Читать книгу Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning - Группа авторов - Страница 39
Bibliography
Оглавление1 1 D'Alconzo, A., Drago, I., Morichetta, A. et al. (2019). A survey on big data for network traffic monitoring and analysis. IEEE Transactions on Network and Service Management 16 (3): 800–813.
2 2 Diao, Y. and Shwartz, L. (2017). Building automated data driven systems for it service management. Journal of Network and Systems Management 25 (4): 848–883.
3 3 Alshammari, R. and Zincir‐Heywood, N. (2015). How robust can a machine learning approach be for classifying encrypted VoIP? Journal of Network and Systems Management 23 (4): 830–869.
4 4 Boden, M.A. (2016). AI Its Nature and Future. Oxford University Press. ISBN 9780198777984.
5 5 Bernstein, L. and Yuhas, C.M. (1988). Expert systems in network management‐the second revolution. IEEE Journal on Selected Areas in Communications 6 (5): 784–787.
6 6 Tran, H.M. and Schönwälder, J. (2015). Discaria: distributed case‐based reasoning system for fault management. IEEE Transactions on Network and Service Management 12 (4): 540–553.
7 7 Fallon, L. and OSullivan, D. (2014). The Aesop approach for semantic‐based end‐user service optimization. IEEE Transactions on Network and Service Management 11 (2): 220–234.
8 8 Alpaydin, E. (2020). Introduction to Machine Learning, vol. 4. MIT Press. ISBN 9780262043793.
9 9 Buczak, A.L. and Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communication Surveys and Tutorials 18 (2): 1153–1176. https://doi.org/10.1109/COMST.2015.2494502.
10 10 Wang, M., Cui, Y., Wang, X. et al. (2017). Machine learning for networking: workflow, advances and opportunities. IEEE Network 32 (2): 92–99.
11 11 Calyam, P., Dhanapalan, M., Sridharan, M. et al. (2014). Topology‐aware correlated network anomaly event detection and diagnosis. Journal of Network and Systems Management 22 (2): 208–234.
12 12 Bhuyan, M.H., Bhattacharyya, D.K., and Kalita, J.K. (2014). Network anomaly detection: methods, systems and tools. IEEE Communications Surveys and Tutorials 16 (1): 303–336. https://doi.org/10.1109/SURV.2013.052213.00046. URL
13 13 Le, D.C. and Zincir‐Heywood, N. (2018). Big data in network anomaly detection. In: Encyclopedia of Big Data Technologies (ed. S. Sakr and A. Zomaya), 1–9. Cham: Springer International Publishing. ISBN 978‐3‐319‐63962‐8. https://doi.org/10.1007/978‐3‐319‐63962‐8_161‐1.
14 14 Nawrocki, P. and Sniezynski, B. (2018). Adaptive service management in mobile cloud computing by means of supervised and reinforcement learning. Journal of Network and Systems Management 26 (1): 1–22.
15 15 Bachl, M., Zseby, T., and Fabini, J. (2019). Rax: deep reinforcement learning for congestion control. ICC 2019‐2019 IEEE International Conference on Communications (ICC), IEEE, pp. 1–6.
16 16 Amiri, R., Almasi, M.A., Andrews, J.G., and Mehrpouyan, H. (2019). Reinforcement learning for self organization and power control of two‐tier heterogeneous networks. IEEE Transactions on Wireless Communications 18 (8): 3933–3947.
17 17 Boutaba, R., Salahuddin, M.A., Limam, N. et al. (2018). A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. Journal of Internet Services and Applications 9 (1): https://doi.org/10.1186/s13174‐018‐0087‐2.
18 18 Syu, Y., Wang, C., and Fanjiang, Y. (2019). Modeling and forecasting of time‐aware dynamic QoS attributes for cloud services. IEEE Transactions on Network and Service Management 16 (1): 56–71.
19 19 Dalmazo, B.L., Vilela, J.P., and Curado, M. (2017). Performance analysis of network traffic predictors in the cloud. Journal of Network and Systems Management 25 (2): 290–320. https://doi.org/10.1007/s10922‐016‐9392‐x.
20 20 Hardegen, C., Pfülb, B., Rieger, S., and Gepperth, A. (2020). Predicting network flow characteristics using deep learning and real‐world network traffic. IEEE Transactions on Network and Service Management 17 (4): 2662–2676.
