Network Intelligence considers the embedding of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in future networks to fasten service delivery and operations, guarantee service availability, allow better agility, resiliency, faster customization, and security. These features perfectly match the complex, dynamic and time-varying nature of future networks. Network intelligence can provide real-time awareness of how a network is performing, with alerts and a clear data trail if a cyber-security issue occurs. This tutorial explores the impact of artificial intelligence, machine learning and deep learning logic to incorporate intelligence in future networks.
Network Intelligence tutorial encompasses three main parts. In the first part, the background on Network Intelligence will be presented i.e., the latest state of the art on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) for network and service management. In the second part, focus will be on Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques for network data analysis to find optimal solution. Various techniques like prediction, classification, clustering, etc will be discussed for case-specific scenarios. In the third part, real-life case studies like forecasting of network data, anomaly detection, etc. will be demonstrated using AI-ML-DL techniques. The demonstration will make use of real network data.
Tapalina Bhattasali is working as Assistant Professor and Head of Information Technology Department at St. Xavier’s College (Autonomous), Kolkata, India. She obtained her PhD in Computer Science & Engineering from University of Calcutta in collaboration with AGH University of Science & Technology, Poland. Her PhD thesis is based on IoT based framework and its security features. She received Doctor of letters (D.Litt.) honorary degree in recognition and appreciation of the contribution in the field of “Secured Internet of Things Framework; Case Study: Rural Healthcare in India” (based on the field works at remote villages of Birbhum, Sundarbans) from IEU, Maldives, working for SAARC countries to eradicate poverty and empowering Economics. She received a number of national and international awards in teaching and research excellence category like Global Outreach Research Award as “Young Researcher in Computer Science & Engineering”; Prestigious IRDP Award for “Teaching and Research Excellence”; National Award of Excellence. She has many years of experience in academia. She teaches various courses in post-graduation, doctoral and industry level on artificial intelligence, machine learning, deep learning, network service management and optimization techniques. She is the member of ACM, ISOC, IAENG. Dr. Bhattasali has published widely in well-regarded national and international journals and edited volumes and conference proceedings. She has presented the results of her research work at numerous national and international conferences in India and abroad. Dr. Bhattasali has delivered her lecture as keynote speaker, invited speaker, resource person in various national and international events. She has been invited as resource person in many national and international webinars, seminars, conferences, workshops. Her main research interest lies in the field of Cloud-IoT, artificial intelligence, machine learning, deep learning, edge intelligence, data analytics, blockchain, swarm intelligence. She is the editor and reviewer of many national and international peer-reviewed journals like IEEE, Inderscience. She has already research collaboration with Bangladesh, Poland, Azerbaijan.
Previsions for 2025 estimate nearly 64 billion Internet-of-Things (IoT) devices connected via the Internet to diverse application scenarios. These scenarios show particularities in terms of devices, communications, data, and services, which increase the complexity of achieving one of their common challenges: to optimize the efficiency of their services. Today, a thriving challenge in behavior data science lies in creating behavior patterns (fingerprints) of devices and networks to optimize their performance and detect potential concerns, especially cyberthreats at early stages.
This tutorial provides an overview of tools and techniques toward behavioral fingerprinting and how these can be used to optimize the efficiency of heterogeneous application scenarios such as cybersecurity, or device authentication. Also, this tutorial explains well-known techniques used to create and evaluate fingerprints, paying particular attention to recent and promising Machine Learning (ML) and Deep Learning (DL) approaches. Further, this is discussed in detail within a set of real use cases, where behavioral fingerprinting is applicable to solve cybersecurity concerns of IoT-based application scenarios. Finally, a selected set of lessons learned within this dedicated area of security management is presented and future trends of behavioral fingerprinting are outlined.
