Master of Engineering in Cybersecurity Analytics: Program Quick Facts

Completion Time: Approximately 2 years
Delivery: 100% online
Total Credits: 30 credits
Cost Per Credit: $1,195
Total Tuition Cost: $35,850

To Stop Hackers, Learn to Think Like a Hacker

In February 2018, the largest distributed-denial-of-service (DDoS) attack ever recorded affected GitHub, a repository of code and programming knowledge. The following week in early March, that record was broken by another DDoS attack. A similar incident recorded by Amazon Web Services in February 2020 broke that record.

It’s not just the scale of threats that cybersecurity professionals today must deal with, but also the complexity. Over the past decade, the information security community has seen a rise in advanced persistent threats (APTs) — attacks that take place over a long period of time because the attacker is focused on collecting sensitive data for the duration of months and even years. These attacks utilize a mixture of hacking, malware, social engineering and other tactics to gain increased levels of access to sensitive systems; in 2019, multiple APTs were discovered targeting government organizations, consumers and businesses alike.

The STEM-based online Master of Engineering in Cybersecurity Analytics program prepares graduates to step to the forefront of cybersecurity and mitigate the damage caused by incidents like these, as well as classify attacks which experts have yet to identify.

A Cybersecurity Curriculum for Problem Solvers

The online master’s in cybersecurity analytics offers practical experience using cybersecurity and analytics tools to identify and mitigate damage from an array of cyberthreats. The curriculum comprehensively covers traditional methods for intrusion detection and assessing information security risks, while also canvassing new approaches such as semantic analysis of open-source intelligence (e.g., social media).

Some of the key areas covered in the curriculum include:

  • Cyber forensics
  • Network defense
  • Cloud computing security
  • Auditing and intrusion detection
  • Applied cyber data analytics
  • Hardware and software security
  • Security data visualization

The program is designed to establish the foundational technical skills necessary to excel in a cybersecurity career; therefore it is made accessible to individuals who are interested in entering the field. At the same time, experienced professionals will benefit from the opportunity to network with GW’s experienced faculty, share their knowledge with one another and further refine their expertise through practical exercises.

The Master of Engineering in Cybersecurity Analytics is designed for accessibility to both technical and non-technical professionals. Once enrolled, an advisor will create a plan of study that best suits students’ needs and gives them the opportunity to hone their skills and maximize their ability to succeed in the program.

As a result, it is open to professionals without a technical undergraduate degree; however, applicants without a STEM undergraduate background should showcase their interest in cybersecurity and aptitude for technical subject matter through their other application materials.

Admissions Requirements

Ideal candidates for the cybersecurity analytics program will meet the following requirements:

  • Hold a bachelor’s degree in engineering, a physical science, mathematics, computer science, business administration, economics, finance, information technology, or a related discipline from an accredited institution.
  • Minimum grade point average of B- (2.7 on a 4.0 scale) or higher.
  • Applicants with less than a 2.7 GPA are welcome to apply and may be accepted conditionally based on a holistic review of application materials.
  • Grade of C or better in one course in college-level calculus and one course in college-level statistics — applicants who do not meet this requirement in full but are otherwise qualified may be conditionally admitted and required to take an additional 3-credit hour course, EMSE 4197 — Special Topics: Quantitative Methods in Engineering Management, during the first year of graduate study.

Note: GW considers a candidate’s entire background when reaching an admissions decision. Applicants who do not meet these requirements may still be eligible for admission and their records will be evaluated on a case-by-case basis. Please contact an admissions counselor for more information.

Application Materials

To expedite our review of your application for admission, please order your official transcripts and prepare all other materials to submit at the same time as your application. All supporting material must be submitted no later than 45 days from the date of application or the application deadline for the semester for which you are applying.

Complete application packets include:

  • Completed Application
    There is no application fee for this program. When completing the application form, you will also need to upload your resume and statement of purpose.

    • Resume or CV: Upload your up-to-date resume or C.V.
    • Statement of Purpose: In 250 words or less, state your purpose in undertaking graduate study at the George Washington University. Describe your academic objectives, research interests and career plans; present your related qualifications including collegiate, professional and community activities and any other substantial accomplishments not previously mentioned.
  • Official Transcripts: Official transcripts are required from all institutions attended to complete the application packet. More information on transcript requirements can be found on the transcript policy page.
  • Letters of Recommendation: Three letters of recommendation are required for admission. At least two of these letters must come from a professional reference. Please download the letter of recommendation form, fill out the top portion and email the form to the individual providing the recommendation. A letter of recommendation is only considered official when it is sent from the individual providing the recommendation and delivered directly to an admissions counselor via email at onlinecybersec@gwu.edu or via fax at (888) 245-5409. Submissions directly from applicants will not be accepted.
  • GRE Scores: GRE scores are recommended.

If you’re applying from outside the U.S., please see international student admissions information for additional requirements.

Transfer Credit

Academic credit earned at another institution will not be transferred into any online graduate program offered through the Online Engineering Programs.