21 21 Chen, Z., Wen, J., and Geng, Y. (2016). Predicting future traffic using Hidden Markov models. 2016 IEEE 24th International Conference on Network Protocols (ICNP), IEEE, pp. 1–6.
22 22 Zhang, Y. and Zhou, Y. (2018). Distributed coordination control of traffic network flow using adaptive genetic algorithm based on cloud computing. Journal of Network and Computer Applications 119: 110–120.
23 23 Diao, Y. and Shwartz, L. (2015). Modeling service variability in complex service delivery operations. In: 11th International Conference on Network and Service Management, CNSM 2015, Barcelona, Spain (9–13 November 2015) (ed. M. Tortonesi, J. Schonwalder, E.R.M. Madeira et al.), 265–269. IEEE Computer Society. https://doi.org/10.1109/CNSM.2015.7367369.
24 24 Diao, Y. and Rosu, D. (2018). Improving response accuracy for classification‐ based conversational IT services. 2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018. Taipei, Taiwan: IEEE (23–27 April 2018), pp. 1–15. https://doi.org/10.1109/NOMS.2018.8406138.
25 25 Morichetta, A. and Mellia, M. (2019). Clustering and evolutionary approach for longitudinal web traffic analysis. Performance Evaluation 135. https://doi.org/10.1016/j.peva.2019.102033.
26 26 Khatouni, A.S., Seddigh, N., Nandy, B., and Zincir‐Heywood, N. (2021). Machine learning based classification accuracy of encrypted service channels: analysis of various factors. Journal of Network and Systems Management 29 (1): 1–27.
27 27 Kim, H., Lee, D., Jeong, S. et al. (2019). Machine learning‐based method for prediction of virtual network function resource demands. 2019 IEEE Conference on Network Softwarization (NetSoft), IEEE, pp. 405–413.
28 28 Moradi, F., Stadler, R., and Johnsson, A. (2019). Performance prediction in dynamic clouds using transfer learning. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), IEEE, pp. 242–250.
29 29 Elsayed, M., Erol‐Kantarci, M., Kantarci, B. et al. (2020). Low‐latency communications for community resilience microgrids: a reinforcement learning approach. IEEE Transactions on Smart Grid 11 (2): 1091–1099. https://doi.org/10.1109/TSG.2019.2931753.
30 30 Khanchi, S., Vahdat, A., Heywood, M., and Zincir‐Heywood, N. (2018). On botnet detection with genetic programming under streaming data label budgets and class imbalance. Swarm and Evolutionary Computation 39: 123–140.
31 31 I. Nevat, D.M. Divakaran, S. G. Nagarajan et al. (2018). Anomaly detection and attribution in networks with temporally correlated traffic. IEEE/ACM Transactions on Networking 26 (1): 131–144.
32 32 Kim, D., Woo, J., and Kim, H.K. (2016). “i know what you did before”: general framework for correlation analysis of cyber threat incidents. MILCOM 2016 – 2016 IEEE Military Communications Conference, pp. 782–787. https://doi.org/10.1109/MILCOM.2016.7795424.
33 33 Meng, M. (2008). Network security data mining based on wavelet decomposition. 2008 7th World Congress on Intelligent Control and Automation, pp. 6646–6649. https://doi.org/10.1109/WCICA.2008.4593932.
34 34 Tartakovsky, A.G., Rozovskii, B.L., Blazek, R.B., and Kim, H. (2006). A novel approach to detection of intrusions in computer networks via adaptive sequential and batch‐sequential change‐point detection methods. IEEE Transactions on Signal Processing 54 (9): 3372–3382. https://doi.org/10.1109/TSP.2006.879308.
35 35 Bantouna, A., Poulios, G., Tsagkaris, K., and Demestichas, P. (2014). Network load predictions based on big data and the utilization of self‐organizing maps. Journal of Network and Systems Management 22 (2): 150–173. https://doi.org/10.1007/s10922‐013‐9285‐1.
36 36 Bacquet, C., Zincir‐Heywood, N., and Heywood, M. (2011). Genetic optimization and hierarchical clustering applied to encrypted traffic identification. 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), April 2011, pp. 194–201. https://doi.org/10.1109/CICYBS.2011.5949391.
