/IMCCC.2018.00101.
Y. Gao, Y. Ma, D. D. Li. Anomaly detection of malicious users’ behaviors for web applications based on web logs. In Proceedings of the 17th IEEE International Conferenc on Communication Technology, Chengdu, China, pp. 1352–1355, 2017. DOI: https://doi.org/10.1109/ICCT.2017.8359854.
M. Singh, B. M. Mehtre, S. Sangeetha. User behavior profiling using ensemble approach for insider threat detection. In Proceedings of the 5th IEEE International Conference on Identity, Security, and Behavior Analysis, Hyderabad, India, pp. 1–8, 2019. DOI: https://doi.org/10.1109/ISBA.2019.8778466.
M. Singh, B. M. Mehtre, S. Sangeetha. User behaviour based insider threat detection in critical infrastructures. In Proceedings of the 2nd International Conference on Secure Cyber Computing and Communications, Jalandhar, India, pp. 489–494, 2021. DOI: https://doi.org/10.1109/ICSCCC51823.2021.9478137.
B. Sharma, P. Pokharel, B. Joshi. User behavior analytics for anomaly detection using LSTM autoencoder-insider threat detection. In Proceedings of the 11th International Conference on Advances in Information Technology, Bangkok, Thailand, Article number 5, 2020. DOI: https://doi.org/10.1145/3406601.3406610.
T. M. Li, L. M. Yan. SIEM based on big data analysis. In Proceedings of the 3rd International Conference on Cloud Computing and Security, Nanjing, China, pp. 167–175, 2017. DOI: https://doi.org/10.1007/978-3-319-68505-2_15.
J. Lee, J. Kim, I. Kim, K. Han. Cyber threat detection based on artificial neural networks using event profiles. IEEE Access, vol.7, pp. 165607–165626, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2953095.
S. El Hajji, N. Moukafih, G. Orhanou. Analysis of neural network training and cost functions impact on the accuracy of IDS and SIEM systems. In Proceedings of the 3rd International Conference on Codes, Cryptology, and Information Security, Rabat, Morocco, pp. 433–451, 2019. DOI: https://doi.org/10.1007/978-3-030-16458-4_25.
S. M. M. Hossain, R. Couturier, J. Rusk, K. B. Kent. Automatic event categorizer for SIEM. In Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, Toronto, Canada, pp. 104–112, 2021.
H. Hindy, D. Brosset, E. Bayne, A. Seeam, X. Bellekens. Improving SIEM for critical SCADA water infrastructures using machine learning. In Proceedings of International Workshop on Security and Privacy Requirements Engineering, Barcelona, Spain, pp. 3–19, 2019. DOI: https://doi.org/10.1007/978-3-030-12786-2_1.
C. Feng, S. N. Wu, N. W. Liu. A user-centric machine learning framework for cyber security operations center. In Proceedings of IEEE International Conference on Intelligence and Security Informatics, Beijing, China, pp. 173–175, 2017. DOI: https://doi.org/10.1109/ISI.2017.8004902.
G. H. Wang, Y. Wu. BIBRM: A Bayesian inference based road message trust model in vehicular ad hoc networks. In Proceedings of the 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Beijing, China, pp. 481–486, 2014. DOI: https://doi.org/10.1109/TrustCom.2014.137.
Z. Yan. Trust Management in Mobile Environments: Autonomic and Usable Models, Hershey, USA: IGI Global, 2013.
Y. Zhang, B. Song, P. Zhang. Social behavior study under pervasive social networking based on decentralized deep reinforcement learning. Journal of Network and Computer Applications, vol.86, pp. 72–81, 2017. DOI: https://doi.org/10.1016/j.jnca.2016.11.015.
D. J. He, C. Chen, S. Chan, J. J. Bu, A. V. Vasilakos. A distributed trust evaluation model and its application scenarios for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 6, pp. 1164–1175, 2012. DOI: https://doi.org/10.1109/TITB.2012.2199996.
J. F. Jiang, G. J. Han, F. Wang, L. Shu, M. Guizani. An efficient distributed trust model for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, vol.26, no.5, pp. 1228–1237, 2015. DOI: https://doi.org/10.1109/TPDS.2014.2320505.
Y. Dou, H. C. B. Chan, M. H. Au. A distributed trust evaluation protocol with privacy protection for intercloud. IEEE Transactions on Parallel and Distributed Systems, vol.30, no.6, pp. 1208–1221, 2019. DOI: https://doi.org/10.1109/TPDS.2018.2883080.
