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. @en

The Internet model has changed from its first design, rolling from host-centric to information-centric. Consequently, researchers foresee the urge for a new network paradigm that will be more suitable for the need of nowadays users. Named-Data Networking (NDN) adheres to the Information-Centric Networking (ICN) paradigms that have been proposed as possible current Internet substitutes. New proposals concerning NDN-related challenges are released regularly. However, most of these proposals are evaluated using network simulations or theoretical analysis due to lacking a full-stack NDN testbed. Although valid, research has shown that simulation environments or proposed overlay testbeds disturb the experiments and introduce performance mismatches. Motivated by the shreds of evidence mentioned above, we propose a setup of an NDN testbed composed of Raspberry Pi devices. After that, we conduct performance analysis for crucial NDN features such as name-based forwarding, in-network caching, and data packet signing. Our experiments confirm the benefits of enabling caches in intermediate nodes. Furthermore, we compare different signing algorithms based on the producer's goodput and processing time. Indeed, SHA-256 is confirmed as the most lightweight with 103 Mpbs goodput and 130 µs processing time. Nevertheless, a security and performance trade-off must be met. On the other hand, as research has demonstrated, such features can be exploited to compromise users' privacy and degrade the network's performance. Additionally, the attack performance might change while implemented in a real deployment. To validate such effects, we transfer two state-of-the-art privacy attacks from a simulation domain to a physical environment, i.e., our testbed. While one of the transferred attacks preserves the preciseness on the testbed, the other demonstrates result mismatches. @en

PATTA is the first privacy attack based on network traffic analysis in Information-Centric Networking. PATTA aims to automatically identify the category of requested content by sniffing the communication towards the first hop router. PATTA applies text processing and machine learning techniques to content names in content-oriented architectures. We evaluate PATTA in a simulated network, achieving an accuracy in determining a real-time content category equal to 96%. @en

Today's Internet is experiencing a massive number of users with a continuously increasing need for data, which is the leading cause of introduced limitations among security and privacy issues. To overcome these limitations, a shift from host-centric to data-centric is proposed, and in this context, Information-Centric Networking (ICN) represents a promising solution. Nevertheless, unsettling the current Internet's network layer - i.e., Internet Protocol (IP) - with ICN is a challenging, expensive task since it requires worldwide coordination among Internet Service Providers (ISPs), backbone, and Autonomous Services (AS). Therefore, researchers foresee that the replacement process of the current Internet will transition through the coexistence of IP and ICN. In this perspective, novel architectures combine IP and ICN protocols. However, only a few of the proposed architectures place the security-by-design feature. Therefore, this article provides the first comprehensive Security and Privacy (SP) analysis of the state-of-the-art IP-ICN coexistence architectures by horizontally comparing the SP features among three deployment approaches - i.e., overlay, underlay, and hybrid - and vertically comparing among the ten considered SP features. Lastly, the article sheds light on the open issues and possible future directions for IP-ICN coexistence. Our analysis shows that most architectures fail to provide several SP features, including data and traffic flow confidentiality, availability, and anonymity of communication. Thus, this article shows the secure combination of current and future protocol stacks during the coexistence phase that the Internet will definitely walk across.@en

In recent years, the booming of Internet of Things (IoT) has populated the world with billions of smart devices that implement novel services and applications. The potential for cyberattacks on IoT systems have called for new solutions from the research community. Remote attestation is a widely used technique that allows a verifier to identify software compromise on a remote platform (called prover). Traditional challenge-response remote attestation protocols between the verifier and a single prover face a severe scalability challenge when they are applied to large scale IoT systems. To tackle this issue, recently researchers have started developing attestation schemes, which we refer to as Collective Remote Attestation (CRA) schemes, that are capable of remotely performing attestation of large networks of IoT devices. In this paper, after providing the reader with a background on remote attestation, we survey and analyze existing CRA schemes. We present an analysis of their advantages and disadvantages, as well as of their effectiveness against a reference attacker model. We focus our attention on CRA schemes' characteristics and adversarial mitigation capabilities. We finally highlight open research issues and give possible directions for mitigating both the limitations of existing schemes, and new emerging challenges. We believe this work can help guiding the design of current and future proposals for CRA.@en

In recent years, the booming of Internet of Things (IoT) has populated the world with billions of smart devices that implement novel services and applications. The potential for cyberattacks on IoT systems have called for new solutions from the research community. Remote attestation is a widely used technique that allows a verifier to identify software compromise on a remote platform (called prover). Traditional challenge-response remote attestation protocols between the verifier and a single prover face a severe scalability challenge when they are applied to large scale IoT systems. To tackle this issue, recently researchers have started developing attestation schemes, which we refer to as Collective Remote Attestation (CRA) schemes, that are capable of remotely performing attestation of large networks of IoT devices. In this paper, after providing the reader with a background on remote attestation, we survey and analyze existing CRA schemes. We present an analysis of their advantages and disadvantages, as well as of their effectiveness against a reference attacker model. We focus our attention on CRA schemes' characteristics and adversarial mitigation capabilities. We finally highlight open research issues and give possible directions for mitigating both the limitations of existing schemes, and new emerging challenges. We believe this work can help guiding the design of current and future proposals for CRA.@en

In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands their usage on a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs’ security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs.@en