In Smart Factories, Automated Guided Vehicles(AGVs) accomplish heterogeneous tasks as moving objects, restoring connectivity or performing different manufacturing activities into production-lines. These kinds of devices combine several capabilities, as artificial intelligence (visual and speech recognition, automatic fault detecting, pro-active behavior) and mobility,into the so-called "mobile intelligence". A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents, mutually interacting on their behalf. Here, to satisfy some basic requirements as effectiveness and efficiency, it is often necessary to organize ad hoc teams of free-moving vehicles, sensors and smart devices to reach a given target. Therefore, a specific issue consists inadequately representing the reciprocal agent/device trustworthiness for advantaging such team formation processes in a smart factory environment. To this end, in this paper(i)we define a trust measure based on reliability and reputation of AGVs, which are computed based on the feedbacks released for the AGVs activities in the factory;(ii)we design a framework exploiting the defined trust measures to support the formation of virtual,temporary and trust-based teams of mobile intelligent devices;and(iii)we present a set of experimental results highlighting that the proposed trust framework can improve the workshop performance in terms of effectiveness and efficiency

Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agent's decision. In the past, we proposed a reliability–reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well-known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF.

In today’s interconnected world, cybersecurity has emerged as a critical domain for ensuring the integrity, confidentiality, and availability of digital assets. Within this sphere, insider threats represent a unique and particularly insidious class of security risks, originating not from external hackers but from within the organization itself. These threats are perpetrated by individuals with inside information concerning the organization’s security practices, data, and computer systems. Traditional security measures like firewalls, intrusion detection systems, and antivirus software are often inadequate for tackling insider threats effectively, owing to their focus on external threats. This inadequacy underscores the urgent need for the development and implementation of more sophisticated, targeted detection techniques for insider threats. In response to this challenge, our research introduces a groundbreaking approach that employs the Density-Based Local Outlier Factor (DBLOF) algorithm, fine-tuned to specifically tackle the challenges posed by the imbalanced nature of the CERT r4.2 insider threat dataset. This dataset is characterized by a highly skewed distribution, with a significant majority of benign instances and only a minimal proportion of malicious activities. Conventional detection algorithms often fail to effectively identify these rare but dangerous instances, leading to a high rate of false negatives. Our methodology capitalizes on the algorithm’s ability to focus on the local density deviation of data points, thereby enabling the precise identification of outliers that are indicative of potential insider threats. Through rigorous testing and validation processes, we have achieved outstanding results, with an of F-score 98%. These remarkable outcomes not only affirm the effectiveness of the DBLOF algorithm as a powerful tool for combating insider threats but also contribute valuable insights to the broader academic and professional discourse on cybersecurity. Importantly, our findings have practical implications, offering organizations actionable recommendations for boosting their internal security mechanisms against the complex and evolving landscape of insider threats.