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AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.

Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack., Comment: cite as: Alwaisi, Z., Soderi, S., De Nicola, R. (2023). Mitigating and Analysis of Memory Usage Attack in IoE System. In: Vo, NS., Tran, HA. (eds) Industrial Networks and Intelligent Systems. INISCOM 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-47359-3_22

With the rapid development of Internet of Things (IoT) technology, intelligent systems are increasingly integrating into everyday life and people's homes. However, the proliferation of these technologies raises concerns about the security of smart home devices. These devices often face resource constraints and may connect to unreliable networks, posing risks to the data they handle. Securing IoT technology is crucial due to the sensitive data involved. Preventing energy attacks and ensuring the security of IoT infrastructure are key challenges in modern smart homes. Monitoring energy consumption can be an effective approach to detecting abnormal behavior and IoT cyberattacks. Lightweight algorithms are necessary to accommodate the resource limitations of IoT devices. This paper presents a lightweight technique for detecting energy consumption attacks on smart home devices by analyzing received packets. The proposed algorithm considers TCP, UDP, and MQTT protocols, as well as device statuses (Idle, active, under attack). It accounts for resource constraints and promptly alerts administrators upon detecting an attack. The proposed approach effectively identifies energy consumption attacks by measuring packet reception rates for different protocols., Comment: Alwaisi, Z., Soderi, S., De Nicola, R. (2024). Detection of Energy Consumption Cyber Attacks on Smart Devices. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 542. Springer, Cham. 10.1007/978-3-031-50051-0_12

The Internet of Things (IoT) has garnered significant interest in both research and industry due to its profound impact on human life. The rapid expansion of IoT technology has ushered in smart healthcare, smart devices, smart cities, and smart grids. However, the security of IoT devices, particularly in healthcare, has become a major concern, with recent attacks revealing serious vulnerabilities. In IoT networks, where connected devices are susceptible to resource-constraint attacks, such as energy consumption attacks, security is paramount. This paper explores the impact of Distributed Denial of Service (DDoS) and Fake Access Points (F-APs) attacks on WiFi-enabled smart healthcare devices. Specifically, it investigates how these attacks can disrupt service on victim devices and Access Points (APs), focusing on device connectivity and energy consumption during attacks. Key findings include identifying the attack rates of DDoS attacks that disrupt services and quantifying the energy consumption impact of Energy Consumption Distributed Denial of Service (EC-DDoS) and F-APs attacks on smart healthcare devices. The study highlights communication protocols, attack rates, payload sizes, and port states of victim devices as critical factors influencing energy consumption. These insights provide a comprehensive understanding of IoT device vulnerabilities in smart healthcare environments and lay the groundwork for future defense strategies., Comment: Cite: Alwaisi, Z., Soderi, S., Nicola, R.D. (2023). Energy Cyber Attacks to Smart Healthcare Devices: A Testbed. In: Chen, Y., Yao, D., Nakano, T. (eds) Bio-inspired Information and Communications Technologies. BICT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-031-43135-7_24

Covert channel networks are a well-known method for circumventing the security measures organizations put in place to protect their networks from adversarial attacks. This paper introduces a novel method based on bit-rate modulation for implementing covert channels between devices connected over a wide area network. This attack can be exploited to exfiltrate sensitive information from a machine (i.e., covert sender) and stealthily transfer it to a covert receiver while evading network security measures and detection systems. We explain how to implement this threat, focusing specifically on covert channel networks and their potential security risks to network information transmission. The proposed method leverages bit-rate modulation, where a high bit rate represents a '1' and a low bit rate represents a '0', enabling covert communication. We analyze the key metrics associated with covert channels, including robustness in the presence of legitimate traffic and other interference, bit-rate capacity, and bit error rate. Experiments demonstrate the good performance of this attack, which achieved 5 bps with excellent robustness and a channel capacity of up to 0.9239 bps/Hz under different noise sources. Therefore, we show that bit-rate modulation effectively violates network security and compromises sensitive data.

