Computer Science Innovations, LLC

 

Curricula

Course Content

All courses are open source


 
Computer Security

An entire Cybersecurity Curricula to advance skills in protecting your data from known and unknown threats in real time before these threats impact critical business operations.

 
Machine Learning

An entire Machine Learning Curricula to advance skills in analysis and Development of models, tranining and data.

 
Cloud Computing

An entire Cloud Computing Curricula to advance skills in IAAS, PAAS, SAAS and Big Data.

 
Relational Database Design

We work with your cybersecurity infrastructure to protect your data from known and unknown threats in real time before these threats impact critical business operations.

 
Introduction to ORACLE

Introduction in how to use ORACLE and how to program and to do database administration tasks.

 
ORACLE Performance and Tuning

A how to on how to tune ORACLE database tasks. We optmize reading and writing in ORACLE along with fault tolerance and load balancing.

Computer Security - Defeat Hackers and Intruders.


 

 

Courses in Threats, Vulnerabilities and Ethical Hacking
Cyber Security - Quantification and Countermeasures

CCourse Description

This course introduces the participant to the topic of cyber security. It presents today's most critical cyber security vulnerabilities related to data center infrastructure, computer applications and electronic information, as well as principles for identifying and securing such vulnerabilities. The course also introduces best practices for designing application security during the Software Development Life-Cycle.

Course Objectives

Upon completion of this course the participant will be able to: describe discretionary access control. describe mandatory access control. given a system will quantify the security level. identify vulnerabilities in a given system. secure an operating system. secure a database. secure a network. describe the difference between one-way SSL and two-way SSL.

Outline

General Security W3C Information security assurance Discretionary Access Control Mandatory Access Control Auditing Identity assertions Trust and provability Application security Aspect-Oriented Programming (AOP) how interceptors affect security JEE security JAAS SAML Firewall and router IEEE standards TCP wrappers Encryption Asymmetric/Symmetric Performance SSL (Secure Sockets Layer) – a model that works 2-way SSL Adjudication Attacks and Threats Denial of Service (Dos) attacks Firewall and router penetration Spoofing Repudiation Quantifiable Computer Security Trusted Computer System Evaluation Criteria (TCSEC) how it applies to network, database, storage, JEE applications, Cloud Computing and the Semantic Web Internet Security Model Adjudication and Underwriting Footprinting and Social Engineering DNS Enumeration Traceroute ICMP Email Tracking Phishing Attacks URL Obfuscation Social Engineering Countermeasures Scanning and Enumeration Port Scanning Network Scanning Vulnerability Scanning NMAP TCP Communication Flag TYpes OS Fingerprinting Proxy Servers HTTP Tunneling Penetration Testing Metasploit integration Aircrack-NG Kismet Nmap Ophcrack Ettercap Wireshark BeEF (Browser Exploitation Framework) Hydra(software) OWASP Mantra Security Framework Cisco OCS Mass Scanner . Password Cracking Denial of Service Keyloggers Rootkits Trojans, Viruses and Worms and other Malware Trojans Overt Channels Covert Channels Wrapping Sniffing ARP Poisoning Ethereal Display Filters MAC Flooding DNS Spoofing Techniques Denial of Service, Web Applications, SQL Injection and Buffer Overflows Types of Denial of Service Attacks Syn Flooding Smirf Attack Hacking Web Servers SQL Injection Buffer Overflows

Working knowledge of a programming language.. The class time is roughly 30% lab. Participants receive all class materials via the Internet. Attendees may be in class or via the Internet.

Inference Labs analyzes of big sensor data. All the data. in real time.


 

 

STATUS: RESEARCH PHASE
Cyber Security - Quantification and Countermeasures

Industry & academia have fantasized about this for a long time. We continuously stores, monitors and analyzes a wide range of rich signal data together with activity and context.

Inference Labs is an environment that focuses on the continuous store, monitor and analysis of a wide range of rich signal data together with activity and context from a variety of sensors. We use forward and backward propagation in developing networks and models. We use Neural Networks for image processing as well as analysis of scaler data.

Inferene Lab solutions become correspondingly “smarter” as additional data is received – continually yielding more and more valuable output and further improving its clinical analytics capabilities.

Cloud Computing - A Hands-On Approach.