37 37 Le, D.C., Zincir‐Heywood, N., and Heywood, M. (2016). Data analytics on network traffic flows for botnet behaviour detection. IEEE Symposium Series on Computational Intelligence (SSCI '16), December 2016, pp. 1–7. ISBN 9781509042401. https://doi.org/10.1109/SSCI.2016.7850078.
38 38 Finamore, A., Mellia, M., Meo, M., and Rossi, D. (2010). KISS: stochastic packet inspection classifier for UDP traffic. IEEE/ACM Transactions on Networking 18 (5): 1505–1515. https://doi.org/10.1109/TNET.2010.2044046.
39 39 Kayacik, G., Zincir‐Heywood, N., and Heywood, M. (2011). Can a good offense be a good defense? Vulnerability testing of anomaly detectors through an artificial arms race. Applied Soft Computing 11 (7): 4366–4383. https://doi.org/10.1016/j.asoc.2010.09.005.
40 40 Haddadi, F. and Zincir‐Heywood, N. (2016). Benchmarking the effect of flow exporters and protocol filters on botnet traffic classification. IEEE Systems Journal 10 (4): 1390–1401. https://doi.org/10.1109/JSYST.2014.2364743.
41 41 Bronfman‐Nadas, R., Zincir‐Heywood, N., and Jacobs, J.T. (2018). An artificial arms race: could it improve mobile malware detectors? Network Traffic Measurement and Analysis Conference, TMA 2018, Vienna, Austria: IEEE (26–29 June 2018), pp. 1–8. https://doi.org/10.23919/TMA.2018.8506545.
42 42 Lotfollahi, M., Siavoshani, M.J., Zade, R.S.H., and Saberian, M. (2019). Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Computing. https://doi.org/10.1007/s00500‐019‐04030‐2.
43 43 Wilkins, Z. and Zincir‐Heywood, N. (2020). COUGAR: clustering of unknown malware using genetic algorithm routines. In: GECCO '20: Genetic and Evolutionary Computation Conference, Cancún Mexico (July 8‐12, 2020) (ed. C.A.C. Coello), 1195–1203. ACM. https://doi.org/10.1145/3377930.3390151.
44 44 Ahmed, S., Lee, Y., Hyun, S., and Koo, I. (2019). Unsupervised machine learning‐based detection of covert data integrity assault in smart grid networks utilizing isolation forest. IEEE Transactions on Information Forensics and Security 14 (10): 2765–2777. https://doi.org/10.1109/TIFS.2019.2902822.
45 45 Le, D.C. and Zincir‐Heywood, N. (2020). Exploring anomalous behaviour detection and classification for insider threat identification. International Journal of Network Management. https://doi.org/e2109.
46 46 Dietz, C., Dreo, G., Sperotto, A., and Pras, A. (2020). Towards adversarial resilience in proactive detection of botnet domain names by using MTD. NOMS 2020 ‐ 2020 IEEE/IFIP Network Operations and Management Symposium, pp. 1–5. https://doi.org/10.1109/NOMS47738.2020.9110332.
47 47 Le, D.C., Zincir‐Heywood, N., and Heywood, M. (2020). Analyzing data granularity levels for insider threat detection using machine learning. IEEE Transactions on Network and Service Management 17 (1): 30–44.
48 48 Bag, T., Garg, S., Rojas, D.F.P., and Mitschele‐Thiel, A. (2020). Machine learning‐based recommender systems to achieve self‐coordination between son functions. IEEE Transactions on Network and Service Management 17 (4): 2131–2144. https://doi.org/10.1109/TNSM.2020.3024895.
49 49 Makanju, A., Zincir‐Heywood, N., and Milios, E.E. (2013). Investigating event log analysis with minimum apriori information. 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 962–968.
50 50 Jiang, H., Zhang, J.J., Gao, W., and Wu, Z. (2014). Fault detection, identification, and location in smart grid based on data‐driven computational methods. IEEE Transactions on Smart Grid 5 (6): 2947–2956. https://doi.org/10.1109/TSG.2014.2330624.
51 51 Uriarte, R.B., Tiezzi, F., and Tsaftaris, S.A. (2016). Supporting autonomic management of clouds: service clustering with random forest. IEEE Transactions on Network and Service Management 13 (3): 595–607. https://doi.org/10.1109/TNSM.2016.2569000.
