Please feel free to share with us any feedback you might have on the reading list below, and also suggest papers for inclusion.
- M. Mohammadi, A. Al-Fuqaha, S. Sorour and M. Guizani, “Deep Learning for IoT Big Data and Streaming Analytics: A Survey,” in IEEE Communications Surveys & Tutorials. doi: 10.1109/COMST.2018.2844341
Summary: The authors present a comprehensive background on different DL architectures and algorithms. They also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, the authors shed light on some challenges and potential directions for future research. At the end of each section, they highlight the lessons learned based on their experiments and review of the recent literature.
- R. Boutaba, M. A. Salahuddin, N. Limam, S. Ayoubi, N. Shahriar, F. Estrada-Solano and O. M. Caicedo, “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities,” Journal of Internet Services and Applications, vol. 9, no 1, p. 16, 2018.
Summary: This survey presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security.
- L. Cui, S. Yang, F. Chen, Z. Ming, N. Lu and J. Qin, “A survey on application of machine learning for Internet of Things,” International Journal of Machine Learning and Cybernetics., vol. 9, pp. 1399–1417, 2018.
Summary: This survey paper focuses on providing an overview of the application of machine learning in the domain of IoT. They provide a comprehensive survey highlighting the recent progresses in machine learning techniques for IoT and describe various IoT applications. The application of machine learning for IoT enables users to obtain deep analytics and develop efficient intelligent IoT applications.
- P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza, “A survey of machine learning techniques applied to self-organizing cellular networks,” IEEE Communications Surveys Tutorials, vol. 19, pp. 2392– 2431, 2017.
Summary: This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed.
- T. Nguyen and G. Armitage, “A Survey of Techniques for Internet Traffic Classification Using Machine Learning,” IEEE Commun. Surveys & Tutorials, vol. 10, no. 4, 2008, pp. 56–76.
Summary: This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification – an inter-disciplinary blend of IP networking and data mining techniques. They provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature.
2. AI applications for Network Management:
Network Traffic Control Systems
- Z. M. Fadlullah et al., “State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems,” in IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2432-2455, Fourth quarter 2017. doi: 10.1109/COMST.2017.2707140
Summary: The authors provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. They also discuss the deep learning enablers for network systems and discuss, in detail, a new use case, i.e., deep learning based intelligent routing. The authors demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Finally, they discuss a number of open research issues, which researchers may find useful in the future.
Network and Protocol Management
- I. T. Abdel-Halim and H. M. A. Fahmy, “Prediction-based protocols for vehicular Ad Hoc Networks: Survey and taxonomy,” Computer Networks, vol. 130. Elsevier B.V., pp. 34–50, 2018.
Summary: This paper follows the guidelines of systematic literature reviews to provide a premier and unbiased survey of the existing prediction-based protocols and develop novel taxonomies of those protocols based on their main prediction applications and objectives. A discussion on each category of both taxonomies is presented, with a focus on the requirements, constrains, and challenges.
- M. F. Zhani, H. Elbiaze, and F. Kamoun, “A prediction-based active queue management for TCP networks,” in IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, July 1-4, 2012.
Summary: In this paper, the authors develop an analytical model to assess the effect of α_SNFAQM on TCP. The study proves that this AQM is efficient enough to stabilize queue size in routers/switches, and thereby allowing to control end-to-end packet delay. These results have been also validated by simulations for a topology with multiple bottleneck links.
Data Center Management
- Q. Zhang, M. F. Zhani, R. Boutaba, and J. L. Hellerstein, “Dynamic heterogeneity-aware resource provisioning in the cloud,” IEEE Transactions on Cloud Computing, vol. 2, no. 1, pp. 14–28, January 2014.
Summary: This paper presents Harmony, a Heterogeneity-Aware dynamic capacity provisioning scheme for cloud data centers. The authors first use the K-means clustering algorithm to divide workload into distinct task classes with similar characteristics in terms of resource and performance requirements. Then they present a technique that dynamically adjusting the number of machines to minimize total energy consumption and scheduling delay.
Traffic Analysis, Modeling, and Prediction
- R. Costa Filho, W. Lautenschlager, N. Kagami, M. C. Luizelli, V. Roesler, L. P. Gaspary “Scalable QoE-aware Path Selection in SDN-based Mobile Networks.” In: Proceedings of IEEE International Conference on Computer Communications (IEEE INFOCOM 2018). Hawaii, USA, 2018
Summary: In this paper the authors introduce a machine learning-based prediction model for dynamically provisioning QoE-aware paths in programmable networks.
- D. Naboulsi, R. Stanica, M. Fiore “Classifying Call Profiles in Large-scale Mobile Traffic Datasets.” IEEE Infocom, Toronto, Canada, April 2014.
Summary: In this paper, the authors propose a framework to analyze broad sets of Call Detail Records (CDRs) so as to define categories of mobile call profiles and classify network usages accordingly. they evaluate their framework on a CDR dataset including more than 300 million calls recorded in an urban area over 5 months.
- M. F. Zhani, H. Elbiaze, and F. Kamoun, “Analysis and prediction of real network traffic,” Journal of Networks, vol. 4, no. 9, pp. 855–865, November 2009.
Summary: In this paper, a neurofuzzy model (α SNF), the AutoRegressive Moving Average (ARMA) model and the Integrated AutoRegressive Moving Average (ARIMA) model are used for predicting. Via experimentation on real network traffic of different links, the authors study the effect of some parameters on the prediction performance in terms of error.
- G. Aceto, D. Ciuonzo, A. Montieri and A. Pescapé, “Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges“, IEEE Transactions on Network and Service Management, 2019 in press.
Summary: In this work, deep learning is leveraged to design practical mobile traffic classifiers based on automatically-extracted features to be able to cope with encrypted traffic, and reflecting their complex patterns. Different state-of-the-art deep learning techniques for traffic classification are reproduced, dissected, and set into a systematic framework for comparison, including also a performance evaluation workbench.
- G. Aceto, D. Ciuonzo, A. Montieri and A. Pescapé, “Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges“, IEEE Transactions on Network and Service Management. DOI: 10.1109/TNSM.2019.2899085
Summary: The authors propose deep learning as a viable strategy to design practical mobile traffic classifiers based on automatically extracted features, able to cope with encrypted traffic, and reflecting their complex traffic patterns. To this end, they evaluate different state-of-the-art deep learning techniques from (standard) traffic classification, and present their results achieved.
- H. Wei, G. Hu, X. Han, P. Qiao, Z. Zhou, Z. Feng and X. Yin, “A New BRB Model for Cloud Security-State Prediction Based on the Large-Scale Monitoring Data,” IEEE Access, vol. 6, pp. 11907-11920, March 2018.
Summary: This paper aims to predict the security state with multiple large-scale attributes in cloud computing system. A double-layer method for predicting the security state of cloud computing system based on the belief rule-base model is proposed, where the evidential reasoning (ER) algorithm is employed to fuse the multiple system indicators of actual cloud system and make a reasonable assessment to describe the cloud security state. This method can utilize quantitative and qualitative information simultaneously.
- A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1153–1176, 2nd Quart., 2016.
Summary: This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized.