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Deep Learning-Based Anomaly Detection in 5G Cellular Networks 15

Deep Learning-Based Anomaly Detection in 5G Cellular Network...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Farooq, Umar Hameed, Aroosa Leivadeas, Aris Lambadaris, Ioannis École de Technologie Supérieure Department of Software and It Engineering Montreal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350368833

The telecommunication industry saw a dramatic shift with the introduction of 5G technologies. This new cellular generation brought lightening speed, massive capacity, and better connectivity. Despite the many introduced opportunities, several challenges emerge at the same time, especially in the field of performance assurance. An example of such challenges is the rapid identification of network anomalies, which is critical for maintaining network performance and ensuring user satisfaction. Traditional techniques of detecting anomalies have fallen short given the unique operating requirements of 5G networks. To address this issue, this paper presents a novel technique applying deep learning for the detection of cellular network anomalies. Specifically, we leverage and exploit the capabilities of several Long-Short-Term-Memory (LSTM) and Artificial Neural Networks (ANN) flavors, such as LSTM with ANN and Bidirection-LSTM (BiLSTM) with ANN, to excel at identifying network anomalies for the preservation and efficiency of 5G networks. Our work includes extensive experimentation and the attained results show that our proposed models are overwhelmingly adapting to preserving the integrity of the network in the fast-paced, ever-changing realm of cellular networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Systematic Analysis of Public Transit Data Availability in Canada

Systematic Analysis of Public Transit Data Availability in C...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Dick, Kevin Hasan, Azizul Dergham, Jamil Clarke, A.J. Khalil, Hoda Wainer, Gabriel Carleton University Systems & Computer Engineering Ottawa Canada

ISBN: (纸本)9781665480451

Regional authorities will publish public transit route and timetable service offerings through the General Transit Feed Specification (GTFS) as a standard format. Systematically collected GTFS data can be used to study the structure, organization, and availability of public transit services nationally. In this work, we provide a systematic approach to collection of public transit data from various sources, preparation of a high-quality GTFS data inventory and analysis of the public transit offerings lending to numerous insights about transit availability at various geographic scales. Using Canada as a case study, we collected GTFS for the 213 candidate census subdivisions ([CSDs] representing cities, towns, municipalities, etc.) with populations greater than 20,000. These data were then cleaned and leveraged along with CSD-specific census statistics to comprehensively compare public transit offerings between provinces/territories and across CSD types. We determined that, despite a systematic collection process, the majority of CSDs lack official GTFS data, certain provinces are under- or over-represented in our analysis. We further proposed using the median of transit stop spatial density as a national baseline revealing that provinces such as Québec are severely lacking in public transit offerings. GTFS data analysis is insightful for understanding the current state and progress in urban public transportation, which is highly relevant to the United Nation's Sustainability Development Goals. Our aggregated dataset and open-sourced codebase are publicly available at: ***/chazingtheinfinite/canada-transit-study. © 2022 IEEE.

关键词： Urban transportation

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Security-Oriented Network Intent Placement using Particle Swarm Optimization 7

Security-Oriented Network Intent Placement using Particle Sw...

引用

7th Cyber Security in Networking Conference, CSNet 2023

作者： Landeau, Gabriel Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and It Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350342871

As the network infrastructure grows, its configuration and service provisioning become a tedious process. Accordingly, new paradigms have emerged, such as the Intent-Based Networking (IBN), that envision the automation of the network configuration, while minimizing the human intervention. Specifically, IBN allows users to interact with the network through high-level and declarative requests, called intents, which later can be translated into low-level configurations. IBN can entail different scopes and target network infrastructures, while being domain specific, which can create several challenges in terms of the final activation of the requested intents. To this end, in this paper, we mainly focus on intents that are expressing security and Quality of Service (QoS) network services demands that can be translated into Service Function Chains (SFC) and automatically deployed over a campus network. Our work and depending on the security level expressed in the intent, tries to optimally decide the level of multi-tenancy or complete segregation of the users' services that can be achieved, while satisfying the network provider's objectives. In particular, an artificial intelligence inspired algorithm called Particle Swarm Optimization (PSO) is modeled that automatically tries to find the best placement of the intents, while satisfying the security and QoS requirements of the users issuing the intents. © 2023 IEEE.