For U.S. presidential elections, most states use the so-called winner-take-all system, in which the state's presidential electors are awarded to the winning political party in the state after a popular vote phase, regardless of the actual margin of victory. Therefore, election campaigns are especially intense in states where there is no clear direction on which party will be the winning party. These states are often referred to as swing states. To measure the impact of such an election law on the campaigns, we analyze the Twitter activity surrounding the 2020 US preelection debate, with a particular focus on the spread of disinformation. We find that about 88% of the online traffic was associated with swing states. In addition, the sharing of links to unreliable news sources is significantly more prevalent in tweets associated with swing states: in this case, untrustworthy tweets are predominantly generated by automated accounts. Furthermore, we observe that the debate is mostly led by two main communities, one with a predominantly Republican affiliation and the other with accounts of different political orientations. Most of the disinformation comes from the former., Comment: arXiv admin note: text overlap with arXiv:2303.12474

For more than a decade scholars have been investigating the disinformation flow on social media contextually to societal events, like, e.g., elections. In this paper, we analyze the Twitter traffic related to the US 2020 pre-election debate and ask whether it mirrors the electoral system. The U.S. electoral system provides that, regardless of the actual vote gap, the premier candidate who received more votes in one state `takes' that state. Criticisms of this system have pointed out that election campaigns can be more intense in particular key states to achieve victory, so-called {\it swing states}. Our intuition is that election debate may cause more traffic on Twitter-and probably be more plagued by misinformation-when associated with swing states. The results mostly confirm the intuition. About 88\% of the entire traffic can be associated with swing states, and links to non-trustworthy news are shared far more in swing-related traffic than the same type of news in safe-related traffic. Considering traffic origin instead, non-trustworthy tweets generated by automated accounts, so-called social bots, are mostly associated with swing states. Our work sheds light on the role an electoral system plays in the evolution of online debates, with, in the spotlight, disinformation and social bots., Comment: 9 pages, 2 figures; Accepted @CySoc 2023, International Workshop on Cyber Social Threats, co-located with the ACM Web conference 2023, April 30, 2023. The present version is a preprint

Thanks to platforms such as Twitter and Facebook, people can know facts and events that otherwise would have been silenced. However, social media significantly contribute also to fast spreading biased and false news while targeting specific segments of the population. We have seen how false information can be spread using automated accounts, known as bots. Using Twitter as a benchmark, we investigate behavioural attitudes of so called `credulous' users, i.e., genuine accounts following many bots. Leveraging our previous work, where supervised learning is successfully applied to single out credulous users, we improve the classification task with a detailed features' analysis and provide evidence that simple and lightweight features are crucial to detect such users. Furthermore, we study the differences in the way credulous and not credulous users interact with bots and discover that credulous users tend to amplify more the content posted by bots and argue that their detection can be instrumental to get useful information on possible dissemination of spam content, propaganda, and, in general, little or no reliable information., Comment: Under submission

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of d/misinformation campaigns, but differently from what already witnessed in other debates, the COVID-19 -- intentional or not -- flow of false information put at severe risk the public health, reducing the effectiveness of governments' countermeasures. In the present manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract the discursive communities, we focus on verified users, i.e. accounts whose identity is officially certified by Twitter. We thus infer the various discursive communities based on how verified users are perceived by standard ones: if two verified accounts are considered as similar by non unverified ones, we link them in the network of certified accounts. We first observe that, beside being a mostly scientific subject, the COVID-19 discussion show a clear division in what results to be different political groups. At this point, by using a commonly available fact-checking software (NewsGuard), we assess the reputation of the pieces of news exchanged. We filter the network of retweets (i.e. users re-broadcasting the same elementary piece of information, or tweet) from random noise and check the presence of messages displaying an url. The impact of misinformation posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable urls shared by political groups come from this community., Comment: 25 pages, 4 figures. The Abstract, the Introduction, the Results, the Conclusions and the Methods were substantially rewritten. The plot of the network have been changed, as well as tables

