## 3 Discussion and Classification of IoT Attacks and Countermeasures
### IoT Attacks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Attack Type** & **Possible Defenses or Architectural Measures** & **IoT Layer Classification** \\ \hline \hline Data Theft & Blockchain, Edge and Fog Computing & Application Layer \\ \hline Sniffing Attacks & Edge and Fog Computing & Application Layer \\ \hline Malicious Code and Database Injections & Edge and Fog Computing & Application Layer \\ \hline Distributed Denial of Service (DDoS) & Machine Learning and Deep Learning, Edge and Fog Computing & Network Layer \\ \hline Spoofing Attacks & Machine Learning and Deep Learning, Edge and Fog Computing & Network Layer \\ \hline Man-in-the-middle (MitM) & Machine Learning and Deep Learning, Edge and Fog Computing & Network Layer \\ \hline Tampering & Edge and Fog Computing & Perception/Physical/Sensing Layer \\ \hline Node Jamming or Radio Frequency Interference & Edge and Fog Computing & Perception/Physical/Sensing Layer \\ \hline Sleep Deprivation or Denial of Sleep (DoSL) & Edge and Fog Computing & Perception/Physical/Sensing Layer \\ \hline \end{tabular}
\end{table} 
Table 1: Classification of Attack Types and possible Defenses or Architectural Measures in Urban IoT Networks. * _Spoofing attacks_ take place when an adversary tries to fake their identity and impersonate a legitimate device or a user (e.g., by spoofing an IP address). This can give the adversary unauthorized access to certain resources or can allow them to observe and collect sensitive data transmitted over the network [1];
* _Man-in-the-middle (MitM)_ is an attack during which an adversary is able to insert itself between two nodes in the IoT network. Consequently, the attacker is able to intercept, capture, modify, and relay data flowing between the two nodes without their knowledge [2]. More specifically, from the nodes' perspective it seems as if they are directly communicating with each other. **Perception/Physical/Sensing Layer:** The perception layer is also known as the sensing [1] or physical layer since it is responsible for handling the physical IoT sensors and actuators. This layer is responsible for collecting data from the end devices and forwarding them to the network layer [2]. Some examples for devices that operate here include smoke detectors, camera sensors, and humidity sensors [1]. Exploiting these devices opens up opportunities for physical layer IoT attacks such as the following:  
* _Tampering_ refers to a physical intervention on the IoT device, through which the perpetrator modifies the hardware in a way that allows them to obtain sensitive information such as credentials, encryption keys, etc. [2];
* _Node Jamming_ or _radio frequency interference_ occurs when an attacker is near the location of the end devices and prevents them from successfully communicating with other devices on the IoT network [9]. This is achieved by sending noise signals that disrupt the wireless communication between the IoT devices [13];
* _Sleep deprivation or Denial of Sleep (DoSL)_ is an attack during which the perpetrator targets low-powered IoT devices and tries to increase their power consumption in order to shut them down [1; 14]. This is a form of DoS attack that can be achieved by injecting infinite (communication and computational) loops or modifying the hardware of the IoT device [14].