Channel hopping is a well-known methodology to dynamically allocate frequency resources to nodes in wireless communication systems. The aim of channel hopping is to mitigate possible interference issues caused by both legitimate and malicious users. The state-of-the-art channel hopping-based jamming mitigation techniques are becoming increasingly smart by using game theory or machine learning. However, we show in this paper that, although the victim may employ smart channel hopping strategies, these are not always effective in mitigating attacks. In this paper, we implement an effective jamming attack strategy to impair the effectiveness of channel hopping mitigation strategies. We test our attack on a testbed network composed by real devices, and test its effectiveness against three mitigation strategies: i) incremental channel hopping, ii) random channel hopping, and iii) smart channel hopping. We show that, by implementing our attack, an attacker can reduce the number of successfully transmitted packets by at least 28.5%,

Initially designed to represent ownership of various assets, Non-Fungible Tokens (NFTs) have emerged as a new tool in the blockchain domain for investment and trading. The NFT markets are rapidly budding with significant growth in trading volumes over the last few years. While the NFT ecosystem is continuously evolving, users are exploring astute trading practices to gain financial profits. In this paper, we uncover shill looping, a novel NFT trade practice that NFT owners can exploit to artificially inflate the price of an NFT token. We investigate shill looping and its primary effects in a multi-billion dollar NFT collection called BAYC, showing that approximately 50% of these NFTs exhibit shill looping. Our empirical analysis shows that shill looping significantly boosts the average NFT values by over 45% in the best case. Our initial results highlight the severeness of the shill looping phenomenon and open a new research direction for further exnloration.

The largely distributed nature of fog computing (FC) provides a major attack surface with increased possibilities for attackers. Compared to networks with higher centralization, securing an FC network represents a significant challenge. This issue is complicated since edge nodes are usually resource-constrained and cannot resort to sophisticated cryptographic solutions. Furthermore, the deployment of a public key infrastructure is complicated considering the large number of connected devices. In this chapter, we investigate the challenges and possibilities of wireless communications in fog computing. In particular, we investigate the advantages provided by the adoption of physical layer security (PLS) techniques. Thanks to PLS and the combination of beyond 5G technologies, we show how an FC network can be secured using low-complexity PLS solutions viable for resource-constrained devices. We also show how PLS solutions can guarantee important features in an FC network, such as authorization and location verification.

The inclusion of drones in Internet of Vehicles (IoV) is a current trend that presents significant trade-offs. On the one hand, Unmanned Aerial Vehicles (UAVs) provide advantages such as enabling ground communications also when physical obstacles limit the connectivity. On the other hand, they increase the attack surface. For instance, physical attacks on drones provide the attacker with credentials that can be used to inject bogus information into the IoV network, thus jeopardizing not only security but also users' safety. In this scenario, authentication plays a fundamental role to guarantee security. It is however fundamental to develop authentication protocols that can, at the same time, protect ground users' data and prevent attacks to drones. However, currently available authentication schemes cannot guarantee security in case of attacks to drones. In this paper, we propose a Blockchain-supported authentication protocol for Drone-assisted IoV using Elliptic curve cryptography (BDIVE). Compared to existing authentication protocols, we extend the threat model from an honest-but-curious drone to active attacks against drones. BDIVE provides both energy-efficiency, traceability, and accountability thanks to the use of blockchain at the Trusted Authority (TA). Using Burrow-Abadi-Needham (BAN) logic, we analyze and prove the security of mutual authentication in BDIVE. We also prove the security of BDIVE against several attacks by implementing it in AVISPA. To assess its scalability and energy efficiency, we implement BDIVE using Omnetpp with its Castalia simulator. The comparison of BDIVE with currently existing authentication protocols, shows that it reduces the energy consumption up to 70% and the computational cost up to 68%, while providing resistance to previously unconsidered attack vectors.