关键词： Quality of service

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Analysis of Airfare during Pandemic: A Multi-Agent Based Modeling Approach

Analysis of Airfare during Pandemic: A Multi-Agent Based Mod...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Mutakabbir, Abdul Lung, Chung-Horng Ajila, Samuel A. Carleton University Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665480451

The impact of the pandemic on the airline industry has been severe. Various factors such as lockdowns, travel bans, travel restrictions and passenger footfall led to changes in the airfare. This is not limited to a few years of the pandemic as there is a possibility of a similar situation recurring in the future. To address this situation and to assess future possibilities, this paper is an attempt to apply multi-agent simulation and modeling on airfare in pandemic conditions. The objective of this paper is to develop a multi-agent model for airfare during the pandemic. We also ran simulation on the developed model based on the pandemic information available from news articles. The proposed multi-agent model has long-term utility and can be used by the airline industry, the travelers, the governments, academia, and research organizations. © 2022 IEEE.

关键词： Multi agent systems

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Security Patterns for Machine Learning: The Data-Oriented Stages 27

Security Patterns for Machine Learning: The Data-Oriented St...

引用

2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Zhang, Xinrui Jaskolka, Jason Systems and Computer Engineering Carleton University OttawaON Canada

ISBN: (纸本)9781450395946

Security in machine learning (ML) is one of the top priorities in many ML-based systems in the field of healthcare, finance, energy, transportation, and cybersecurity. Since developing ML applications requires multidisciplinary effort, it is important to eliminate knowledge mismatch about security among the team members in the early design phase. In this paper, we present a collection of security patterns for the data-oriented stages in the ML workflow, including data collection, data storage, and data preparation. This provides a concise guidance on how to protect each stage from known threats, as well as a communication vocabulary for different roles to consider security without being security experts. © 2022 ACM.

关键词： Digital storage

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Privacy-Preserving Attribute-Based Access Control with Non-Monotonic Access Structure 7

Privacy-Preserving Attribute-Based Access Control with Non-M...

引用

7th Cyber Security in Networking Conference, CSNet 2023

作者： Ashouri-Talouki, Maede Kahani, Nafiseh Barati, Masoud University of Isfahan Dept. of Information Technology Iran Carleton University Systems and Computer Engineering Canada School of Information Technology Carleton University Canada

ISBN: (纸本)9798350342871

Attribute-Based Encryption (ABE) with non-monotonic access policies provides fine-grained access control for widespread applications like Cloud-assisted HealthIoT systems. In this context, multi-authority ABE with untrusted authorities eliminates the need for a trusted authority, but ensuring user's identity and attributes-set privacy against these authorities remains a significant challenge. This paper proposes a new, efficient multi-authority ABE approach that preserves user's identity privacy and attributes-set privacy, and is secure against collusion attack. Also, the proposed approach provides non-monotonic access policies, which supports positive and negative constraints using NOT operation as well as AND and OR operations. © 2023 IEEE.

关键词： Access control

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Emergence of an Autonomous Vehicle Secondary Data Market for Breakthrough Applications

Emergence of an Autonomous Vehicle Secondary Data Market for...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Dick, Kevin Green, James R. Carleton University Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665480451