 

 

IAAS, PAAS, SAAS, Web Service and Big Data
Cloud Computing - Defintion and Action

Course Description

A study of the available “cloud computing technologies” in the field of Scalability. Topics include fault tolerance and load balancing at the network, data, and web server level. Introduces students to the technologies, their benefits, and how to leverage them. Class includes labs and optional take home assignments in which students apply the knowledge following real scenarios. Course uses the Java programming language.

Course Objectives

Upon completion of this course the student will be able to: Rapidly deploy scaling and redundancy requirements into an application using existing open source (Apache) technologies. Understand network level load balancing and fault tolerance. Specify two mechanisms of load balancing and fault tolerance in relational databases. Describe fault tolerance and load balancing in Hbase and Hadoop Setup fault tolerance and load balancing in Hbase and Hadoop Demonstrate an understanding of “Map” and “Reduce” Implement Session Replication through Sticky Sessions Explain the components of an “ACID” XA Transaction Choose appropriate Database Replication methods Perform Remote Procedure calls using various different methods Understand the J2EE technology stack Use Jboss, a J2EE server Understand Federation and use both JMS and Beans Implement JbossCache

Outline

Network Load balancing and fault tolerance Network Topology Load Balancing Fault Tolerance Google Map/Reduce distributed computing Comparion with other systems Mapper Reducer Merge Apache Hadoop distributed file system using Map/Reduce History of Hadoop Scaling Combiner Functions Running a Distributed Job HDFS HDFS Concepts Blocks Namenodes and Datanodes Java Interface Reading Data Writing Data Querying Deleting Data Data Integrity Serilization How MapReduce Works Anatomy of a MapReduce Job Run Failures Job Scheduling Data Types and Formats Apache HBase distributed database HBase Overview HBase Data Model Java Clients Example Schemas Example Queries Differences between HBase and RDBMS Database replication Replication Replication in Distributed Systems Modes of Replication Oracle RAC Remote Procedure Calls RMI, Java Remote Method Invocation Serilization Remote Interfaces XML-RPC REST J2EE Java 2 Platform Servlets JSP, Java Server Pages EJB, Enterprise Java Beans JMS, Java Messaging Service JNDI, Java Naming and Directory Interface Jboss, J2EE Server Jboss Features Jboss Setup Using Jboss Federation Enterprise Beans Message Driven Beans JMS as a Resource Temporary Destinations Jboss Cache Cluster Cache Jboss AOP Implementing cloud scaling technologies Developing a MapReduce Application Configuration API Unit Testing Running Locally Running on a cluster Workflows Setting up a Hadoop Cluster Network Topology Cluster setup and installation Hadoop Configuration Security Benchmarking HBase Installation Test Drive

Working knowledge of a programming language.. The class time is roughly 30% lab. Participants receive all class materials via the Internet. Attendees may be in class or via the Internet.

Relational Database Design


 

 

Learn Definitions, Modeling, Implementation and the like
Relational Database Design - Including Real-World Examples

Course Description

This two-day course illustrates how-to take user requirements into a functional database design at the logical and physcial layers. Participants will learn to design optimal relational schemata for real world applications. The course uniquely covers complex data modelling extensions to the rules for normalization. The duration of the course is two days (16 hours).

Course Objectives

Upon completion of this course the student will: Analyze user requirements into a generalized Entity-Relationship Diagram. Understand and incorporate complex modelling issues including subentities, recursive entities, dependent entities and inheritance. Expand Entity-Relationship Diagrams to handle all relationships including Many-to-Many. Incorporate optional attributes. Incorporate of plural attributes. Develop a generalized schema from Entity-Relationship Diagrams. Expand a schema to handle referential integrity. Perform normalization checks. Define physical structures for generalized processing. Take complex user(s) requirements and create a complete fully functional data model including tables definitions and referential integrity.

Topics covered

Lecture 1 Overview of Logical Database Design Lecture 2 Requirements Gathering Lecture 3 Data Modelling Lecture 4 Detailing E-R Diagrams Lecture 5 E-R Diagrams to Tables Lecture 6 Referential Integrity Lecture 7 Normalization Lecture 8 Physical Storage Lecture 9 Implementation Using SQL Lecture 10 In-Depth Workshop and Conclusion

Course Prerequisites, Co-requisites, and/or Other Restrictions

Working knowledge of a programming language.. The class time is roughly 30% lab. Participants receive all class materials via the Internet. Attendees may be in class or via the Internet.

Experienced leadership and a strong vision.

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