# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 5 Applications and Use Cases of NDN
### Wireless Sensor Networks and NDN  
WSNs (a subset of IoT) often involve multi-hop low-power networks. NDN has been adapted to WSNs for efficient data collection. In such networks, an IFA could originate from a compromised sensor node or an external attacker spoofing interests over the wireless channel. One characteristic of WSNs is that nodes often know their immediate neighbors and parent node (in a tree), so some defenses leverage local monitoring: if a child node sends way more interests upstream than usual, the parent can suspect an attack [50]. Techniques such as rate policing have been employed at cluster heads or parent nodes in a sensor tree. These act similarly to interface rate limiting, but here the \"interface\" is a single-child sensor. Because WSN nodes have limited PIT space, a few dozen malicious interests could already cause drops, so WSN-focused schemes are very aggressive in filtering [51].  
Another technique in WSN/IoT is to utilize the knowledge of the central base station. Often, the IoT/WSN has a base station or gateway to which all data ultimately flows. This gateway can maintain a global view of the content names (similar to the InterestFence content awareness idea) and inform the network of fake interests. In essence, the base station can play the role of the content producer (for data collected in the network) and can quickly recognize if an interest is asking for non-existent data. The base station can then send a command to the sensors to drop interests with that name [52]. The recognition of interests is analogous to the InterestFence HSL concept but tailored to WSN: the base station could send a short \"do not forward Interests for X\" message if X is malicious. Although the specific literature on WSN-targeted IFA defense is not abundant, these principles are discussed in IoT security contexts.  
To contextualize mitigation strategies within practical deployment settings, Table 3 associates each category with a relevant application domain. Domains such as Smart Homes, Healthcare IoT, Industrial IoT, NDNoT, and Wireless Sensor Networks each impose limitations and expose resources to threats. Smart Home systems often emphasize low energy usage and reduced latency, which favors approaches like PIT management and rate limiting over computationally demanding methods such as blockchain-based validation. On the other hand, NDNoT deployments involving various edge devices may benefit more from hybrid techniques that integrate lightweight machine learning with caching or trust-based filtering.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Application Domain** & **Recommended Mitigation Approaches** \\ \hline Smart Homes & Rate limiting, PIT management, content-agnostic filtering \\ \hline \hline \multirow{2}{*}{Healthcare IoT} & Lightweight ML detection, trust/reputation scoring, flow-based filtering \\ \hline \hline \multirow{2}{*}{Industrial IoT (IIoT)} & Stateless detection, adaptive rate control, collaborative trust systems \\ \hline \hline \multirow{2}{*}{NDNoT (NDN over IoT)} & ML + caching strategies, low-latency interest shaping, PIT pruning \\ \hline \hline \multirow{2}{*}{Wireless Sensor Networks} & Rule-based detection, periodic PIT flushing, constrained rate-limiting \\ \hline \hline \end{tabular}
\end{table}
Table 3: Application domains vs. IFA mitigation strategies.