Understanding how different honeypot levels affect data collection helps you see why this level is the one that yields richer attacker information. Low-interaction honeypots mimic only a few services and log basic contact data—IP addresses, ports, and simple banners—so you see who is probing but not how they move or what tools they use. High-interaction honeypots expose a real, fully functional system, allowing attackers to perform complex actions and revealing extensive details about techniques and tools—but this comes with higher risk and more management overhead. Medium-interaction honeypots strike a middle ground. They simulate more realistic services and permit meaningful, limited user interactions. Attackers can try commands, access files, or deploy common utilities, which generates deeper telemetry than a low-interaction setup. You collect richer data: the specific techniques attempted, the tools used, command sequences, and the progression of an attack, without giving an attacker full control of a real system. This level provides enough detail to map out an attack vector comprehensively while keeping the environment safer and easier to manage than a high-interaction deployment. So, the deeper, actionable visibility into attacker behavior and tool usage that medium-interaction honeypots provide is why this choice is the best fit for capturing complete information about a given attack vector.

In IoT architecture, the Device Layer covers the physical things and the immediate means they use to connect. It includes the sensors, actuators, and other devices, along with the communication methods that let them talk to gateways or edge devices. This means the protocols themselves (like MQTT, CoAP, Zigbee, Bluetooth) and the networks (Wi‑Fi, cellular, LPWAN) that carry data to the next layer. Edge computing can be part of this layer as data is processed near the source before moving on. That’s why describing the device-side communication protocols and networks used for connectivity and edge processing best fits the Device Layer. Statements about user interfaces for remote control relate to how humans interact with the system, which sits in a higher layer. Cloud servers for storage and analytics belong in the cloud/processing layer, not the device layer. Dashboards for monitoring and proactive decisions are part of visualization/decision-support at the application level.

Trojan protection is provided by antivirus/anti-malware suites that detect, block, and remove Trojan horse malware in real time. McAfee LiveSafe is a security suite that includes comprehensive real-time malware protection, signature and heuristic detection, behavior monitoring, and automatic updates. These capabilities specifically target Trojan threats, enabling the software to identify known trojans, block suspicious activity, and quarantine or remove malicious files as part of ongoing protection. Because of its broad, integrated protection against trojans, it serves as a clear example of anti-Trojan software. While other options also offer Trojan protection, this one is commonly highlighted as a representative anti-Trojan solution.

Proactive approach emphasizes forecasting threats and shaping defenses before an attack occurs. It uses threat intelligence, lessons from past incidents, vulnerability assessments, and risk modeling to guide decisions about where to invest in controls, how to architect the network, and which defenses to tune. By turning these insights into actionable plans, it informs countermeasures for future attacks on the target network. While preventive measures aim to stop threats upfront, retrospective analysis looks at what happened after an incident, and detection controls focus on identifying ongoing activity. None of these emphasizes forward-looking decision making as strongly as the proactive approach.