In the last two decades, one of the most important topics of discussion is the environmental problem, and to solve the latter, scientists have proposed several types of solutions. Recently Smart Grid concept has taken hold. The latter was presented in 2007 to manage electricity and integrate renewable energy sources. In that context, one of the problems is obtaining power system stability. This pertains to the ability of an electrical grid, transforming smart grid technologies, to maintain steadfast and dependable operation despite the inherent variability introduced by renewable resources. In this paper, we propose a technique to determine if a network is stable or unstable by employing several deep-learning architectures. In detail, we used a dataset retrieved from Kaggle, where all data have been converted into images thanks to the usage of a Python script designed and built by the authors. After getting the related pictures, the new dataset obtained was divided into 80-10-10 (training-validation-test). After this step, we executed several experiments where we reached an accuracy and precision of 0.998. In particular, to perform them we employed different Convolutional Neural Networks present in the state-of-the-art like AlexNet, DenseNet, LeNet, ResNet50, Standard CNN, VGG16, and VGG19. In the final step, to perform the explainability, we also employed three different CAMs Grad-CAM, Grad-CAM++, and Score-CAM, to highlight the most distinctive areas of an image.

Artificial intelligence has increasingly pervaded both everyday life and business environments, assuming a role in supporting human decision-making. These systems have grown progressively intricate and effective, carrying the potential to unearth valuable insights across various applications. Nevertheless, for Artificial Intelligence to gain widespread acceptance, human trust in its outcomes is essential. Trustworthy Artificial Intelligence is a term used to describe legal, ethically compliant, and technically sound Artificial Intelligence. In this paper, we design a proposal with the aim to introduce trustworthiness in Artificial Intelligence models. In the proposed workflow, we consider from a set of models contained in a model repository, the computation of a trustworthy index obtained from the prediction explainability in both training and testing phases. The latter is to consider the most reliable model from the trustworthy index point of view.

Data poisoning represents a set of techniques aimed at perturbing data for training machine learning models, affecting performance. Such intentional attacks are widespread in many applications involving deep learning algorithms and are aimed to provide misclassifications. In this paper, data poisoning on retinal images for the diabetic retinopathy binary classification (health and sick) is presented and evaluated. The presented attacks are almost imperceptible perturbations of the images that nevertheless decrement the metrics of the trained models. Once exposed to the data poisoning distortions on these images, a possible countermeasure to enhance the security from these attacks is shown. In this way, the robustness and vulnerabilities of the network are highlighted and the best result is also analyzed through the use of heatmaps, for the qualitative point of view. The paper aims to focus on the effects of data poisoning in deep learning model testing phase and to discuss possible countermeasures.

Over the years, the number of compromised accounts dramatically increased. To avoid this type of attack various types of authentication methods were introduced. In particular, currently, researchers are focusing on biometric-based techniques such as physical-biometric and behavior-biometric. The idea at the bottom of the last technique is that each person exhibits a unique behavior. Starting from the touch dynamics, and keyboard dynamics nowadays, one of the most promising investigation areas is currently represented by mouse dynamics. Because of the simpler technology necessary to gather biometric data without employing user sensor data, the latter has recently been a popular study area. In this paper, we propose an approach for continuous and silent user authentication based on mouse dynamics and explainable deep learning. We build a set of images using an existing dataset of mouse dynamics in CSV format. The images obtained were then used to train a deep-learning model to discriminate between legitimate and malicious users. We also adopted the Gradient-weighted Class Activation Mapping, to allow highlighting the areas of the images which are responsible for a specific legitimate/attack prediction, thus providing explainability behind the model classification. The preliminary experimental analysis based on ten different users shows that the proposed method can be promising in silent and continuous user authentication.

Many tasks that require a big workforce are automated. In many areas of the world, the consumption of utilities such as electricity, gas, and water is monitored by meters that need to be read by humans. The reading of such meters requires the presence of an employee or a representative of the utility provider. Automatic meter reading is a crucial in the implementation of smart grids. For this reason, with the aim to boost the implementation of the smart grid paradigm, in this paper we propose a method aimed to automatically read digits in a dial meter. In detail the proposed method is aimed to localise the dial meter from an image, to detect the digits and the classify the digits. Deep learning is exploited, in particular the YOLOv5s model is considered for the localisation of digits and for their recognition. An experimental real-world case study is proposed to confirm the effectiveness of the proposed method for automatic digits localisation recognition in dial meter.

