In recent years, blockchain technology has seen significant practical growth, yet it has not seen the same advancement from a theoretical perspective. This has led to the creation of numerous blockchains that are very different from each other and behave like isolated worlds. The research and development of theoretical frameworks, which define the fundamental properties of blockchains and define standards to follow for a more homogeneous implementation approach, have become extremely important. A theoretical model can help not only to design blockchains in the future but also to define a set of minimum requirements to be met for the creation of interoperability protocols between existing blockchains. In this work, we propose a theoretical model of blockchain that describes its most significant properties. Starting from our theoretical model, we present Smart Listeners , a blockchain interoperability protocol that finalises an inter-chain transaction with just two transactions: one on the source blockchain and one on the destination blockchain. The protocol is optimistic since changes on the source blockchain occur as if inter-chain transactions were successful. It is hybrid since it combines some properties of watchtowers and oracles in the off-chain components. We provide a benchmark on the performance of the proposed protocol in terms of latency and transactions per second. Finally, we define the minimum requirements that a blockchain should satisfy to allow the application of general-purpose interoperability protocols.

The integration of Unmanned Aerial Vehicles (UAVs) with the Internet of Things (IoT), also known as IoUAVs, facilitates real-time data transmission and coordinated operations in critical applications such as smart agriculture, disaster response, and infrastructure monitoring. The growing development of IoT has, however, made IoUAVs vulnerable to emerging cyberattacks that could disrupt these essential services. Deep learning can detect hidden attack patterns, but power and processing constraints make it challenging for resource-constrained IoUAVs. Edge computing offloads real-time analysis tasks, but optimizing workloads with unpredictable connectivity and high latency requirements for intrusion detection remains challenging. To address these challenges, this paper proposes a novel Edge-Based Intrusion Detection System (EIDS) that introduces two key innovations. We developed a metaheuristic task optimization technique for the IoUAV edge environment to efficiently manage computational loads and resources. Second, a Deep Transfer Learning (DTL) technique optimized for intrusion detection minimizes training time and computational overhead. Our novel EIDS-DTL technology synergistically incorporates these components for powerful intrusion detection. Our method optimizes feature extraction from IoUAV network traffic by purifying, filtering, and normalizing data. By fine-tuning pre-trained models, the system achieves high accuracy in identifying malicious activity while ensuring optimal performance in resource-constrained environments. Experimental results on two benchmark datasets demonstrate classification accuracies of 98.95% and 99.27%, outperforming existing approaches by up to 5% in accuracy while maintaining high precision, recall, and F1 scores. The proposed method enhances accuracy and efficiency, providing an effective solution for IoUAV security and edge optimization.

Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance.

Designing systems tolerant to faults is crucial to assure continuity of service for mission critical applications. However, their implementation may be costly and challenging. In this study, analytical models are presented for performance evaluation of systems equipped with Practical Byzantine Fault-Tolerant consensus protocols. Byzantine Fault Tolerance is particularly compelling, since it can provide a robust consensus mechanism to implement decentralized platforms, like Decentralised Ledger Technology and, notably, blockchains. The performability model is based on continuous-time Markov chains, in which the processes involved follow the exponential distribution. The numerical results presented report an inverse non-linear relation between number of nodes and performability. Performance decreases also as the ratio between break-down rate and repair rate increases.

The public blockchain lacks data confidentiality. Although a level of anonymity seems guaranteed, it is still possible to link transactions and disclose related information. A solution to the privacy problem is to use cryptography in transactions, however this can lead to increased costs and slowdown in network throughput. Recent works experiment with advanced cryptography, in particular Zero-Knowledge proofs (ZK-proofs) can be supplied within a transaction to prove its validity, without revealing sensitive information. We analyze solutions that adopt ZK-proofs, such as Confidential Transactions (CTs). Several challenges emerge depending on both the zero-knowledge system and the balance model considered (UTXO, hybrid or account model). For ZK-proofs, systems that do not introduce additional trust are required. On the other hand, the account model is the most flexible for addressing security challenges. Moreover, CTs do not fully exploit the potential of ZK-proofs, since each transaction comes with one or more ZK-proof for a single transfer. Within this paper, we present ZeroMT, a novel multi-transfer private payment scheme for account-based blockchains. Drawing inspiration from Zether, our approach extends their work to develop a payment model that supports multiple payees within a single transaction. This also benefits scalability: ZeroMT enriches the CTs with the aggregation property, i.e., the batch verification of multiple transfers from a single and aggregate proof. We show that in our extended model the overdraft-safety and privacy security properties still hold. We provide an implementation and evaluation of ZeroMT, which shows the benefits of aggregating multiple transfers.

