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Network Security Concentration
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Credits:
15
Concentration Description
The undergraduate concentration in Network Security is for students who want a basic understanding of how to analyze and evaluate network security threats and vulnerabilities to complement their major in Cybersecurity. The course work provides a practical framework for understanding how to analyze network communication protocols, network analysis tools, and hacking methodologies.
The concentration is accepted in any Cambridge College bachelor’s degree, as open electives.
Curriculum
This course covers the ground-up fundamentals of implementing policies, controls, procedures and technologies that work together to protect cloud-based systems, data, and infrastructure. A basic understanding to the cloud and the concepts, and technologies associated with it. The course will delve into not only how public and private cloud security works but the known risk and challenges it can pose for an organization.
This course will cover the fundamentals of wireless security. Students will be introduced to the issues and technologies involved in designing and securing a wireless network from various passive and active attacks.
This course presents an in-depth examination of digital forensics. Students will learn advanced cyber-investigatory techniques including collecting and preserving digital evidence, cybercrime defenses, mobile forensics, network forensics, endpoint forensics, case studies, and the latest forensic tools in the industry to combat cybercrime.
Learners take an advanced look into the world of network intrusion analysis. Advanced intrusion methodologies, incident response, data analysis, malware analysis, network analysis, and advanced reporting are covered within this course.
Cybercriminals are using machine learning to launch large-scale and sophisticated attacks. Machine learning and AI provides the ability to uncover patterns and vulnerabilities that may be difficult for human security team to find. This class teaches students how some businesses are embracing machine learning-based security tools to identify and fight off cyber-attacks.