Machine Learning Meets Communication Networks.pptx

Machine Learning Meets Communication Networks

1. Department of Computer Science and Engineering Technical Seminar Presentation on “ Machine Learning Meets Communication Networks: Current Trends and Future Challenges ” 1 Guide Name: Name:Mrs.Sushmita N. Nesarikar Designation: Assistant Professor Batch No:07 Student Name: Ms.Vani B Magadum (2BU20CS102) Accredited by NBA VISVESVARAYA TECHNOLOGICAL UNIVERSITY - BELAGAVI 4/16/2024

2. Contents Abstract Introduction Technical keyword Literature Survey Motivation Proposed methodology Applications Future Research Direction 9. Results and Conclusion References….. 2 4/16/2024

3. Abstract The growing network density and increase in network traffic caused by the massively expanding number of connected devices and online services, require intelligent network operations. Research on the application of ML in communication networks is described in: i ) the three layers, i.e., physical, access, and network layers; and ii) Networking concepts such as Multi-access Edge Computing (MEC) , Software Defined Networking (SDN), Network Functions Virtualization (NFV) , and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. 3 4/16/2024

4. Intr odu ction The M achine learning and communication networks represents a dynamic frontier in technology, offering transformative potential across diverse domains. Current trends reveal a burgeoning synergy, with machine learning enhancing network performance, security, and resource management. The integration also presents formidable challenges, including algorithmic complexity, data privacy concerns, and the need for adaptable infrastructure. This intersection promises to reshape communication paradigms, enabling autonomous networks and efficient spectrum utilization. 4 4/16/2024

5. Technical Keywords MAC layer SDN ( Software Defined Networking) NFV ( Network Functions Virtualization) MEC ( Multi-access Edge Computing) 5 4/16/2024

6. Literature Survey : S.NO AUTHORS/YEAR TITLE OBSERVATIONS 1 M. Casado, M. J. Freedman, J. Pettit, J. Luo, N. McKeown, and S. Shenker , 2007 “Ethane: Taking control of the enterprise” "Ethane" marks a significant advancement in enterprise network control, providing centralized management through a global view of network state. This observation explores Ethane's impact on network security, scalability, and policy enforcement. 2 A. Krizhevsky, I. Sutskever, and G. E. Hinton 2012. ‘‘ImageNet classification with deep convolutional neural networks’’. The paper demonstrated the power of deep convolutional neural networks for large-scale image classification tasks and played a pivotal role in popularizing deep learning within the computer vision community. 3 Z. Jiang, A. Balu, C. Hegde, and S. Sarkar,2017 “ Collaborative deep learning in fixed topology networks” Collaborative deep learning in fixed topology networks demonstrates the potential to overcome resource limitations and enhance model performance through distributed computation. 4 S. J. Russell and P. Norvig,2016 ‘‘ Artificial Intelligence: A Modern Approach. ’’ The observations provided above are speculative based on the general understanding modern approach language modeling and advancements in recurrent neural network architectures up to the present. 6 4/16/2024

7. Contd… S.NO AUTHORS/YEAR TITLE OBSERVATIONS 5 D. Grzonka, A. Jakóbik, J. Koodziej, and S. Pllana ,2018 “Using a multi-agent system and arti ficial intelligence for monitoring and improving the cloud performance and security.” Employing a multi-agent system and AI enhances cloud performance and security by enabling proactive monitoring and adaptive responses, ensuring reliability and resilience. 6 M. L. Puterman,2014 “Markov Decision Processes: Discrete Stochastic Dynamic Programming” Markov Decision Processes employ discrete stochastic dynamic programming to model decision-making in sequential, uncertain environments 7 F. Bonomi, R. Milito, P. Natarajan, and J. Zhu,2014 “Fog computing: A plat form for Internet of Things and analytics” Fog computing acts as an intermediary platform between IoT devices and the cloud, enabling real-time data processing, analysis, and decision-making 8 M. Chen, Z. Yang, W. Saad, C. Yin, H. Vincent Poor, and S. Cui,2019 “A joint learning and communications framework for federated learning over wireless networks” A joint learning and communications framework facilitates federated learning over wireless networks, optimizing model 9 Y.-L. Lee, P.-K. Tsung, and M. Wu “Technology trend of edge AI” Edge AI is a burgeoning technology trend, leveraging AI algorithms to process data locally on edge devices, enhancing efficiency, reducing latency, and preserving privacy 7 4/16/2024

8. Motivation The motivation behind exploring potential to revolutionize network management, enabling autonomous decision-making, predictive analytics, and real-time optimization. By leveraging machine learning, communication networks can adapt to changing conditions, reliability and security, ultimately shaping the future of interconnected systems. Machine learning techniques offer promising solutions to address the increasingly complex challenges faced by communication networks, including dynamic traffic patterns, resource optimization, security threats, and quality of service improvements . 8 4/16/2024