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection.

The act of source code plagiarism is an academic offense that discourages the learning habits of students. Online support is available through which students can hire professional developers to code their regular programming tasks. These facilities make it easier for students to practice plagiarism. First, raw source codes are cleaned from noisy data to extract meaningful codes as the actual logic is more important to the programmers. Second, pre-processing techniques based on tokenization are used to convert filtered codes into meaningful tokens. It breaks the codes into small instances with the number of occurrences known as the frequency. Thirdly, the local and global weighting scheme method is applied to estimate the significance of each feature in an individual or a group of documents. It helps us greatly to zoom in on the importance of each feature of how effective it is for the next phase. Fourth, the single value decomposition method is used to reduce the dimensions of these features by maintaining the actual semantics of the source codes. This technique is used to remove overloaded noise information and collect only those features that are more effective for plagiarism detection. Fifth, the latent semantic analysis (LSA) technique is used to mine the actual semantics of the source codes in the form of latent variables. After that, the LSA features are used as input to cosine similarity to compute the plagiarism among different source codes. To validate the proposed approach, we used the topic modeling approach to group the relevant features into different topics.

Macro-programming advocates the use of high-level abstractions to specify distributed systems as a whole. However, macro-programming implementations are often centralised. In this paper we present PICO-MP, the first fully decentralised macro-programming middleware for wireless sensor and actuator network (WSAN) applications. PICO-MP incorporates a novel publish-subscribe service that can correlate events scattered across a WSAN using global formulae specifications that are automatically checked in a distributed fashion. PICO-MP has been implemented for the TinyOS operating system and validated on a case study that uses global formulae to improve energy efficiency (lifetime) of the implementation.

Wireless sensor networks (WSNs) are an essential part of the Internet of Things (IoT). They provide a virtual layer where is possible to collect information about the physical world. WSN devices can be battery powered, produce a large volume of data and have heterogeneous hardware such as computational power, memory, and communication capabilities. Gathering data from battery powered heterogeneous devices in an energy efficient way is a challenging research area. Clustering is one of the solutions which has been proposed by researchers. In this paper we propose a novel Rotating Energy Efficient Clustering for Heterogeneous Devices (REECHD). Our experiments show that REECHD improves the network lifetime when compared to the state of art clustering protocols for heterogeneous WSNs.

Intelligent Environments (IE) are a very active area of research and a number of applications are currently being deployed in domains ranging from smart home to e-health and autonomous vehicles. In a number of cases, IE operate together with (or to support) humans, and it is therefore fundamental that IE are thoroughly verified. In this paper we present how a set of techniques and tools developed for the verification of software code can be employed in the verification of IE described by means of event-condition-action rules. In particular, we reduce the problem of verifying key properties of these rules to satisfiability and termination problems that can be addressed using state-of-the-art SMT solvers and program analysers. We introduce a tool called vIRONy that implements these techniques and we validate our approach against a number of case studies from the literature.

A Wireless Sensor Network (WSN) is composed of distributed sensors with limited processing capabilities and energy restrictions. These unique attributes pose new challenges amongst which prolonging the WSN lifetime is one of the most important. Clustering is an energy efficient routing technique that has been widely applied to report data from the WSN nodes to a centralised Base Station. A plethora of different clustering protocols have been proposed. Some protocols are based on equal-sized clusters while others use clusters of unequal size. Some others make use of rotation techniques to reduce the amount of cluster head elections. When different clustering approaches are presented different simulation settings are used. In this paper we perform a comparison study of HEED based clustering protocols that are HEED, UHEED, RUHEED and a novel variation of R-HEED that is ER-HEED. We have considered the same network model, the same energy consumption model and we have compared the lifetime of the protocols by considering various case studies. Our comparison study shows that the selection of the protocol to be used depends on the case study and the WSN lifetime measure that is considered.

Wireless Sensor Networks are becoming one of the most successful choices for the development and deployment of applications in a range of scenarios, from intelligent homes to environment monitoring. Nowadays, there is a growing demand for programming large-scale wireless sensor networks. New programming paradigms should ease the task of building WSN applications that adapt at run-time to changes in the context, in the available resources, and also in user requirements. In this paper we describe PROTEUS, a platform to manage adaptation and reconfiguration, with the aim of supporting the development of WSN applications. After introducing PROTEUS, we show how it can be used to program a dynamic clustering algorithm, where clusters are created and destroyed at runtime, and nodes need to adapt and reconfigure accordingly. We provide a prototype implementation using TinyOS. Some remarks on the work are also presented.