M. Ashtiani, M. A. Azgomi. A novel trust evolution algorithm based on a quantum-like model of computational trust. Cognition, Technology & Work, vol.21, no. 2, pp. 201–224, 2019. DOI: https://doi.org/10.1007/s10111-018-0496-9.
M. Ashtiani, M. A. Azgomi. A formulation of computational trust based on quantum decision theory. Information Systems Frontiers, vol.18, no. 4, pp. 735–764, 2016. DOI: https://doi.org/10.1007/s10796-015-9555-4.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd. Quantum machine learning. Nature, vol.549, no. 7671, pp. 195–202, 2017. DOI: https://doi.org/10.1038/nature23474.
M. Schuld, I. Sinayskiy, F. Petruccione. An introduction to quantum machine learning. Contemporary Physics, vol.56, no. 2, pp. 172–185, 2015. DOI: https://doi.org/10.1080/00107514.2014.964942.
P. Mitra. Recent Advances in Cryptography and Network Security, Intechopen, 2018. DOI: https://doi.org/10.15772/intechopen.71917.
E. Al Alkeem, S. K. Kim, C. Y. Yeun, M. J. Zemerly, K. F. Poon, G. Gianini, P. D. Yoo. An enhanced electrocardiogram biometric authentication system using machine learning. IEEE Access, vol.7, pp. 123069–123075, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2937357.
F. H. Al-Naji, R. Zagrouba. A survey on continuous authentication methods in internet of things environment. Computer Communications, vol. 163, pp. 109–133, 2020. DOI: https://doi.org/10.1016/j.comcom.2020.09.006.
N. Bala, R. Gupta, A. Kumar. Multimodal biometric system based on fusion techniques: A review. Information Security Journal: A Global Perspective, vol.31, no.3, pp. 289–337, 2022. DOI: https://doi.org/10.1080/19393555.2021.1974130.
S. K. Choudhary, A. K. Naik. Multimodal biometric authentication with secured templates-a review. In Proceedings of the 3rd International Conference on Trends in Electronics and Informatics, Tirunelveli, India, pp. 1062–1069, 2019. DOI: https://doi.org/10.1109/ICOEI.2019.8862563.
D. Dasgupta, A. Roy, A. Nag. Multi-factor authentication. In Advances in User Authentication, D. Dasgupta, A. Roy, A. Nag, Eds., Cham, The Netherlands: Springer, pp. 185–233, 2017. DOI: https://doi.org/10.1007/978-3-319-58808-7_5.
S. W. Shah, N. F. Syed, A. Shaghaghi, A. Anwar, Z. Baig, R. Doss. LCDA: Lightweight continuous device-to-device authentication for a zero trust architecture (ZTA). Computers & Security, vol. 108, Article number 102351, 2021. DOI: https://doi.org/10.1016/j.cose.2021.102351.
A. Candore, O. Kocabas, F. Koushanfar. Robust stable radiometric fingerprinting for wireless devices. In Proceedings of IEEE International Workshop on Hardware-Oriented Security and Trust, San Francisco, USA, pp. 43–49, 2009. DOI: https://doi.org/10.1109/HST.2009.5224969.
J. Q. Zhang, R. Woods, M. Sandell, M. Valkama, A. Marshall, J. Cavallaro. Radio frequency fingerprint identification for narrowband systems, modelling and classification. IEEE Transactions on Information Forensics and Security, vol.16, pp. 3974–3987, 2021. DOI: https://doi.org/10.1109/TIFS.2021.3088008.
Z. J. Wang, W. W. Dou, M. J. Ma, X. X. Feng, Z. H. Huang, C. M. Zhang, Y. J. Guo, D. Chen. A survey of user authentication based on channel state information. Wireless Communications and Mobile Computing, vol.2021, Article number 6636665, 2021. DOI: https://doi.org/10.1155/2021/6636665.
K. Riad, T. Huang, L. S. Ke. A dynamic and hierarchical access control for IoT in multi-authority cloud storage. Journal of Network and Computer Applications, vol. 160, Article number 102633, 2020. DOI: https://doi.org/10.1016/j.jnca.2020.102633.
C. Esposito. Interoperable, dynamic and privacy-preserving access control for cloud data storage when integrating heterogeneous organizations. Journal of Network and Computer Applications, vol. 108, pp. 124–136, 2018. DOI: https://doi.org/10.1016/j.jnca.2018.01.017.