Due to the frequent unauthorized invasions by commercial drones to Critical Infrastructures (CIs), the US-based Federal Avionics Administration (FAA) recently published a new specification, namely RemoteID. Such a rule requires all drones to broadcast information about their identity and location, to allow for immediate invasion attribution and counter-actions. However, the enforcement of such a rule poses severe concerns on drones' operators, especially in terms of location privacy and tracking threats. Indeed, by simply receiving wireless signals, an adversary could know the precise drone location, track it, and infer sensitive information. In this paper, we demonstrate that CI operators can detect timely and efficiently drones invading no-fly zones, without sacrificing drones location privacy. Specifically, we provide two major contributions. First, we propose DiPrID, the first RemoteID-compliant solution enhancing drones location privacy via Differential Privacy. Second, we introduce and evaluate ICARUS, an effective area invasion detection technique capable of identifying invasions by unauthorized drones, even when equipped with DiPrID, with remarkable accuracy and negligible detection delays. Our experiments showed that when drones obfuscate their location by an average distance as large as 31.914 km, ICARUS can detect up to 94.2% of invasions, while false-positives can be mitigated through the help of the FAA.

The Internet of Things (IoT) is a pervasive phenomenon in our everyday life, binding and enabling communication between heterogeneous physical devices and the internet. The applications of this technology range from industrial control to medical devices, hence interconnecting a huge number of devices collecting sensitive information. Although this provides a highly dynamic network with extensive capabilities, its widespread adoption and sensitive nature of the collected data open a large number of challenges from a cybersecurity perspective. In this paper, we offer a fresh perspective on the IoT security landscape by providing a full list of reported vulnerabilites. By organizing protocols and vulnerabilities according to the TCP/IP stack, we guide both novice and expert users and developers in gaining a clear overview of the state-of-the-art IoT and its security issues. Furthermore, by discussing open problems and future directions, we think our paper will be helpful to the research community in designing the next generation of secure IoT.

Cryptocurrencies are digital assets that can be used as a medium of exchange and are stored in a ledger in the form of a cryptography-secured computer database. Most cryptocurrencies are deployed in decentralized networks such as the blockchain, where they can be transferred from an address to another. However, anyone having access to the private key of an address can fully operate and steal the full balance of such address. Although brute-force attacks consume significant resources in terms of time and power, breaking one of the richest cryptocurrency addresses leads to a huge reward in terms of hard money. Therefore, an attacker may be incentivized to run this attack.In this paper, we run a statistical study of cryptocurrencies distribution over the richest addresses and assess the worthiness of a brute-force attack towards their public key. We consider an attacker investing a certain amount of hard money (i.e., a currency that is made up of or directly backed by a valuable commodity such as gold) to run the attack and propose a statistical characterization of the average gained reward. To further assess the worthiness of a brute-forcing attack, we compare the average reward with that of other probabilistic reward systems, such as mining and scratch cards. Results show that the average reward of the proposed attack is six orders of magnitude lower than that of mining and 10 orders of magnitude lower than that of scratching cards for a given initial investment.

Internet of Drones (IoD) is a framework to set up drones networks that may serve multiple purposes, e.g., data collection. New IoD applications (such as drone assisted internet of vehicles) envision the simultaneous collection of multiple data types. Although authentication may prevent unauthorized users to access the collected data, existing authentication solutions do not distinguish between the different types of data collected by drones. Therefore, authenticated users may receive sensitive data regarding another user incurring hence in a privacy leakage. In this paper, we propose SETCAP, a novel Service-Based Energy-Efficient Temporal Credential Authentication Protocol for IoD. SETCAP exploits the distinction between data types to prevent information leakage. We formally test SETCAP against the Real-Or-Random (ROR) model and implemented SETCAP in Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Moreover, we validated SETCAP via non-mathematical security analysis to show its security against many attacks. We assessed the superiority of SETCAP in terms of functionality and security characteristics as well as computation, communication, and energy costs. The communication cost of creating a session in SETCAP is approximately 20% smaller than that of creating a session in the closest state-of-the-art protocol. Furthermore, the framework that we propose requires the creation of a number of sessions that are additive in terms of the number of drones and users, whereas the existing solutions are multiplicative. SETCAP is therefore a secure and scalable solution for resource-constrained devices such as drones.

