Cybersecurity Analytics

Master of Engineering in Cybersecurity Analytics: Program Quick Facts

Master the Hacker's Mindset, Secure Your Cyber Frontiers

In the early months of 2018, GitHub, a central hub for code and programming resources, was hit by an unprecedented DDoS attack, setting a new record for the magnitude of such cyber assaults. However, this record was short-lived, as another, even more powerful DDoS attack occurred the following week in March. This upward trend in the scale of cyber threats continued, with Amazon Web Services reporting an even larger DDoS attack in February 2020, surpassing previous incidents.

The challenges facing cybersecurity experts are not only growing in scale but also in intricacy. The past ten years have seen a surge in the prevalence of advanced persistent threats (APTs). These are sophisticated cyber attacks that unfold over extended periods, often aiming to extract sensitive information over many months or years. APTs are particularly insidious, employing a combination of techniques including hacking, malware, social engineering, and others to penetrate and persist in targeted networks. In 2019, these types of threats became increasingly common, with discoveries of APTs aimed at government entities, individual consumers, and various businesses.

Addressing these complex and evolving cyber threats, the STEM-based online Master of Engineering in Cybersecurity Analytics program is designed to equip graduates with cutting-edge skills and knowledge. The curriculum aims to enable graduates to tackle current cybersecurity challenges effectively and also to anticipate and defend against new types of attacks that have not yet been encountered, ensuring they are at the forefront of the field in protecting against such critical incidents.

A Cybersecurity Curriculum for Problem Solvers

The Master's program in cybersecurity analytics provides hands-on training in employing cybersecurity and analytical tools to detect and counteract a wide range of cyber threats. The curriculum thoroughly encompasses established techniques for detecting intrusions and evaluating information security risks, while also exploring modern methods like the semantic analysis of open-source intelligence, including platforms like 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 Cybersecurity Analytics Master's program builds essential skills for cybersecurity careers, welcoming both newcomers and seasoned professionals. Experts benefit from networking and skill refinement through practice, while beginners receive tailored guidance from GW faculty.

Open to diverse academic backgrounds, applicants without a STEM degree should highlight their cybersecurity interest and technical aptitude in their application to demonstrate their readiness for the field.

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 may also 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 all the 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 [email protected] 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 Cybersecurity Analytics Master's program delivers a robust curriculum that not only introduces the basics of information security but also delves into the hands-on application of cybersecurity strategies and tools. Students gain expertise in areas like intrusion detection, cyber forensics, network defense, cloud security, management practices, and applied security analytics.

Beyond the extensive curriculum, students benefit from the real-world expertise of faculty members who bring decades of experience from various cybersecurity roles across government, nonprofit, and private sectors, providing students with insights into the industry's practical aspects.

The program's online structure provides the flexibility of both live (synchronous) and recorded (asynchronous) class sessions, accommodating students' diverse schedules and personal commitments. While live participation is encouraged for the most engaging experience, lectures are recorded, granting the ability to revisit the material as needed.

M.Eng Cybersecurity Analytics Required Courses

CSCI 6016 Applied Network Defense

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.

ECE 6132 Secure Cloud Computing

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.

EMSE 6540 Management of Information and Systems Security

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

EMSE 6543 Managing the Protection of Information Assets and Systems

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.

EMSE 6544 Auditing, Monitoring and Intrusion Detection for Information Security Managers

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

EMSE 6560 Open-Source Intelligence Analysis

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.

EMSE 6767 Applied Data Analytics

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.

EMSE 6769 Applied Machine Learning for Engineers

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

SEAS 6410 Security Data Visualization

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

SEAS 6800 ST Python Applications in Cybersecurity Analytics

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 master's degree program in cybersecurity analytics arms graduates with an integrated set of technical and business competencies, essential for recognizing and tackling cybersecurity challenges with pragmatic business strategies. Through a curriculum that melds hands-on exercises with theoretical instruction, students become adept in applying information security tools and methodologies, readying them for success in diverse cybersecurity positions, from analytical to managerial roles within the information security sphere.

The educational goals of the program are:

• 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