COE Dept. Seminars - By MS Students - Shihab Hasan and Mohammed Naser Haidari
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College of Computing and Mathematics
Computer Engineering Department
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Presents Public Seminar
Date: Wednesday, April 5, 2023
Time: 02:30 PM – 04:00 PM
Location: Bldg. 22, Room 132
Seminar 1
Speaker:
Mr. Shihab Hasan
MS Student, Computer Engineering Department
KFUPM
Title: A Queuing Theory Model for Fog Computing
Abstract:
Under many scenarios where resources may be scarce or a good quality of service is a requirement, appropriately sizing components and devices is one of the main challenges. New scenarios, such as IoT, mobile cloud computing, mobile edge computing, or fog computing, have emerged recently. The ability to design, model, and simulate those infrastructures is critical to dimensioning them correctly. Queuing theory models provide a good approach to understanding how a given architecture would behave for a given set of parameters, thus helping to detect possible bottlenecks and performance issues in advance. This work presents a fog-computing modeling framework based on queuing theory. The proposed framework was used to simulate a given scenario allowing the possibility of adjusting the system by means of user-defined parameters. The results show that the proposed model is a good tool for designing optimal fog architectures regarding QoS requirements. It can also be used to fine-tune the designs to detect possible bottlenecks or improve the performance parameters of the overall environment.
Speaker Bio:
Mr. Shihab Hasan is currently pursuing his MSc degree in Computer Engineering at KFUPM. He obtained his BSc in Computer Networks and Distributed Systems from Taiz University in 2020. His areas of specialization and keen interest include computer networking, cybersecurity, and fog computing.
Seminar 2
Speaker:
Mr. Mohammed Naser Haidari
MS Student, Computer Engineering Department
KFUPM
Title: Intrusion Detection in IoT Using Deep Learning
Abstract:
Cybersecurity technologies have been widely deployed in various application domains such as intelligent industrial systems, homes, personal devices, and cars. This has led to innovative developments that continue to face challenges in solving problems related to security methods for IoT devices. Effective security methods, such as deep learning for intrusion detection, have been introduced. Recent research has focused on improving deep learning algorithms for improved security in IoT. This research explores intrusion detection methods implemented using deep learning. It also compares the performance of different deep learning methods and identifies the best method for implementing intrusion detection in IoT. This research is conducted using deep learning models based on convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). A standard dataset for intrusion detection in IoT is considered to evaluate the proposed model. Finally, the empirical results are analyzed and compared with the existing approaches for intrusion detection in IoT. The proposed method has the highest accuracy compared to the existing methods.
Speaker Bio:
Mr. Mohammed Naser received his Bachelor's degree in communication engineering in 2017 from International Islamic University Malaysia. From 2019 to 2022, he worked in a telecommunication company as an engineer. In August 2022, Mr. Mohammed joined KFUPM as a graduate student in the MSc program in Computer Networks. His interests include network security, Intrusion detection, and cloud computing.
All faculty, researchers and graduate students are invited to attend.
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Computer Engineering Department, College of Computing and Mathematics
Telephone: +966 (13) 860 2110, Email: c-coe@kfupm.edu.sa, Website: www.kfupm.edu.sa/departments/coe/
Copyright © 2014 King Fahd University of Petroleum & Minerals
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