The 6TiSCH standard provides minimum latency and reliability in mission-critical IoT applications. To optimize resource allocation during 6TiSCH network formation, IETF released the 6TiSCH minimal configuration (6TiSCH-MC) standard. 6TiSCH-MC considered IETF's IPv6 Routing Protocol for Low power and Lossy network (RPL) as a routing protocol for both upward and downward routing. In RPL, new joining nodes or joined nodes transmit DODAG Information Solicitation (DIS) requests to get routing information from the network. However, we observe that malicious node(s) can severely affect 6TiSCH networks by sending multiple DIS requests. In this letter, we show and experimentally evaluate on real devices the impact of the DIS attack during 6TiSCH networks formation. We show that the attacker does not need expensive resources or access to the network's sensitive information to execute the DIS attack. Our testbed experiments show that the DIS attack significantly degrades the nodes' joining time and energy consumption, increasing them by 34% and 16%, respectively, compared to normal functioning during 6TiSCH network formation.

Autonomous message propagation in Internet of Vehicles (IoV) need to be secure to guarantee safety. In fact, a malicious user propagating false information may cause accidents impacting on the safety of both vehicles and drivers. Authentication should however account for the time-critical nature of messages impacting the vehicles behavior. Furthermore, it should account for the resource-limited nature of vehicles. Currently available authentication protocols fail in providing a secure and lightweight solution in terms of computation time, communication cost, and storage cost. Furthermore, as we later show, they are vulnerable to multiple attacks.In this paper, we propose Method for Lightweight and Secure Communication (MeLSeC), a novel lightweight and secure authentication scheme for IoV. Compared to state-of-the-art authentication solutions, MeLSeC is resistant to a larger number of attacks. We test MeLSeC against multiple attacks using Automated Validation of Internet Security Protocols (AVISPA), and show its higher security guarantees against other relevant state-of-the-art solutions. By comparing it with other relevant state-of-the-art solutions, we show that MeLSeC provides up to 700 times smaller computation costs on a desktop computer and up to 4 times smaller computation cost on Raspberry Pi. Furthermore, MeLSeC reduces the joint communication and storage costs by up to 7 times.

Received signal Direction of Arrival (DOA) estimation represents a significant problem with multiple applications, ranging from wireless communications to radars. This problem presents significant challenges, mainly given by a large number of closely located transmitters being difficultly separable. Currently available state of the art approaches fail in providing sufficient resolution to separate and recognize the DOA of closely located transmitters, unless using a large number of antennas and hence increasing the deployment and operation costs. In this paper, we present a deep learning framework for DOA estimation under Line-of-Sight scenarios, which able to distinguish a number of closely located sources higher than the number of receivers' antennas. We first propose a formulation that maps the received signal to a higher dimensional space that allows for better identification of signal sources. Secondly, we introduce a Deep Neural Network that learns the mapping from the receiver antenna space to the extended space to avoid relying on specific receiver antenna array structures. Thanks to our approach, we reduce the hardware complexity compared to state of the art solutions and allow reconfigurability of the receiver channels. Via extensive numerical simulations, we demonstrate the superiority of our proposed method compared to state-of-the-art deep learning-based DOA estimation methods, especially in demanding scenarios with low Signal-to-Noise Ratio and limited number of snapshots.

Visible Light Communication (VLC) is one the most promising enabling technology for future 6G networks to over-come Radio-Frequency (RF)-based communication limitations thanks to a broader bandwidth, higher data rate, and greater efficiency. However, from the security perspective, VLCs suffer from all known wireless communication security threats (e.g., eavesdropping and integrity attacks). For this reason, security re-searchers are proposing innovative Physical Layer Security (PLS) solutions to protect such communication. Among the different solutions, the novel Reflective Intelligent Surface (RIS) technology coupled with VLCs has been successfully demonstrated in recent work to improve the VLC communication capacity. However, to date, the literature still lacks analysis and solutions to show the PLS capability of RIS-based VLC communication. In this paper, we combine watermarking and jamming prim-itives through the Watermark Blind Physical Layer Security (WBPLSec) algorithm to secure VLC communication at the physical layer. Our solution leverages RIS technology to improve the security properties of the communication. By using an opti-mization framework, we can calculate RIS phases to maximize the WBPLSec jamming interference schema over a predefined area in the room. In particular, compared to a scenario without RIS, our solution improves the performance in terms of secrecy capacity without any assumption about the adversary's location. We validate through numerical evaluations the positive impact of RIS-aided solution to increase the secrecy capacity of the legitimate jamming receiver in a VLC indoor scenario. Our results show that the introduction of RIS technology extends the area where secure communication occurs and that by increasing the number of RIS elements the outage probability decreases.

