Design and Analysis of Algorithms

Introduce some preliminary material, including asymptotics, summations, and recurrences and sorting. Discuss approaches to designing optimization algorithms, including dynamic programming and greedy algorithms. Review of breadth-first and depth-first search and their application in various problems related to connectivity in graphs. Discuss minimum spanning trees, shortest paths, and network flows. Discuss algorithmic problems arising from geometric settings; i.e., computational geometry. Discuss intractability and NP-hard problems. Discuss methods to approximate NP-hard problems, and how to prove how close these approximations are to the optimal solutions.

Distributed Systems and Network Programming

Present the principles, advanced concepts, and technologies of distributed systems in detail. Understand and implement various client-server network designs. Understand and implement various network programming concepts such as naming, storage systems, replication, fault tolerance, and security. Learn how to implement multithreading in distributed systems. Understand and implement the process of accessing and handling various types of files. Study real-word examples and case studied including NFS, CORBA, DCOM, Jini and the World Wide Web.

Information and Software Security

Begin with an introduction to cryptography and its applications. Discuss the fundamentals of secret key and public key cryptography (without delving into the details of specific algorithms). Understand the topics of hash functions, authentication, digital signature, digital certificate, key management, SSL/TLS, and IP security. Present the various types of software vulnerabilities and threats and the corresponding countermeasures. Study the vulnerability classes and classifications to understand how to protect and secure software. Present the analysis and design techniques used for improving software security, as well as how to use these techniques and tools to improve and verify software designs and security. (Secure software design and coding, software assurance, secure software engineering lifecycle, software security testing …)

System and Network Security

Provide the students with a good understanding of different security aspects related to systems and computer networks. Delve into the details of firewalls and intrusion detection systems (Firewall designs/architectures and Host-based and Network-based Intrusion Detection Systems). Present the attacks to different network layers (application, transport, network, and link layer) and their countermeasures. Discuss the methods and types of attacks on different types of networks (such as denial-of-service attacks; password cracking; key loggers; buffer overflow attacks; sniffing; SNMP; scanning; and fingerprinting) and ways to defend against them. Review the general knowledge and techniques for streaming data traffic, such as VoIP and multimedia: the security challenges unique to such traffic will be covered in detail, including disruption of service, theft of service, and violation of confidentiality. 

Introduction to Big Data

Understand the architectures of relational and non-relational database systems and the different data models they can support. Become familiar with the expanding role of new database technologies. Learn new ways to query and model data and Big Data. Understand selected topics on Big Data and NoSQL databases, namely: Big Data revolution, non-structural databases, column databases and multi-version data. Get familiar with document and graph databases, NoSQL and NewSQL, and Web databases. Apply with basic case studies on various NoSQL and Big Data tools such as: MongoDB, CouchDB, Neo4j… (document, key-value pairs, graph) + tools for data integration (import/export, migration…)

Data Science and Machine Learning

Understand the basics of machine learning. Understand ML problems and algorithms. Understand the role of statistics in ML and data analysis. Understand ML applications on mining and analysis of various data types: text, images… Understand Neural Networks (NN), Deep NN, Convolution NN and their application on ML. Become able to consider the pros and cons when choosing ML / AI methods for different applications. Get experience with different ways of using a data set for training and testing (training using TensorFlow). Build a fundamental knowledge on Big Data Analytics to handle various real-world challenges.

Advanced Networking

Learn the basics of Software Defined Networks (SDN) and Network Function Virtualization (NFV). Learn the fundamentals of Wireless networks and study mobility issues at the network and transport layers (Mobile IP and micro mobility protocols, TCP in wireless environments). Study the various “Device to Device” Communication Architectures and explore the algorithms and protocols designed for MANET, mesh, cellular and opportunistic networks. Explore various Content based Network Architectures and understand the principles of data dissemination, aggregation and caching that are applied to sensor networks, Internet of Things and other content based paradigms. Explore the characteristics of Data center and high-performance networking. Understand the concepts and details of Network virtualization. Study the characteristics of IoT networks and IoT communication patterns, and explore some IoT protocols such as MQTT and oneM2M. Understand and analyze various types of modern network applications, such as social networks and Cloud computing network applications. Perform network simulations and experimentation using the Network Simulator -v3 (ns3).

Research Methods

Cloud and IoT Security

Learn the fundamentals of cloud computing architectures based on current standards, protocols, and best practices intended for delivering Cloud based enterprise IT services and business applications. Identify the known threats, risks, vulnerabilities and privacy issues associated with Cloud based IT services. Understand the concepts and guiding principles for designing and implementing appropriate safeguards and countermeasures for Cloud based IT services. Design security architectures that assure secure isolation of physical and logical infrastructures including compute, network and storage, comprehensive data protection at all layers, end-to-end identity and access management, monitoring and auditing processes and compliance with industry and regulatory mandates. Understand the industry security standards, regulatory mandates, audit policies and compliance requirements for Cloud based infrastructures. Learn current security issues and identify Threats, Vulnerabilities and Risks related to the IoT. Gain familiarity with fundamental cryptographic controls needed to safeguard IoT systems. Walk through a real-world threat model for IoT systems and identify top priority risks. Incorporate privacy controls into the new IoT systems’ designs. Discuss privacy regulations and standards that apply to securing IoT systems and keeping stakeholder information private. Plan to leverage the cloud to securely support the discussed IoT system.

CSC649-Advanced Relational Databases and Business Intelligence

Review in depth the architecture of a database system and the different data models it can support. Understand and criticize different types of database applications and their design. Understand, analyze, and develop efficient algorithms for operating on the data models, and build-upon their implementation in modern databases. The course introduces advanced database topics such as normalization, query optimization, transaction processing, concurrency control, database recovery, database indexing, data replication, and database consistency. Overview and application on Relational Business Intelligence tools.

Big Data Analysis and Machine Learning

Design and apply large-scale machine learning methods. Handle analytics algorithms on different platforms. Apply using software tools such as Python, R. Understand Big data visualization issues. Visualize big data using appropriate tools. Discuss mobile issues in Big Data Analytics (Mobile Big Data Analytics). Apply several methods to optimize the analytics based on different hardware platforms, such as Intel & Power chips, GPU, FPGA, etc. Discuss future challenges of Big Data, especially on the ongoing Linked Big Data issues that involve graphs, graphical models, spatio-temporal analysis, cognitive analytics, etc.