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors that monitor environmental data such as temperature, humidity, light, speed and sound. WSNs pose new security challenges because of their unattended nature and limited resources. Although prevention measures such as encryption and firewalls have been successfully applied, the attacker can physically access the node and modify it. Intrusion Detection Systems (IDSs) are a second line of defence that can be used to mitigate this problem. Building IDSs for WSNs is a new challenge because of the limited resources of the WSN nodes. IDS solutions for sensor networks should try to minimise the use of battery of the sensor nodes in order to prolong the network lifetime. In this paper we analyse different solutions that have been proposed for intrusion detection in wireless sensor networks. More specifically we analyse the impact of popular intrusion detection systems on the life time of the WSNs. Our study is quite general since we consider IDSs that are distributed on the sensor nodes and continuously monitor the networks for evidence of attacks. We also consider IDSs that are event triggered, which means that they require agreement between nodes when a suspicious activity is detected. The agreement is used to detect the attack and isolate the attacker. We analyse the effects of IDSs on battery life. The results show that, popular oral message algorithm of Byzantine generals problem should be considered for small scale WSNs because of the overhead introduced in terms of messages exchanged for decision. We conclude our paper with properties and recommendations for IDSs working for WSNs and some future works.

Pervasive systems are increasingly being designed using a service-oriented approach where services are distributed across wireless devices of varying capabilities. Service orchestration is a simple and popular method to coordinate web-based services but introduces a single point of failure and lacks the flexibility to cope with the greater variability of pervasive environments. Choreography in contrast advocates explicitly modelling systems as interacting peers that conform to rules of interaction. Choreography offers greater reliability and flexibility but leads to systems that are much harder to validate. In this paper we describe a novel intermediate approach, where given a logically centralised service orchestration, we automatically generate a distributed implementation that correctly enforces the orchestration behaviour. Our system handles all the synchronisation and consensus issues and ensures correctness. The system also incorporates a number of abstractions for grouping pervasive peers and coordinating pervasive peer-to-peer interactions.

This paper presents the tool DESERT that allows the generation of decentralized monitoring systems for component based applications.

We present an approach to provide intrusion detection systems (IDS) facilities into wireless sensors networks (WSN). WSNs are usually composed of a large number of low power sensors. They require a careful consumption of the available energy in order to prolong the lifetime of the network. From the security point of view, the overhead added to standard protocols must be as light as possible according to the required security level. Starting from the DESERT tool (P. Inverardi et al., 2005) which has been proposed for component-based software architectures, we derive a new framework that permits to dynamically enforce a set of properties of the sensors behavior. This is accomplished by an IDS specification that is automatically translated into few lines of code installed in the sensors. This realizes a distributed system that locally detects violation of the sensors interactions policies and is able to minimize the information sent among sensors in order to discover attacks over the network

Building a distributed system from third-party components introduces a set of problems, mainly related to compatibility and communication. Our approach to solve these problems is to build an adaptor which forces the system to exhibit only a set of safe or desired behaviors. By exploiting an abstract and partial specification of the global behavior that must be enforced, we automatically build a centralized adaptor. It mediates the interaction among components by both performing the specified behavior and, simultaneously, avoiding possible deadlocks. However in a distributed environment it is not always possible or convenient to insert a centralized adaptor. In contrast, building a distributed adaptor might increase the applicability of the approach in a real-scale context. In this paper we show how it is possible to automatically generate a distributed adaptor by exploiting an approach to the definition of distributed IDS (Intrusion Detection Systems) filters developed by us to increase security measures in component based systems. Firstly, by taking into account a high level specification of the global behavior that must be enforced, we synthesize a behavioral model of a centralized adaptor that allows the composed system to only exhibit the specified behavior and, simultaneously, avoid possible unspecified deadlocks. This model represents a lower level specification of the global behavior that is enforced by the adaptor. Secondly, by taking into account the synthesized adaptor model, we generate a set of component filters that validate the centralized adaptor behavior by simply looking at local information. In this way we address the problem of mechanically generating correct and distributed adaptors for real-scale component-based systems.