Distributed Ledger Technology (DLT) is one of the most promising technology at present with applications in different domains, such as Cyber-Physical Systems (CPS) and Internet of Thing (IoT). IOTA is an example of Directed Acyclic Graph (DAG)-based DLTs applicable to IoT, addressing the issues of scalability, consensus complexity, and mining incentives of blockchains. In order to join the network and to later disseminate fresh information, nodes exploit IOTA's autopeering module. The security of this module is hence of fundamental importance for the network liveness, however this requirement has never been analyzed in the literature. In this paper, we provide the first security analysis of the autopeering module of the Coordicide, IOTA's main framework. We divide the contribution in two parts: i) we show that the peer-discovery process in the autopeering module is vulnerable to flooding attack, a type of Denial of Service (DoS), and ii) we propose Time Constrained Identity Match (TID), a novel protocol to mitigate the flooding attack. In particular, we compare two implementations of TID: leaky-TID based on leaky bucket, and token-Tid based on token bucket. We discuss their suitability to IOTA both in terms of effectiveness in mitigating the flooding attack and in terms of resource consumption (memory and CPU). Based on the results, we observe that leaky-TID shows the better performance in preventing flooding attacks, reducing the traffic under attack by 67% and reducing memory consumption by 12.5%.

The sensing and communication capabilities of the Internet of Vehicles (IoV) allow for the design of safer Intelligent Transportation Systems (ITS), with capabilities for reduced crashes and drivers' injuries. To this aim, entities in the IoV can autonomously exchange Warning Messages (WMs) on the road status to help drivers, passengers, and vehicles to take immediate actions and reduce road accidents. WMs authentication, timely propagation, and integrity is a fundamental requirement, as the injection of false WMs or their delay by a malicious user can lead to accidents or traffic congestion. The currently available WMs authentication and propagation schemes focus on the security properties, without however accounting for the scalability issues related to the large number of entities connected in the IoV. In this paper, we propose Secure Warning Message Authentication and Propagation (SWAP), an authentication protocol specifically designed for WM propagation in the Internet of Vehicles (IoV). We introduce the Warning Message Authority (WMA), a novel entity responsible for secure WMs handling. Thanks to the WMA, WMs are managed by dedicated resources that allow for the timely delivery of safety-critical information, avoiding delays due to network congestion at the Trusted Authority (TA). We prove the security of SWAP by using the Random Oracle Model (ROM) and via security analysis. Compared to state of the art authentication schemes, SWAP reduces the number of communication bits by 100 bits, and provides a ten-fold decrease of the computation time.

Channel hopping provides a defense mechanism against jamming attacks in large scale Internet of Things (IoT) networks. However, a sufficiently powerful attacker may be able to learn the channel hopping pattern and efficiently predict the channel to jam. In this paper, we present FOLPETTI, a Multi-Armed Bandit (MAB)-based attack to dynamically follow the victim's channel selection in real-time. Compared to previous attacks implemented via Deep Reinforcement Learning (DRL), FOLPETTI does not require recurrent training phases to capture the victim's behavior, allowing hence a continuous attack. We assess the validity of FOLPETTI by implementing it to launch a jamming attack. We evaluate its performance against a victim performing random channel selection and a victim implementing a MAB defence strategy. We assume that the victim detects an attack when more than 20% of the transmitted packets are not received, therefore this represents the limit for the attack to be stealthy. In this scenario, FOLPETTI achieves a 15% success rate for the victim's random channel selection strategy, close to the 17.5% obtained with a genie-aided approach. Conversely, the DRL-based approach reaches a success rate of 12.5%, which is 5.5% less than FOLPETTI. We also confirm the results by confronting FOLPETTI with a MAB based channel hopping method. Finally, we show that FOLPETTI creates an additional energy demand independently from its success rate, therefore decreasing the lifetime of IoT devices.

