Deep learning,5G,security
225525817
Deep Learning at the Mobile Edge: Opportunities for 5G Networks
Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.
Miranda McClellan Cristina Cervello-Pastor Sebastia Sallent
225128544
UbiPriSEQ Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets
5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy efficiency, security, and privacy. UbiPriSEQ is built on a three layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.
Thaha Mohammed Aiiad Ahmad Albeshri Iyad A Katib Rashid Mehmood
215745543
Multi stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks
In 5G networks, the Cloud Radio Access Network (C RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources effi ciently by providing real time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C RAN networks has attracted significant attention. Among various anomaly based intrusion detection techniques, the most promising one is the machine learning based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi stage machine learning based intrusion detection (ML IDS) in 5G C RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN DS (Wireless Sensor Networks DataSet) which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.
2020 International Symposium on Networks, Computers and Communications (ISNCC)
Marouane Hachimi Georges Kaddoum Ghyslain Gagnon Poulmanogo Illy
211207936
DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks
Existing cellular communications and the upcoming 5G mobile network requires meeting high reliability standards, very low latency, higher capacity, more security, and high speed user connectivity. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end to end network resource allocation using the concept of Network Slicing (NS) Data driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. In this paper, we have developed a 'DeepSlice' model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in network deep learning and prediction. We use available network Key Performance Indicators (KPIs) to train our model to analyze incoming traffic and predict the network slice for an unknown device type. Intelligent resource allocation allows us to use the available resources on existing network slices efficiently and offer load balancing. Our proposed DeepSlice model will be able to make smart decisions and select the most appropriate network slice, even in case of a network failure.
2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON)
Anurag Thantharate Rahul Arun Paropkari Vijay Walunj Cory C Beard
219690114
Deep Learning in 5G Wireless Networks Anomaly Detections
The year of 2020 is critical for global implementation of 5G wireless networks. While enjoying a whole new level of user experience in 5G networks, such as high data rate, low latency and virtually everything to everything connections, the ever growing diversity, complexity of network and data traffics impose a set of new challenges for effectively operating and managing 5G networks. As our daily lives are more dependent on mobile devices and apps, so does the cyber security risk and venerability increase. Many of the algorithms, protocols and practices used to safeguard 4G networks fall short for 5G networks without degrading the performance expected for 5G networks. In this paper we report our early research results of using deep learning algorithms for anomaly detection in 5G network while minimizing the impacts to network latency. We developed a prototype model using U Net and conducted a simulation experiment with a well known botnet dataset to evaluate the suitability and performance.
2020 29th Wireless and Optical Communications Conference (WOCC)
Minh Tuan Doan Zhanyang Zhang
121370288
An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks
A Network Intrusion Detection System is a critical component of every internet connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.
2019 53rd Annual Conference on Information Sciences and Systems (CISS)
Shahadate Rezvy Yuan Luo Miltos Petridis Aboubaker Lasebae Tahmina Zebin
232151549
Deep Reinforcement Learning based Cloud native Network Function Placement in Private 5G Networks
With the advantages of satisfying service requirements and providing high security, standalone private fifth generation (5G) network is perceived as a promising technology for vertical industries. However, to manage the cloud native network functions (CNFs) in an effective manner, a sophisticated control plane management scheme should be designed in standalone private 5G networks. In this paper, we propose a deep Q network based CNF placement algorithm (DQN CNFPA) that jointly minimizes the cost occurred in launching and operating CNFs on edge clouds and the back haul control traffic overhead. In addition, DQN CNFPA learns spatiotemporal patterns in service requests and places CNFs in consideration of future cost leveraged by the previous CNF placement strategy. Evaluation results demonstrate that DQN CNFPA can reduce the cost per hour up to 11.2% compared to the scheme without learning spatiotemporal service request patterns.
2020 IEEE Globecom Workshops (GC Wkshps
Joonwoo Kim Jaewook Lee Taeyun Kim Sangheon Pack
232413395
Deep Learning for Security Problems in 5G Heterogeneous Networks
With increasingly complex network structure, requirements for heterogeneous 5G are also growing. The aim of this study is to meet the network security performance under the existing high capacity and highly reliable transmission. In this context, deep learning technology is adopted to solve the security problem of the 5G heterogeneous network. First, the security problems existing in 5G heterogeneous networks are presented, mainly from two aspects of the physical layer security problems and application prospects of deep learning in communication technology. Then the combination of deep learning and 5G heterogeneous networks is analyzed. The combination of deep learning technology, modulation information recognition, and beam formation is introduced. The application of deep learning in communications technology is analyzed, and the modulation information recognition and beamforming based on deep learning are introduced. Finally, the challenges of solving security problems in 5G heterogeneous networks by deep learning are explored. The results show that the deep learning model can solve the modulation recognition problem well, and the modulation mode of the convolutional neural network can well identify the modulation signals involved in the experiment. Therefore, deep learning has a good advantage in solving modulation recognition. In addition, compared to the traditional algorithm, the unsupervised beamforming algorithm based on deep learning proposed in this research can effectively reduce the computational complexity under different numbers of transmitting antennas, which verifies the superiority of the unsupervised beamforming algorithm based on deep learning proposed in this research. Therefore, the present work provides a good idea for solving the security problem of 5G heterogeneous networks.
IEEE Network
Zhihan Lv Amit Kumar Singh Jinhua Li
232072630
A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security
The current development of 5G technology is flourishing with widespread deployment across the world at a rapid pace. However, there is still a demand concerning 5G research for service and performance improvement. Research tasks include but are not limited to quality of service (QoS) energy efficiency, massive connectivity, reliable communications, and security. Due to the advancement of deep learning, numerous such research has utilized this technique. This article provides a comprehensive review of 5G communications research using deep learning. Specifically, we address the issues of low density parity check (LDPC) coding, massive multiple input multiple output (MIMO) non orthogonal multiple access (NOMA) resource allocation, and security.
IEEE Open Journal of the Communications Society
Amanda Ly Yu-dong Yao
235558528
Deep Learning and Blockchain Empowered Security Framework for Intelligent 5G Enabled IoT
Recently, many IoT applications, such as smart transportation, healthcare, and virtual and augmented reality experiences, have emerged with fifth generation (5G) technology to enhance the Quality of Service (QoS) and user experience. The revolution of 5G enabled IoT supports distinct attributes, including lower latency, higher system capacity, high data rate, and energy saving. However, such revolution also delivers considerable increment in data generation that further leads to a major requirement of intelligent and effective data analytic operation across the network. Furthermore, data growth gives rise to data security and privacy concerns, such as breach and loss of sensitive data. The conventional data analytic and security methods do not meet the requirement of 5G enabled IoT including its unique characteristic of low latency and high throughput. In this paper, we propose a Deep Learning (DL) and blockchain empowered security framework for intelligent 5G enabled IoT that leverages DL competency for intelligent data analysis operation and blockchain for data security. The framework's hierarchical architecture wherein DL and blockchain operations emerge across the four layers of cloud, fog, edge, and user is presented. The framework is simulated and analyzed, employing various standard measures of latency, accuracy, and security to demonstrate its validity in practical applications.
IEEE Access
Shailendra Rathore Jong Hyuk Park Hangbae Chang