Networks & Security
Network engineering and defense are increasingly automated: telemetry is triaged by machine-learning detectors, configuration and remediation are drafted by AI agents, and those agents become part of the attack surface through tool-calling integrations and supply-chain exposure. The course keeps its networking identity while adding the verification, threat-modeling, and detection-evaluation skills practitioners now need.
Current catalog prerequisites — (CSC 250 or 2050).
Current description → proposed description
This course is an in-depth view of data communication and networking ranging from the primitive historical approaches to the ever changing modern state of the field. It includes principles of network design, using a top-down approach and focusing on technologies used in the Internet. It will help students learn to design network-aware applications using sockets, threading, and concurrency. This course will help students understand how the Internet works, from the transport layer down to the physical layer. It will help students prepare for future positions in research and development by introducing them to the latest research in Internet technologies. This course will help students become better writers by emphasizing written work where possible. Finally, CSC 3610 will also help students apply networking technology in ways that can enrich their lives and assist in spreading the gospel.
Prerequisites: (CSC 250 or 2050).
This course examines data communication and computer networking from its historical foundations to the present state of the field, using a top-down approach centered on the technologies of the Internet. Students study network design principles and the protocol stack from the transport layer to the physical layer, and they build network-aware applications using sockets, threading, and concurrency. Security is treated as a design concern throughout, including segmentation, authentication, encryption in transit, and traffic monitoring. Because network operations and defense are increasingly mediated by automated agents, the course also addresses machine-learning traffic classification and anomaly detection, the verification of AI-generated configuration and code before deployment, and the attack surface that agentic tooling introduces. Written communication is emphasized in design documents and incident reports. Students consider the stewardship of networked infrastructure and the ways networking technology enriches lives, serves neighbors, and assists in spreading the gospel.
What changes
- Security elevated to an explicit, course-long strand matching the catalog title
- Threat modeling extended to agentic tooling, prompt injection, and supply-chain risk
- Operational evaluation of ML-based traffic classification and anomaly detection
- Verification of AI-generated network configuration and code before deployment
- Design documents and incident reports that disclose and verify AI contributions
6 proposed outcomes, mapped to 9 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to analyze the end-to-end behavior of the Internet protocol stack from the transport layer to the physical layer, using packet captures and performance measurements to explain how addressing, routing, and congestion control shape application outcomes.
Mapped on the analytical-method reading of PLO 6.2: packet-capture measurement and the inference of congestion, loss, and latency behavior from it is the quantitative and qualitative analysis the PLO describes; the PLO's 'AI patterns' framing does not apply to this outcome, and CLO 4 supplies the AI-pattern evidence for 6.2.
Students will be able to implement a concurrent, network-aware application using sockets, threading, and transport-layer protocols, accepting AI-generated code and configuration only after verifying it against the relevant protocol specification and its security implications.
Judging whether AI-generated code and configuration actually satisfy the protocol specification and its security implications before accepting them is the evaluation of AI-driven solutions named in PLO 4.2.
Students will be able to design layered defenses for a networked system, including segmentation, authentication, and encryption in transit, together with a threat model that accounts for agent-driven automation, prompt injection through tool-calling integrations, and supply-chain risk in network tooling, and that assesses the consequences for users and third parties if an agent in that system is compromised.
The threat model the CLO requires must state what a compromised agent could do to users and third parties, which is the critical analysis of data-driven outcomes and their societal consequences that PLO 4.1 names.
Students will be able to evaluate the operational behavior of an existing intrusion-detection or traffic-classification system by examining alert volume, detection thresholds, false-positive cost, and symptoms of drift, and by weighing the privacy implications of the monitoring it requires.
The detector's alerts and scores are AI data outcomes the student must interpret and explain, which is PLO 6.1; reasoning from alert volume, thresholds, and drift symptoms to a judgment about the system is the quantitative and qualitative analysis of AI patterns in PLO 6.2; and weighing who bears the cost of false positives against the privacy intrusion the monitoring requires is the assessment of an AI system for fairness and moral impact in PLO 6.3.
Students will be able to compose network design documents and incident reports that convey findings accurately to both engineering and non-specialist audiences and that disclose where AI tools contributed and how their output was verified.
Producing written design and incident documentation with explicit disclosure of AI assistance is the transparent, high-integrity communication of PLO 5.1, and writing the same findings for a non-specialist audience is the translation work of PLO 5.3.
Students will be able to articulate how decisions about network access, cost, and reliability determine whether under-resourced communities can reach AI-dependent services, framing the stewardship of networked infrastructure as a Christian vocation that serves the neighbor and supports the mission of the church.
Naming the stewardship of networked infrastructure as a calling that serves the neighbor and the church's mission is the vocation claim of PLO 1.3, and the CLO's own requirement to show how access, cost, and reliability decisions determine whether under-resourced communities can reach AI-dependent services is the equitable-access and responsible resource management concern of PLO 2.3.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all eighteen.