Reputation plays a fundamental role in the blockchain ecosystem, as it provides means to assign trust levels to participating nodes. Reputation is usually gained in time: The higher the fair participation of a node to the network, the higher its reputation. IOTA's Coordicide proposes a reputation system based on an asset named Mana. This reputation system is the base of the Coordicide's primary security protection. At present, IOTA assigns to nodes an amount of Mana equivalent of the amount of transferred funds only. However, this may lead to privacy leakage on nodes' status and may not be sufficient to fully characterize the behavior of a node in the network. Furthermore, the Mana reputation system is currently susceptible to monopoly, eclipse, and spamming attacks. An efficient and secure reputation system shall comprehend a larger number of factors to provide security and reliability.In this paper, we propose Enhanced Mana (eMana), a theoretical model for the IOTA reputation system accounting for nodes' good or bad behaviour based on their assigned tasks. We model each of the Coordicide modules having direct or indirect connections with the reputation system. In eMana the behavior of a peer node can be transparently analyzed by the other peers; thus, it is infeasible to modify the reputation value. Theoretically, eMana is flexible enough to include possible new modules' behaviour and provides a strict hard-To-gain reputation. To the best of our knowledge, eMana is the first mathematical model for Coordicide's enhanced reputation based on nodes' multi-dimensional behaviour. Additionally, eMana is generic and flexible, and may also be adaptable to other Distributed Ledger Technology (DLT) systems.

The Fifth Generation of Communication Networks (5G) envisions a broader range of services compared to previous generations, supporting an increased number of use cases and applications. The broader application domain leads to increase in consumer use and, in turn, increased hacker activity. Due to this chain of events, strong and efficient security measures are required to create a secure and trusted environment for users. In this paper, we provide an objective overview of 5G security issues and the existing and newly proposed technologies designed to secure the 5G environment. We categorize security technologies using Open Systems Interconnection (OSI) layers and, for each layer, we discuss vulnerabilities, threats, security solutions, challenges, gaps and open research issues. While we discuss all seven OSI layers, the most interesting findings are in layer one, the physical layer. In fact, compared to other layers, the physical layer between the base stations and users' device presents increased opportunities for attacks such as eavesdropping and data fabrication. However, no single OSI layer can stand on its own to provide proper security. All layers in the 5G must work together, providing their own unique technology in an effort to ensure security and integrity for 5G data.

Blockchains are currently deployed in a large number of different scenarios, such as cryptocurrencies, communications, factory automation, and vehicular networks. Both academia's and industry's interest in this technology is motivated by its distributed nature and its inherent security in managing information. However, traditional blockchains are limited in terms of the transaction rate they can approve. To overcome this limitation, IOTA proposed a novel blockchain based on a Directed Acyclic Graph (DAG) structure (the Tangle). It handles a larger number of transactions to target high-traffic scenarios such as Internet of Things (IoT). However, the security of the IOTA's blockchain depends on the design of its underlying communication protocols. In this paper, we analyze the security of the IOTA Tangle from a transport layer perspective. In particular, we show that well-known port-based attacks can jeopardize the integrity and availability of the IOTA services. We focus on the IOTA's TCP-based ports, and provide a discussion of all the possible attacks and their effects. We assess their validity via experimental evaluation showing their effect on the network operations. Results show that SYN flooding affects the gossip protocol by fully disrupting the service. We also find that 16% of the nodes are unable to create a neighbours list, leading to a disruption of the information exchange process. On the other side, we also show that some components are not affected by these attacks. To the best of our knowledge, we are the first to identify and validate these vulnerabilities in IOTA. Lastly, we discuss some possible countermeasures.

Charging an EV (Electric Vehicle) comprises two phases: a) resource negotiation, and b) actual charging. While the former phase runs over secure communication protocols, the latter is usually assumed not to be a threat to security and privacy. However, we believe that the physical signals exchanged between the EV and the EVSE (Electric Vehicle Supply Equipment) represent information that a malicious user could exploit for profiling. Furthermore, as a large number of EVSEs has been deployed in public places to ease out-of-home EV charging, an attacker might easily have physical access to unsecured data. In this paper, we propose EVScout, a novel attack to profile EVs during the charging process. By exploiting the physical signals exchanged by the EV and the EVSE as a side-channel to extract information, EVScout builds a set of features peculiar for each EV. As an EVScout component, we also propose a novel feature extraction framework, based on the intrinsic characteristics of EV batteries, to identify features from the exchanged electric current. We implemented and tested EVScout over a set of real-world measurements (considering 100 charging sessions of 22 EVs). Numerical results show that EVScout could profile EVs, attaining a maximum of 0.9 recall and 0.85 precision. To the best of authors’ knowledge, these results set a benchmark for upcoming privacy research for EVs.