Curriculum

The GW Master of Engineering in Cybersecurity Analytics offers a comprehensive curriculum which covers the foundational concepts of information security while making in-depth progression regarding the practical application of cybersecurity techniques and tools — including intrusion detection, cyber forensics, network defense and cloud security — along with management and applied security analytics coursework.

In addition to the comprehensive scope of the curriculum, the cybersecurity analytics master’s program offers students the opportunity to learn from industry practitioners. Our faculty have decades of experience working in a variety of cybersecurity functions themselves, where they share their experience from government, non-profit and private sector cybersecurity in their course delivery.

The online format offers the advantage of synchronous or asynchronous delivery, allowing students the flexibility to study when and where it works best for their situations. While students are encouraged to attend and participate, all live lectures are recorded and can be viewed at a later time if needed.

M.Eng Cybersecurity Analytics Required Courses

Apply theory and practice of computer security, focusing specifically on the protection aspects of the Internet. It reviews cryptographic tools to provide security, such as shared key encryption (DES, 3DES, RC and more), public key encryption, key exchange and digital signature (Diffie-Hellmann, RSA, DSS and more). It then reviews how these tools are utilized within the internet protocols and applications like SSL/TLS, IPSEC, Kerberos and more (including wireless). By leveraging case studies and reading seminal research papers, students will learn about network attacks and vulnerabilities as well as current defenses. Topics covered include cryptography, confidentiality and authentication protocols, botnets, firewalls, intrusion detection systems and communication privacy and anonymity. This course also covers offensive and defensive information warfare operations, simulation of various attacks on and defenses of computer systems, laws related to information warfare and history and literature related to information warfare attacks.

Security and privacy issues in cloud computing systems. Confidentiality, integrity and availability of data and computations. Examination of cloud computing models, threat models, outsourcing and security issues. Practical applications of secure cloud computing.

Development and management of effective security systems. Includes information, personnel and physical security. Emphasis on risk analysis for information protection.

Advanced topics in protection of information assets and systems, including authentication, asset control, security models and kernels, physical security, personnel security, operational security, administrative security, security configuration management, and resource control. Prerequisite: EMSE 6540.

Methods for detecting problems with unauthorized activity in information systems and management challenges associated with those activities. Prerequisite: EMSE 6540

Analyzing social media and other publicly available data sources can provide a wealth of data that can be used to identify and evaluate threats to an organization’s information assets. The challenge of using social media and other public sources is filtering the useful information from the noise. Students will use data analytics tools and develop decision support frameworks to identify threats, evaluate capability of actors to exploit vulnerabilities and evaluate the risk of damage those actors can do to an organization. While each individual data source may not provide actionable intelligence, compiling data across multiple sources can reveal critical indications of intent and capability of potential threats. This course provides an overview of publicly available data sources and strategies for mining and aggregating data across multiple sources to build a comprehensive profile of threat sources and develop an action plan to defend against these threats.

Applied and practical data analytics. High-level theory, with primary focus on practical application of a broad set of statistical techniques needed to support an empirical foundation for systems engineering and engineering management. A variety of practical visualization and statistical analysis techniques. Leveraging Minitab and Excel to examine raw data to arrive at insightful conclusions.

Theory and practice of machine learning, leveraging open source frameworks to explore the ideas, algorithms, and techniques. Restricted to graduate students.

The main goal of this course is to help students learn, understand and practice the visualization aspect of security data, which includes the study of data analytics and scaling up information security, security metrics and security monitoring techniques focusing on industry applications. It also covers the fundamentals of security data visualization and exploratory data analysis and provides guidelines on information security data visualization and insights with data dashboards. Furthermore, it introduces valuable tools to empower students to create an effective visual image of security data and prepare security data for using the latest techniques in Information Technology (IT) data analytics fields and extracting features from security data sets. Prerequisite: EMSE 6767

Introduction to programming with Python; Developing python scripts to automate data cleaning; Introduction to machine learning with Python including text mining and time series analysis

Program Learning Objectives

The cybersecurity analytics master’s degree program equips graduates with a blend of technical and business skills to utilize for identifying cybersecurity problems and creating realistic business solutions. The coursework integrates real-world use cases and practical exercises with learning materials designed to give students experience using information security tools and techniques, while preparing them to excel in a variety of cybersecurity careers, including analyst and managerial information security roles.

For example, some coursework requires students to analyze an organization’s response to a cybersecurity incident and identify what technical and business process solutions could have been implemented to mitigate the damage caused.

The following learning objectives outline the design and structure of the program:

  • Lead organizations in cybersecurity, data analytics and forensics
  • Conduct vulnerability assessment of network applications and operating systems
  • Master fundamentals in upcoming issues in hardware security and address system security holistically
  • Become proficient in developing resilient and defendable networks and emerging IT systems
  • Identify and defend against emergent and advanced persistent threats
  • Demonstrate technological proficiency in secure system/hardware design and cyber resilience
  • Understand the key security components of cloud computing