J. Li, X. F. Chen, S. S. M. Chow, Q. Huang, D. S. Wong, Z. L. Liu. Multi-authority fine-grained access control with accountability and its application in cloud. Journal of Network and Computer Applications, vol.112, pp.89–96, 2018. DOI: https://doi.org/10.1016/j.jnca.2018.03.006.
A. Sentuna, A. Alsadoon, P. W. C. Prasad, M. Saadeh, O. H. Alsadoon. A novel enhanced naive Bayes posterior probability (ENBPP) using machine learning: Cyber threat analysis. Neural Processing Letters, vol.53, no. 1, pp. 177–209, 2021. DOI: https://doi.org/10.1007/s11063-020-10381-x.
S. L. Zhang, Z. Y. Zhong, D. W. Li, Q. L. Fan, Y. Q. Sun, M. Zhu, Y. Z. Zhang, D. Pei, J. Y. Sun, Y. L. Liu, H. Yang, Y. Q. Zou. Efficient KPI anomaly detection through transfer learning for large-scale web services. IEEE Journal on Selected Areas in Communications, vol.40, no. 8, pp. 2440–2455, 2022. DOI: https://doi.org/10.1109/JSAC.2022.3180785.
S. Hu, Z. H. Xiao, Q. Rao, R. T. Liao. An anomaly detection model of user behavior based on similarity clustering. In Proceedings of the 4th IEEE Information Technology and Mechatronics Engineering Conference, Chongqing, China, pp. 835–838, 2018. DOI: https://doi.org/10.1109/ITOEC.2018.8740748.
X. H. Sun, G. H. Yang, J. L. Zhang. A real-time detection scheme of user behavior anomaly for management information system. In Proceedings of the 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China, pp. 1054–1058, 2020. DOI: https://doi.org/10.1109/ITNEC48623.2020.9084982.
Y. P. Tang, B. X. Ma, Z. Wu. Research on user clustering algorithm based on software system user behavior trajectory. In Proceedings of the 2nd International Conference on Big Data Technologies, Jinan, China, pp. 11–14, 2019. DOI: https://doi.org/10.1145/3358528.3358572.
S. Marchal, X. Y. Jiang, R. State, T. Engel. A big data architecture for large scale security monitoring. In Proceedings of IEEE International Congress on Big Data, Anchorage, USA, pp. 56–63, 2014. DOI: https://doi.org/10.1109/BigData.Congress.2014.18.
G. M. Køien. Zero-trust principles for legacy components. Wireless Personal Communications, vol.121, no. 2, pp. 1169–1186, 2021. DOI: https://doi.org/10.1007/s11277-021-09055-1.
X. J. Wu, L. W. Xiao, Y. X. Sun, J. H. Zhang, T. L. Ma, L. He. A survey of human-in-the-loop for machine learning. Future Generation Computer Systems, vol. 135, pp. 364–381, 2022. DOI: https://doi.org/10.1016/j.future.2022.05.014.
Y. P. Hu, W. X. Kuang, Z. Qin, K. L. Li, J. L. Zhang, Y. S. Gao, W. J. Li, K. Q. Li. Artificial intelligence security: Threats and countermeasures. ACM Computing Surveys, vol.55, no. 1, Article number 20, 2021. DOI: https://doi.org/10.1145/3487890.
M. N. Islam, R. Colomo-Palacios, S. Chockalingam. Secure access service edge: A multivocal literature review. In Proceedings of the 21st International Conference on Computational Science and its Applications, Cagliari, Italy, pp. 188–194, 2021. DOI: https://doi.org/10.1109/ICCSA54496.2021.00034.
K. Ramezanpour, J. Jagannath. Intelligent zero trust architecture for 5G/6G networks: Principles, challenges, and the role of machine learning in the context of O-RAN. Computer Networks, vol.217, Article number 109358, 2022. DOI: https://doi.org/10.1016/j.comnet.2022.109358.
S. Li, M. Iqbal, N. Saxena. Future industry internet of things with zero-trust security. Information Systems Frontiers, to be published. DOI: https://doi.org/10.1007/s10796-021-10199-5.