Recent studies have confirmed a growing trend, especially among youngsters, of using Online Social Media as favourite information platform at the expense of traditional mass media. Indeed, they can easily reach a wide audience at a high speed; but exactly because of this they are the preferred medium for influencing public opinion via so-called fake news. Moreover, there is a general agreement that the main vehicle of fakes news are malicious software robots (bots) that automatically interact with human users. In previous work we have considered the problem of tagging human users in Online Social Networks as credulous users. Specifically, we have considered credulous those users with relatively high number of bot friends when compared to total number of their social friends. We consider this group of users worth of attention because they might have a higher exposure to malicious activities and they may contribute to the spreading of fake information by sharing dubious content. In this work, starting from a dataset of fake news, we investigate the behaviour and the degree of involvement of credulous users in fake news diffusion. The study aims to: (i) fight fake news by considering the content diffused by credulous users; (ii) highlight the relationship between credulous users and fake news spreading; (iii) target fake news detection by focusing on the analysis of specific accounts more exposed to malicious activities of bots. Our first results demonstrate a strong involvement of credulous users in fake news diffusion. This findings are calling for tools that, by performing data streaming on credulous' users actions, enables us to perform targeted fact-checking., Comment: 15 pages and 8 tables. Accepted to appear in the Proceedings at IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS2020)

We review some results regarding specification, programming and verification of different classes of distributed systems which stemmed from the research of the Concurrency and Mobility Group at University of Firenze. More specifically, we examine the distinguishing features of network-aware programming, service-oriented computing, autonomic computing, and collective adaptive systems programming. We then present an overview of four different languages, namely Klaim, Cows, Scel and AbC. For each language, we discuss design choices, present syntax and semantics, show how the different formalisms can be used to model and program a travel booking scenario, and describe programming environments and verification techniques., Comment: In Press

Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users; even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. The best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results., Comment: 8 pages, 2 tables. Accepted for publication at IDEAL 2019 (20th International Conference on Intelligent Data Engineering and Automated Learning, Manchester, UK, 14-16 November, 2019). The present version is the accepted version, and it is not the final published version

Social Media are nowadays the privileged channel for information spreading and news checking. Unexpectedly for most of the users, automated accounts, also known as social bots, contribute more and more to this process of news spreading. Using Twitter as a benchmark, we consider the traffic exchanged, over one month of observation, on a specific topic, namely the migration flux from Northern Africa to Italy. We measure the significant traffic of tweets only, by implementing an entropy-based null model that discounts the activity of users and the virality of tweets. Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers. The retwitting activity of such automated accounts amplifies the presence on the platform of the hubs' messages., Comment: Under Submission

Modern systems evolve in unpredictable environments and have to continuously adapt their behavior to changing conditions. The "DReAM" (Dynamic Reconfigurable Architecture Modeling) framework, has been designed for modeling reconfigurable dynamic systems. It provides a rule-based language, inspired from Interaction Logic, which is expressive and easy to use encompassing all aspects of dynamicity including parametric multi-modal coordination with creation/deletion of components as well as mobility. Additionally, it allows the description of both endogenous/modular and exogenous/centralized coordination styles and sound transformations from one style to the other. The DReAM framework is implemented in the form of a Java API bundled with an execution engine. It allows to develop runnable systems combining the expressiveness of the rule-based notation together with the flexibility of this widespread programming language., Comment: 29 pages, 10 figures

Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour., Comment: Changed formatting

We propose a process calculus, named AbC, to study the behavioural theory of interactions in collective-adaptive systems by relying on attribute-based communication. An AbC system consists of a set of parallel components each of which is equipped with a set of attributes. Communication takes place in an implicit multicast fashion, and interaction among components is dynamically established by taking into account "connections" as determined by predicates over their attributes. The structural operational semantics of AbC is based on Labeled Transition Systems that are also used to define bisimilarity between components. Labeled bisimilarity is in full agreement with a barbed congruence, defined by simple basic observables and context closure. The introduced equivalence is used to study the expressiveness of AbC in terms of encoding broadcast channel-based interactions and to establish formal relationships between system descriptions at different levels of abstraction., Comment: 30 pages, preprint submitted to Elsevier. arXiv admin note: text overlap with arXiv:1711.06092 and arXiv:1602.05635

Collective adaptive systems are new emerging computational systems consisting of a large number of interacting components and featuring complex behaviour. These systems are usually distributed, heterogeneous, decentralised and interdependent, and are operating in dynamic and possibly unpredictable environments. Finding ways to understand and design these systems and, most of all, to model the interactions of their components, is a difficult but important endeavour. In this article we propose a language-based approach for programming the interactions of collective-adaptive systems by relying on attribute-based communication; a paradigm that permits a group of partners to communicate by considering their run-time properties and capabilities. We introduce AbC, a foundational calculus for attribute-based communication and show how its linguistic primitives can be used to program a complex and sophisticated variant of the well-known problem of Stable Allocation in Content Delivery Networks. Also other interesting case studies, from the realm of collective-adaptive systems, are considered. We also illustrate the expressive power of attribute-based communication by showing the natural encoding of other existing communication paradigms into AbC.