Nowadays pathologists have to analyse blood cells manually with the aim to diagnose diseases. In order to perform this manual task blood samples must be collected from the patient and then placed on a microscope slide. This slide is studied with the aim to detect the abnormality presence. The manual method used to identify abnormality of blood cells is tedious, prone to human errors and time consuming. Hence there is a need for computer aided system which can analyze blood cells automatically at faster rate with accuracy. Such systems can be designed using image processing techniques. To automatise this process by helping pathologists, in this paper we propose the adoption of deep learning to automatically count and localise red blood cells, white blood cells and platelets by analysing blood microscopic images. We resort to the YOLO object detection model, able to look at the whole image so its predictions are informed by global context in the image. To show the effectiveness of the proposed method we evaluate our model on a dataset composed by 874 microscopic blood images, obtaining interesting results. Furthermore we show several examples related to how the proposed method can be helpful for the pathologists in their real-world work.

The application collusion attack is a new form of threat that is becoming widespread in mobile environment. This technique requires that two or more apps cooperate in some way with the aim to perform a malicious action that they are unable to perform independently. In this paper we propose the adoption of model checking to detect whether two or more apps are performing a collusion attack.

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose the adoption of formal methods to discriminate between different users by exploiting a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we consider a feature vector able to capture the typing style that is specific to each given user. By considering this feature model, in detail we propose to consider model checking with logic temporal properties to discriminate between different users using a set of keystroke features.

This paper proposes a machine-learning framework for supporting intelligent web phishing detection and analysis, and provides its experimental evaluation. In particular we make use of state-of-the-art decision tree algorithms for detecting whether a Web site is able to perform phishing activities. If this is the case, the Web site is classified as a Web-phishing site. Our experimental evaluation confirms the benefits of applying machine learning methods to the well-known web-phishing detection problem.

Considering the widespread diffusion of machine learning techniques to solve several issues, from network security to malware detection, in this paper we propose the adoption of word embeddings aimed to improve the machine learning-based classifiers. Three real-world experiment we perform in order to demonstrate that the proposed method overcomes in terms of performances the mostly used machine-learning algorithms.

Considering the widespread diffusion of mobile applications in many aspects of our life, one of the most required developer's skill is related to mobile applications development, with particular regard to the Android environment. In this paper, we present a framework aimed to quantify students learning ability in mobile applications development. The proposed framework can be useful also to teachers in order to improve their teaching method.

Phishing is a technique aimed to imitate an official websites of any company such as banks, institutes, etc. The purpose of phishing is to theft private and sensitive credentials of users such as password, username or PIN. Phishing detection is a technique to deal with this kind of malicious activity. In this paper we propose a method able to discriminate between web pages aimed to perform phishing attacks and legitimate ones. We exploit state of the art machine learning algorithms in order to build models using indicators that are able to detect phishing activities.

Tor is an anonymous Internet communication system based on the second generation of onion routing network protocol. Using Tor is really difficult to trace the users Internet activity: this is the reason why the usage of Tor is intended in order to protect the privacy of users, their freedom and the ability to conduct confidential communications without being monitored. Tor is even more used by cyber-criminals in order to cover their illegal activities: the Tor community has observed, for instance an alarming increase in the number of malware that abuse of the popular anonymizing network to hide their command and control infrastructures. In this paper we present a technique able to identify whether an host is generating Tor-related traffic. We resort to well-known machine learning algorithms in order to evaluate the effectiveness of the proposed feature set in a real world environment. In addition we demonstrate that the proposed method is able to recognize the kind of activity (e.g., email or P2P applications) the user under analysis is doing on the Tor network.

We propose a mobile implementation of an e-voting protocol. We also provide a formal analysis to validate a security property of our system.