# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 7 Conclusions  
This survey examined IFAs, a persistent threat to DoS in NDN. We explored how NDN's stateful forwarding and pull-based communication model make it uniquely vulnerable to such attacks, particularly through PIT misuse. A taxonomy of IFA variants, including fake, unsatisfiable, and collusive attacks, was discussed alongside their operational characteristics. The paper reviews recent detection and mitigation strategies by grouping them into five categories: rate limiting, PIT management, ML/AI-based techniques, reputation/trust systems, and blockchain-based approaches. Rate limiting controls traffic volume but may reduce legitimate requests during high load. PIT management adjusts interest handling to block attacks at the forwarding layer, though it may disrupt normal routing. ML/AI-based methods adaptively detect anomalies using traffic features, but can be resource-intensive. Reputation systems score nodes based on behavior, helping with gradual mitigation, but face cold-start and collusion issues. Blockchain-based approaches offer decentralized trust and accountability at the cost of increased latency and complexity. Each category has its strengths and limitations, particularly when evaluated in terms of metrics such as scalability, detection accuracy, overhead, and adaptability.  
Although progress is evident, challenges remain. Real-time detection with low false positives, content-agnostic filtering that preserves privacy, and evaluation frameworks that ensure reproducibility are still lacking. Applications in NDNoT, smart homes, and industrial networks demand lightweight, adaptive defenses.  
To guide future innovation, this survey discusses five main research directions: development of mitigation strategies that combine multiple defense layers, privacy-preserving detection mechanisms suited for encrypted or obfuscated content names, real-time optimization for edge and IoT environments, standardized benchmarking frameworks for reproducibility and cross-study comparison, and adaptive systems capable of learning and evolving with new attack patterns.  
Future work should focus on hybrid models that blend statistical learning with decentralized validation, ensuring security without sacrificing efficiency. Addressing these challenges will be the key to realizing the promise of NDN as a future secure and scalable Internet architecture.  
**Author Contributions:** Conceptualization, S.O. and Q.Z.; methodology, S.O.; software, S.O.; validation, Y.C., G.C. and E.B.; formal analysis, S.O., Q.Z., D.N. and S.W.; investigation, Y.C. and G.C.; resources, Y.C. and G.C.; data curation, S.O.; writing--original draft preparation, S.O. and Y.C.; writing--review and editing, Q.Z., D.N., S.W., G.C. and E.B.; visualization, S.O.; supervision, Y.C. and E.B.; project administration, G.C.; funding acquisition, Y.C. and G.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Navy through grant number N68335-25-C-0232, from 26 February 2025 to 18 July 2025.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: Data are contained within the article.  
* Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Intelligent Fusion Technology, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.