Variants of the must testing approach have been successfully applied in service oriented computing for analysing the compliance between (contracts exposed by) clients and servers or, more generally, between two peers. It has however been argued that multiparty scenarios call for more permissive notions of compliance because partners usually do not have full coordination capabilities. We propose two new testing preorders, which are obtained by restricting the set of potential observers. For the first preorder, called uncoordinated, we allow only sets of parallel observers that use different parts of the interface of a given service and have no possibility of intercommunication. For the second preorder, that we call individualistic, we instead rely on parallel observers that perceive as silent all the actions that are not in the interface of interest. We have that the uncoordinated preorder is coarser than the classical must testing preorder and finer than the individualistic one. We also provide a characterisation in terms of decorated traces for both preorders: the uncoordinated preorder is defined in terms of must-sets and Mazurkiewicz traces while the individualistic one is described in terms of classes of filtered traces that only contain designated visible actions and must-sets.

Among the paradigms for parallel and distributed computing, the one popularized with Linda, and based on tuple spaces, is one of the least used, despite the fact of being intuitive, easy to understand and to use. A tuple space is a repository, where processes can add, withdraw or read tuples by means of atomic operations. Tuples may contain different values, and processes can inspect their content via pattern matching. The lack of a reference implementation for this paradigm has prevented its widespread. In this paper, first we perform an extensive analysis of a number of actual implementations of the tuple space paradigm and summarise their main features. Then, we select four such implementations and compare their performances on four different case studies that aim at stressing different aspects of computing such as communication, data manipulation, and cpu usage. After reasoning on strengths and weaknesses of the four implementations, we conclude with some recommendations for future work towards building an effective implementation of the tuple space paradigm.

In open systems, i.e. systems operating in an environment that they cannot control and with components that may join or leave, behaviors can arise as side effects of intensive components interaction. Finding ways to understand and design these systems and, most of all, to model the interactions of their components, is a difficult but important endeavor. To tackle these issues, we present AbC, a calculus for attribute-based communication. An AbC system consists of a set of parallel agents each of which is equipped with a set of attributes. Communication takes place in an implicit multicast fashion, and interactions among agents are dynamically established by taking into account "connections" as determined by predicates over the attributes of agents. First, the syntax and the semantics of the calculus are presented, then expressiveness and effectiveness of AbC are demonstrated both in terms of modeling scenarios featuring collaboration, reconfiguration, and adaptation and of the possibility of encoding channel-based interactions and other interaction patterns. Behavioral equivalences for AbC are introduced for establishing formal relationships between different descriptions of the same system., Comment: Extended Technical Report, All proofs are included in the appendix

In this paper we present CARMA, a language recently defined to support specification and analysis of collective adaptive systems. CARMA is a stochastic process algebra equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals. A CARMA model, termed a collective, consists of a set of components, each of which exhibits a set of attributes. To model dynamic aggregations, which are sometimes referred to as ensembles, CARMA provides communication primitives that are based on predicates over the exhibited attributes. These predicates are used to select the participants in a communication. Two communication mechanisms are provided in the CARMA language: multicast-based and unicast-based. In this paper, we first introduce the basic principles of CARMA and then we show how our language can be used to support specification with a simple but illustrative example of a socio-technical collective adaptive system., Comment: In Proceedings QAPL 2015, arXiv:1509.08169

Major search engines deploy personalized Web results to enhance users' experience, by showing them data supposed to be relevant to their interests. Even if this process may bring benefits to users while browsing, it also raises concerns on the selection of the search results. In particular, users may be unknowingly trapped by search engines in protective information bubbles, called "filter bubbles", which can have the undesired effect of separating users from information that does not fit their preferences. This paper moves from early results on quantification of personalization over Google search query results. Inspired by previous works, we have carried out some experiments consisting of search queries performed by a battery of Google accounts with differently prepared profiles. Matching query results, we quantify the level of personalization, according to topics of the queries and the profile of the accounts. This work reports initial results and it is a first step a for more extensive investigation to measure Web search personalization., Comment: In Proceedings WWV 2015, arXiv:1508.03389