CN Syllabus.docx
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This document outlines the objectives, units, outcomes, textbook, and references for the course CS8591 Computer Networks. The objectives are to understand protocol layering, network performance analysis, and components required to build networks. The five units cover introduction to physical layer, data link layer protocols, network layer protocols, transport layer protocols, and application layer protocols. The outcomes are for students to understand computer network concepts, analyze network performance, implement routing protocols, and understand various application layer protocols.
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This document outlines the objectives, units, outcomes, textbook, and references for the CS6551 Computer Networks course. The objectives are to understand network layering, components required for different network types, functionality at each layer, and flow and congestion control algorithms. The 5 units cover fundamentals and link layer, media access and internetworking, routing, transport layer, and application layer. At the end of the course students should be able to identify components for different networks, functionality at each layer, solutions for each layer, and trace information flow between nodes.
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This document outlines the course objectives and units for a communication networks course. The course aims to help students understand network layering and protocols. It is divided into 5 units covering fundamentals of networking, media access and internetworking, routing, transport layer protocols, and the application layer. Students will learn about components of different network types, functionality at each layer, routing algorithms, transport protocols like TCP and UDP, and applications like HTTP, DNS, email and network security. The course objectives are to identify network components, functionality at layers, and trace information flow end-to-end.
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This document outlines the objectives, units, outcomes, textbook, and references for the CS6551 Computer Networks course. The objectives are to understand network layering, components required for different network types, functionality at each layer, and flow and congestion control algorithms. The 5 units cover fundamentals and link layer, media access and internetworking, routing, transport layer, and application layer. At the end of the course students should be able to identify components for different networks, functionality at each layer, solutions for each layer, and trace information flow between nodes.
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Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
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Presentation at IcETRAN 2024 session:
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Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, SerbiaDEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Eric Nizeyimana's document 2006 from gicumbi to ttc nyamata handball play
this notes have been created
by Eric36 at nyamata ttc
after readings
pe book for y2sme .
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2:同时对留学生所学专业登记给予评定
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4:这个认证书并且可以归档倒地方
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6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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