N. H. Mahmood, S. Böcker, I. Moerman, O. A. López, A. Munari, K. Mikhaylov, F. Clazzer, H. Bartz, O. S. Park, E. Mercier, S. Saidi, D. M. Osorio, R. Jäntti, R. Pragada, E. Annanperä, Y. H. Ma, C. Wietfeld, M. Andraud, G. Liva, Y. Chen, E. Garro, F. Burkhardt, C. F. Liu, H. Alves, Y. Sadi, M. Kelanti, J. B. Doré, E. Kim, J. S. Shin, G. Y. Park, S. K. Kim, C. Yoon, K. Anwar, P. Seppänen. Machine type communications: Key drivers and enablers towards the 6G era. EURASIP Journal on Wireless Communications and Networking, vol.2021, no. 1, Article number 134, 2021. DOI: https://doi.org/10.1186/s13638-021-02010-5.
Acknowledgements
Open Access funding enabled and organized by CAUL and its Member Institutions.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declared that they have no conflicts of interest to this work.
Additional information
Colored figures are available in the online version at https://link.springer.com/journal/11633
Yang Cao received the B. Sc. degree in information technology from Monash University, Australia in 2020, the M. Sc. degree in data science at Deakin University, Australia in 2021. He is currently a Ph.D. degree candidate in Deakin University, Australia.
His research interests include clustering analysis, anomaly detection and their application in renewable energy.
Shiva Raj Pokhrel received the Ph.D. degree in information communication technology engineering from the Swinburne University of Technology, Australia in 2017. He is a lecturer of Mobile Computing with Deakin University, Australia. He was a Research Fellow with the University of Melbourne, and a network engineer with Nepal Telecom, Nepal from 2007 to 2014. Dr. Pokhrel was a recipient of the prestigious Marie Sklodowska-Curie Grant Fellowship in 2017 and the finalist of the IEEE Future Networks’ Connecting the Unconnected Challenge in 2021. He serves/served as the Workshop Chair/Publicity Co-Chair for several IEEE/ACM conferences, including IEEE INFOCOM, IEEE GLOBECOM, IEEE ICC, and ACM MobiCom.
His research interests include multiconnectivity, federated learning, Industry 4.0 automation, blockchain modeling, optimization, recommender systems, 6G, cloud computing, dynamics control, Internet of Things, and cyber-physical systems as well as their applications in smart manufacturing, autonomous vehicles, and cities.
Ye Zhu received the Ph.D. degree in artificial intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University, Australia in 2017. He is a senior lecturer at the School of Information Technology, Deakin University, Australia. He has published more than 40 papers in AI-related top international conferences or journals, including SIGKDD, AAAI, IJCAI, VLDB, AIJ, TKDE, PRJ, JAIR, ISJ and MLJ. He is on the program committee of SIGKDD, AAAI, IJCAI, PAKDD and ADMA. He has also secured several large research grants for multi-disciplinary research. He is an IEEE Senior Member.
His research interests include clustering analysis, anomaly detection, and their applications for pattern recognition and information retrieval.
Robin Doss received the Ph.D. degree in wireless network from Royal Melbourne Institute of Technology (RMIT) University, Australia in 2004. He is currently the Research Director of the Centre for Cyber Security Research and Innovation (CSRI), Deakin University, Australia. In addition, he also leads the “Next Generation Authentication Technologies” theme within the National Cyber Security Cooperative Research Centre (CSCRC). His research program has been funded by the Australian Research Council (ARC), government agencies such as the Defence Signals Directorate (DSD), Department of Industry, Innovation and Science (DIIS), and industry partners. He has an extensive research publication portfolio. He is a member of the Executive Council of the IoT Alliance Australia (IoTAA). He was a recipient of the “Cyber Security Researcher of the Year Award” from the Australian Information Security Association (AISA) in 2019. He is an IEEE Senior Member.
His research interests include system security, protocol design, and security analysis with a focus on smart, cyber-physical, and critical infrastructures.
Gang Li received the Ph.D. degree in computer science from Institute of Software, Chinese Academy of Sciences, China in 2005. He is currently a professor with the School of Information Technology, Deakin University, Australia. He served on the Program Committee for over 200 international conferences in artificial intelligence, data mining and machine learning, tourism, and hospitality management. He is currently an Associate Editor of Decision Support Systems (Elsevier) and has been the Guest Editor of Enterprise Information Systems (Taylor & Francis), Chinese Journal of Computers, Concurrency and Computation: Practice and Experience (Wiley), and Future Generation Computer Systems (Elsevier).
His research interests include data mining, machine learning and business intelligence.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Cao, Y., Pokhrel, S.R., Zhu, Y. et al. Automation and Orchestration of Zero Trust Architecture: Potential Solutions and Challenges. Mach. Intell. Res. 21, 294–317 (2024). https://doi.org/10.1007/s11633-023-1456-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11633-023-1456-2