The prophesied circulation of fleets of autonomous vehicles (AVs) in urban and rural environments promises unprecedented opportunities to remotely sense streetscapes at fine-grain spatial and temporal resolution. AVs employ a variety of on-board sensors to capture information about the local environs for the primary purpose of vehicular navigation. However, we propose that these data may find further secondary use in a broad array of breakthrough applications: technologies and use cases that are enabled through the fine-grain spatio-temporal sensing of the lived environment. Consequently, a market for the secondary use of AV-collected data is emergent and a cloud-based architecture to manage the collection, processing, and communication of AV-derived data is required. Excitingly, the application of machine learning models to extract desirable secondary information from these fine-grain spatio-temporal data will enable unprecedented global-scale and time-series studies. Herein, we outline our vision for the utility of a Remote sensing AV-based Informatics Layer (RAIL) and the breakthrough applications it would enable. We define our vision based on recent and relevant trends in AV technology, discuss anticipated applications, discuss key technical considerations, and explore theoretical economic models for the exposed API. We conclude with discussion of the socio-technical ramifications of this system. © 2022 IEEE.

关键词： computer vision

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Robust Data Exchange Across Wind Turbines 5

Robust Data Exchange Across Wind Turbines

引用

5th International Conference on computer Communication and the Internet, ICCCI 2023

作者： Liu, Xian Huang, Changcheng University of Arkansas at Little Rock Department of Systems Engineering Little Rock United States Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350326956

Wind farms play an important role in the smart grid initiative. Most wind turbines are deployed in open areas and their wireless communication layer needs to be robust. In this paper, the concept of information-theoretic secrecy is introduced to establish some benchmarks of robust communication. Compatible with the common topology of wind turbine deployment, the secrecy analysis is conducted in the framework of cooperative communication with a generic relay configuration. The analysis takes several issues into account, such as fading, line-of-sight, and multi-scattering. We derive several benchmark metrics for this system. These metrics are explicitly expressed in the closed-form. The availability of these solutions helps directly gain the deep insights, while reducing the computational load of tedious simulations. © 2023 IEEE.

关键词： Wind turbines

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IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis 10

IoT Sensor Deployment in the Wildland Urban Interface: Lever...

引用

10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Purcell, Richard Naik, Kshirasagar Zaman, Marzia Lung, Chung-Horng Sampalli, Srinivas Mutakabbir, Abdul Dhindsa, Manavjit Singh Das, Ursula Dalhousie University Faculty of Computer Science HalifaxNS Canada University of Waterloo Electrical and Computer Engineering WaterlooON Canada Research and Development Cistel Technology OttawaON Canada Carleton University Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350373011

This paper investigates algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Using geospatial data analysis and a validated wildland fire growth model, we generated burn maps to guide sensor placement strategies across a defined region. We evaluated even grid distribution, random distributions, and genetic algorithm-based methods, testing each against 50,000 burn maps with sensor counts ranging from 50 to 800. Results indicate that while even grid distribution achieved the highest detection rates, its practicality in real-world applications is limited. Genetic algorithms showed promise but require further exploration to simulate field deployment accurately. Notably, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, highlighting the need for further research into the representativeness of burn maps. © 2024 IEEE.

关键词： Genetic algorithms

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Emission Mitigation Dispatch in Wind Power Generation with Expedited Machine Learning 10

Emission Mitigation Dispatch in Wind Power Generation with E...

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10th International Conference on Power and Energy systems engineering, CPESE 2023

ISBN: (纸本)9798350327625

In this paper: a machine learning (ML) module is presented for emission mitigation dispatch (EMD) in wind power (WP) generation. It is developed for the mobile edge computing (MEC) platforms. Therefore, the module is based on a lightweight ML scheme: radial basis neural network (RBN). It has been well known that RBN is compact compared with a more popular ML scheme: feedforward neural network. However, this is the first study to combine EMD with RBN, strongly motivated by the MEC implementations. MEC is becoming an important platform in the evolving smart grid program. As a trial, a case study with 36 generators and 50 wind turbines is presented. Two standard ML stages, training and testing, are conducted in the simulation. It is shown that, by using the trained RBN, the time of solving EMD is significantly reduced. It is expected to be a desirable model for MEC. © 2023 IEEE.

关键词： Wind power

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