52 52 Fadlullah, Z.M., Tang, F., Mao, B. et al. (2017). State‐of‐the‐art deep learning: evolving machine intelligence toward tomorrow's intelligent network traffic control systems. IEEE Communication Surveys and Tutorials 19 (4): 2432–2455. https://doi.org/10.1109/COMST.2017.2707140.
53 53 Messager, A., Parisis, G., Kiss, I.Z. et al. (2019). Inferring functional connectivity from time‐series of events in large scale network deployments. IEEE Transactions on Network and Service Management 16 (3): 857–870. https://doi.org/10.1109/TNSM.2019.2932896.
54 54 Tiwana, M.I., Sayrac, B., and Altman, Z. (2010). Statistical learning in automated troubleshooting: application to lte interference mitigation. IEEE Transactions on Vehicular Technology 59 (7): 3651–3656. https://doi.org/10.1109/TVT.2010.2050081.
55 55 Ahmed, J., Josefsson, T., Johnsson, A. et al. (2018). Automated diagnostic of virtualized service performance degradation. NOMS 2018 – 2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–9. https://doi.org/10.1109/NOMS.2018.8406234.
56 56 Renga, D., Apiletti, D., Giordano, D. et al. (2020). Data‐driven exploratory models of an electric distribution network for fault prediction and diagnosis. Computing 102 (5): 1199–1211. https://doi.org/10.1007/s00607‐019‐00781‐w.
57 57 Steenwinckel, B., Paepe, D.D., Hautte, S.V. et al. (2021). FLAGS: a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems 116: 30–48. https://doi.org/10.1016/j.future.2020.10.015.
58 58 Xie, J., Yu, F.R., Huang, T. et al. (2018). A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges. IEEE Communication Surveys and Tutorials 21 (1): 393–430.
59 59 Zhang, C., Patras, P., and Haddadi, H. (2019). Deep learning in mobile and wireless networking: a survey. IEEE Communication Surveys and Tutorials. 21 (3)
60 60 Park, S., Kim, H., Hong, J. et al. (2020). Machine learning‐based optimal VNF deployment. 21st Asia‐Pacific Network Operations and Management Symposium, APNOMS 2020, Daegu, South Korea (22–25 September 2020), IEEE, pp. 67–72. https://doi.org/10.23919/APNOMS50412.2020.9236970.
61 61 Lerner, A. (2017). Intent‐based networking. Gartner Blog: https://blogs.gartner.com/andrew-lerner/2017/02/07/intent-based-networking/ (accessed 15 April 2021).
62 62 ETSI (2020). Zero‐touch network and Service Management. https://www.etsi.org/technologies/zero-touch-network-service-management (accessed 13 April 2021).
63 63 Tsvetkov, T., Ali‐Tolppa, J., Sanneck, H., and Carle, G. (2016). Verification of configuration management changes in self‐organizing networks. IEEE Transactions on Network and Service Management 13 (4): 885–898. https://doi.org/10.1109/TNSM.2016.2589459.
64 64 Zhang, Y., Yao, J., and Guan, H. (2017). Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Computing 4 (6): 60–69. https://doi.org/10.1109/MCC.2018.1081063.
65 65 Mismar, F.B., Choi, J., and Evans, B.L. (2019). A framework for automated cellular network tuning with reinforcement learning. IEEE Transactions on Communications 67 (10): 7152–7167. https://doi.org/10.1109/TCOMM.2019.2926715.
66 66 Yao, H., Mai, T., Jiang, C. et al. (2019). Ai routers network mind: a hybrid machine learning paradigm for packet routing. IEEE Computational Intelligence Magazine 14 (4): 21–30. https://doi.org/10.1109/MCI.2019.2937609.
67 67 Zhang, Q., Wang, X., Lv, J., and Huang, M. (2020). Intelligent content‐aware traffic engineering for SDN: an Ai‐driven approach. IEEE Network 34 (3): 186–193. https://doi.org/10.1109/MNET.001.1900340.
68 68 Zhang, J., Ye, M., Guo, Z. et al. (2020). CFR‐RL: traffic engineering with reinforcement learning in SDN. IEEE Journal on Selected Areas in Communications 38 (10): 2249–2259. https://doi.org/10.1109/JSAC.2020.3000371.
69 69 Le, D.C. and Zincir‐Heywood, N. (2020). A frontier: dependable, reliable and secure machine learning for network/system management. Journal of Network and Systems Management 28 (4): 827–849.