Alberto Huertas Celdrán received the MSc and PhD degrees in Computer Science from the University of Murcia, Spain. He is currently a postdoctoral fellow at the Communication Systems Group CSG, Department of Informatics IfI at the University of Zurich UZH. His scientific interests include medical cyber-physical systems (MCPS), brain-computer interfaces (BCI), cybersecurity, data privacy, continuous authentication, semantic technology, context-aware systems, and computer networks.
Pedro Miguel Sánchez received the MSc degree in computer science from the University of Murcia, Spain. He is currently pursuing his PhD in computer science at University of Murcia. His research interests are focused on continuous authentication, networks, 5G, cybersecurity and the application of machine learning and deep learning to the previous fields.
Muriel Franco is a Junior Researcher and Ph.D. candidate in Informatics under the supervision of Prof. Dr. Burkhard Stiller at the University of Zürich UZH, Switzerland, within the Communication Systems Group CSG of the Department of Informatics IfI. Since September 2018, Muriel is working in Zürich on cybersecurity, economics, blockchains, and Network Function Virtualization (NFV), participating in and driving the Concordia H2020 project's Task 4.3 (Cybersecurity Economics) within a team of networking, security, and economic researchers. Besides that, Muriel is conducting, within the CSG, projects to analyze and visualize cyberattacks. At UZH, Muriel is the Teaching Assistant of the “Mobile Communication Systems” and “Protocols for Multimedia Communications” classes. Muriel holds an MSc (2017) in Computer Science from the Federal University of the Rio Grande do Sul (UFRGS), Brazil, and obtained a BSc (2014) in Computer Science from the Federal University of Pelotas (UFPEL), Brazil.
Bruno Rodrigues is currently a postdoctoral fellow at UZH, who received his PhD degree from the University of Zurich UZH, Switzerland, in 2020 and his MSc degree in 2016 from the University of Sao Paulo, Brazil. He focuses his research on network security focused on aspects of building a collaborative network defense against cyber threats such as Distributed Denial-of-Service (DDoS) attacks. He co-authored 3 patents and published over 30 research papers and journals in related IEEE networking conferences (e.g., IEEE/IFIP NOMS, IEEE/IFIP IM, IEEE LCN) and leading IEEE journals (IEEE TNSM, IEEE Communications Magazine).
Gérôme Bovet is the head of data science for the Swiss Department of Defense, where he leads a research team and a portfolio of about 30 projects. His work focuses on Machine and Deep Learning approaches applied to cyber-defense use cases, with an emphasis on anomaly detection, adversarial and collaborative learning. He received his PhD in networks and systems from Telecom ParisTech, France, in 2015.
Gregorio Martinez Pérez is Full Professor in the Department of Information and Communications Engineering of the University of Murcia, Spain. His scientific activity is mainly devoted to cybersecurity and networking, also working on the design and autonomic monitoring of real-time and critical applications and systems. He is working on different national (14 in the last decade) and European IST research projects (11 in the last decade) related to these topics, being Principal Investigator in most of them. He has published 160+ papers in national and international conference proceedings, magazines and journals.
Burkhard Stiller received the Informatik-Diplom (MSc) degree in Computer Science and the Dr. rer.- nat. (PhD) degree from the University of Karlsruhe, Germany, in 1990 and 1994, respectively. He was with the Computer Lab, University of Cambridge, U.K, ETH Zürich, Switzerland, and the University of Federal Armed Forces Munich, Germany. Since 2004 he chairs the Communication Systems Group CSG, Department of Informatics IfI, University of Zürich UZH, Switzerland as a Full Professor. Besides being a member of the editorial board of the IEEE Transactions on Network and Service Management, Springer’s Journal of Network and Systems Management, and the KICS’ Journal of Communications and Networks, Burkhard is past Editor in-Chief of Elsevier’s Computer Networks journal. His main research interests are published in well over 300 research papers and include systems with a fully decentralized control (block¬chains, clouds, peer-to-peer), network and service management (economic management), Internet-of-Things (security of constrained devices, LoRa), and telecommunication eco¬nomics (charging and accounting).