# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 3 IoT Vulnerability Layers
### Network Vulnerabilities
#### 3.3.3 Cloud Based Networks  
When delving into network vulnerabilities within the Internet of Things (IoT), one must recognize the transformative role of the IoT cloud computing architecture--a recent and vital player in this domain. As standalone IoT technology struggles to meet the growing demands of users and their computational needs, the integration of IoT into cloud computing resources emerges as a powerful solution. This synergy combines the sensing and connectivity of IoT devices with the scalability and processing power of the cloud, as illustrated in Figure 14. While this union enables remarkable capabilities--imagine accessing a smart thermostat's data from across the globe--it also introduces significant security risks. Numerous studies thoroughly catalog IoT cloud vulnerabilities, so this section focuses on the contemporary Phantom-Delay Attack and expands to other pertinent threats, each highlighting the precarious balance of this hybrid ecosystem. Phantom-delay attacks, a recently uncovered vulnerability, exploit the timing of communications between IoT devices and cloud servers. Unlike conventional disruptions, these attacks manipulate message delivery without discarding packets, leading to severe consequences [103]. These hinge on the following two primitives: IoT Event Message Delay (e-Delay), delaying device-to-server state updates (for example, a \"motion detected\" alert), and IoT Command Message Delay (c-Delay), stalling server-to-device instructions (for example, \"lock the door\"). Typically, transmission delays are fleeting--sub-second lags that pose no issue. Yet, a phantom-delay attack, depicted in Figure 15, extends these into minutes or hours. The cloud server, receiving a delayed \"motion active\" event, assumes it reflects the device's current state, acting on outdated information [104]. Figure 14: System model of a typical smart cloud-based home deployment. This image has been completely re-modified; however, the idea of the image was inspired by [103]. These attacks manifest in three forms. The state-update delay attack employs e-Delay to stall critical state reports--consider a medical IoT device (IoMT) or agricultural sensor lagging in notifying the server of urgent shifts, jeopardizing timely intervention [103]. The action-delay attack disrupts automation; an e-Delay might postpone an insulin pump's response to a glucose spike detected by a continuous monitor, risking health outcomes. Finally, the erroneous execution attack splits into two subtypes as follows: spurious execution, where a delayed event keeps a condition \"true\" past its validity, prompting unneeded actions (for example, a sprinkler activating despite ample rain); and disabled execution, where a delay holds a condition as \"false\", preventing necessary actions (for example, a door failing to lock despite a command) [104]. Unlike jamming, these attacks--launched from a WiFi device sniffing traffic and hijacking TCP sessions via ARP spoofing--leave no trace of dropped packets, dodging retransmission flags [105]. To mitigate phantom-delay attacks, cloud-based IoT frameworks should incorporate timestamp verification in all message exchanges, ensuring that outdated or delayed data are flagged as invalid. Secure time synchronization protocols, such as the Network Time Protocol (NTP) with cryptographic authentication, can help detect discrepancies in the expected message's timing. Additionally, introducing anomaly detection mechanisms that monitor latency variations can preemptively identify these delays, prompting system alerts or requiring revalidation before acting on stale data [106; 107; 108]. Beyond phantom delay, data injection attacks pose a stealthy threat. An adversary might feed falsified inputs--say, spoofed temperature readings--into the cloud, tricking it into erroneous decisions, like shutting down a smart HVAC system [103].