9. 4/16/2024 9 Figure 2: A setup of a Neuro beamformer where an ANN is used to predict the beamformingweights BeamFormer

10. 10 In the setup of a Neuro beamformer, an Artificial Neural Network (ANN) is employed to predict beamforming weights. This involves training the ANN with input data such as spatial information, channel characteristics, and signal-to-noise ratios. The ANN learns to generate optimal beamforming weights based on this input, effectively adapting to dynamic environmental conditions. This approach offers advantages in real-time beamforming applications by providing rapid and accurate adjustments without extensive computational overhead . 4/16/2024

11. Autoencoder: 4/16/2024 Figure 3: A simple autoencoder for end-to-end communication .

12. Autoencoder: Transmitter (Encoder) : At the transmitter side, the encoder component of the autoencoder transforms the input data into a compact representation. The encoder compresses the input data into a lower-dimensional representation, capturing its essential features. Channel (Transmission) : The compact representation, analogous to the encoded message, is transmitted over the communication channel. The channel may introduce noise, distortion, or other impairments to the transmitted signal, mimicking real-world communication environments where data is subject to interference during transmission. Receiver (Decoder) : Upon receiving the transmitted signal, the decoder component of the autoencoder reconstructs the original data from the compact representation.. The decoder maps the received signal back to the original data space, aiming to minimize the effects of noise and distortion introduced during transmission. 12 4/16/2024

13. Network Traffic Control: 13 FIGURE 4. General learning approaches for network traffic control 4/16/2024

14. Networking Architectures : SDN and NFV 14 4/16/2024

15. SDN and NFV T he integration of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) presents a transformative paradigm. SDN decouples network control from forwarding functions, enabling centralized management and programmability. NFV virtualizes network functions, allowing them to run as software instances on commodity hardware. Machine learning (ML) techniques enhance SDN and NFV architectures by optimizing network resource allocation, predicting traffic patterns, and improving security measures. However, challenges such as dynamic network conditions, scalability, and data privacy persist. 4/16/2024 15

16. ML and Edge for service and infrastructure optimization. 16 Fig 5. Models 4/16/2024

17. T he fusion of Machine Learning (ML) and Edge Computing revolutionizes service and infrastructure optimization. Edge Computing decentralizes computation, enabling data processing closer to the data source, reducing latency, and conserving bandwidth. ML algorithms deployed at the edge analyze real-time data streams, predict network demand, and optimize resource allocation. This synergy enhances service delivery, enables dynamic network adaptation, and improves user experience. 4/16/2024 17

18. Advantages: Enhanced Performance Optimized Resource Management Predictive Analytics Innovative Applications Personalized Services 18 4/16/2024

19. 4/16/2024 19 Disadvantages: Dependency on Data Quality Vulnerabilities to Adversarial Attacks Model Interpretability Data Privacy Concerns

20. Application : Network Traffic Optimization Security and Threat Detection Dynamic Spectrum Management Edge Computing Optimization Resource Management in IoT Networks Smart Grid Management 4/16/2024 20

21. Results: Analysis of large amounts of data or monitoring the network traffic-in-transit for security for proactive, self-aware and self-adaptive intelligent systems. A security attack or a security lapse happens and then patching starts once the attack or the lapse has been recognized. Deep Packet Inspection (DPI) can be considered as another instantiating technology of ML in security ML-based security systems might provide better results in intrusion prevention systems.. For example, Torabi et. al [60] conducted a study of security systems for DomainNameSystem (DNS)security. 21 4/16/2024

22. Future Research Direction: Future research should explore the integration of ML with edge and fog computing paradigms to enable real-time decision-making and analytics at the network edge. Research is needed to enhance the robustness and security of ML-based communication networks against adversarial attacks, data poisoning, and other threats. This collaborative approach can lead to the development of holistic solutions that address complex challenges spanning multiple domains. 22 4/16/2024

23. Conclusion: First, the applications of ML in wireless networks are described in three layers, i.e., physical, MAC, and network layers. Then the applications of ML are discussed in novel technological concepts such as SDN, NFV, and MEC. An overview of ML in network security is provided, mainly to highlight the need for ML-based security operations in wireless networks. This paper highlights the merger of major research efforts between the disciplines, techniques, and tools of ML and the technologies of communication networks. Since ML has yet to be deployed on a practical basis in wireless networks, there are many interesting open research questions that need further investigation. 23 4/16/2024

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26. 26 4/16/2024 THANK YOU