The emergence of softwarized network devices, like programmable switches and smart NICs, has brought about new and advanced network functionalities. Intelligent decision-making becomes possible at line rate by offloading network functionality from the network control-plane to the programmable data-plane. In this paper, we offload fine-grained Distributed Denial of Service (DDoS) attack detection to the data-plane. The state-of-the-art in this regard, mainly aims to embed Machine Learning (ML) models into the data-plane without compromising on inference accuracy. Besides accuracy, we must consider multiple other factors, like traffic feature availability and false positive rates. To that end, we propose O’MINE: ONE MODEL IS NOT ENOUGH, a novel collaborative detection mechanism comprising lightweight ML models. This maximises the detection accuracy while keeping the false positive rate (FPR) low. We use three state-of-the-art datasets to evaluate the O’MINE algorithm and its ML models. Our results show that O’MINE can detect DDoS attacks with high accuracy (≈98% and ≈96% with full and scarce training data, respectively) and low FPR (≈0.22% and ≈0.72% with full and scarce training data, respectively), outperforming the state-of-the-art. Lastly, O’MINE only consumes a few device resources (≈6% of LUT and ≈4% of FF) on the Xlinx Alevo U250 FPGA we have used for inference at line rate.

In the Internet of Things era, the Internet demands extremely high-speed communication and data transformation. To this end, the tactile Internet has been proposed as a medium that provides the sense of touch ability, facilitating data transferability with extra-low latency in various applications ranging from industry, robotics, and healthcare to road traffic, education, and culture. Here, programmable networks are role players in approaching the tactile Internet's low latency (≈ 1ms) pillar. Several functionalities - including security - are offloaded onto the network core employing programmable in-network pipelines. From the security perspective, Artificial Intelligence (AI) is another role player that enables the line-rate inference on the core network without involving the control plane. However, integrating AI-based security solutions in programmable devices is challenging mainly because of their constrained anatomy. Furthermore, such solutions inherit well-known adversarial AI vulnerabilities, representing an additional threat to programmable networks. Considering the above, this article discusses AI-based security solutions in programmable networks, focusing on the explored modalities of integrating AI models in programmable constrained network devices. Moreover, we elaborate on the challenges and risks of relying on AI for such mechanisms. Lastly, the article brings a visionary glimpse for future trends in this regard, raising some essential questions on the indispensability of AI for security functionalities and providing some alternative solutions.

The Internet of Things rapid growth poses privacy and security challenges for the traditional key storage methods. Physical Unclonable Functions offer a potential solution but require secure fuzzy extractors to ensure reliable replication. This paper introduces X-Lock, a novel and secure computational fuzzy extractor that addresses the limitations faced by traditional solutions in resource-constrained IoT devices. X-Lock offers a reusable and robust solution, effectively mitigating the impacts of bias and correlation through its design. Leveraging the preferred state of a noisy source, X-Lock encrypts a random string of bits that can be later used as seed to generate multiple secret keys. To prove our claims, we provide a comprehensive theoretical analysis, addressing security considerations, and implement the proposed model. To evaluate the effectiveness and superiority of our proposal, we also provide practical experiments and compare the results with existing approaches. The experimental findings demonstrate the efficacy of our algorithm, showing comparable memory cost (≈2.4 KB for storing 5 keys of 128 bits) while being 3 orders of magnitude faster with respect to the state-of-the-art solution (0.086 ms against 15.51 s).

In the context of Information-Centric Networking, Interest Flooding Attacks (IFAs) represent a new and dangerous sort of distributed denial of service. Since existing proposals targeting IFAs mainly focus on local information, in this paper we propose GNN4IFA as the first mechanism exploiting complex non-local knowledge for IFA detection by leveraging Graph Neural Networks (GNNs) handling the overall network topology.In order to test GNN4IFA, we collect SPOTIFAI, a novel dataset filling the current lack of available IFA datasets by covering a variety of IFA setups, including ~40 heterogeneous scenarios over three network topologies. We show that GNN4IFA performs well on all tested topologies and setups, reaching over 99% detection rate along with a negligible false positive rate and small computational costs. Overall, GNN4IFA overcomes state-of-the-art detection mechanisms both in terms of raw detection and flexibility, and - unlike all previous solutions in the literature - also enables the transfer of its detection on network topologies different from the one used in its design phase.