In the smart grid ecosystem, the automated reading of water meters is a critical task that can be accomplished with deep learning algorithms instead of the traditional way that relies on the physical presence of employees or utility representatives. The YOLO (You Only Look Once) family of models has gained prominence among these approaches because of its excellent accuracy and quick processing time. In this study, we compare the performance of four YOLO models for automated reading in water metering systems (YOLOv5, YOLOv6, YOLOv7, and YOLOv8). We assess the model's precision, recall, and processing time using a custom dataset of water meter images. In terms of precision and processing time, our testing results demonstrate that YOLOv8 surpasses YOLOv7, YOLOv6, and YOLOv5 by scoring 0.889 in precision, making it a suitable alternative for automated reading in smart metering systems. This benchmarking study provides a valuable reference for researchers and practitioners in the field of smart grid and deep learning, and it can aid in the selection of the most appropriate model for this task.

The COVID-19 pandemic has radically changed our daily habits. One of these habits introduced is the use of a face mask, to avoid the spread of the infection, to be used especially in closed and crowded environments. To protect people and to counter the spread of COVID-19, in this paper we propose a method to automatically verify from images whether people are wearing a face mask. We designed a deep learning network to classify whether an image under analysis is with a mask or without a face mask. With the aim to provide explainability to the classifier decision, the proposed method is able to localise the areas of the image under analysis symptomatic of a certain prediction: in this way it is possible to understand the reason why the model predicts a certain label. The experimental analysis considers 7553 (with mask and without mask) images showing that the proposed approach is able to obtain interesting performances in face mask detection.

In light of the growing reliance on digital technology, the security of digital devices and networks has become a critical concern in the information technology industry. Network analysis can be helpful for identifying and mitigating network-based attacks, as it enables the monitoring of network behavior and the detection of anomalous activity. Through the use of network analysis, organizations can better defend against potential security threats and protect their interconnected digital systems.

Mobile malware are increasing their complexity to be able to evade the current detection mechanism by gathering our sensitive and private information. For this reason, an active research field is represented by malware detection, with a great effort in the development of deep learning models starting from a set of malicious and legitimate applications. The recent introduction of quantum computing made possible quantum machine learning i.e., the integration of quantum algorithms within machine learning algorithms. In this paper, we propose a comparison between several deep learning models, by taking into account also a hybrid quantum malware detector. We explore the effectiveness of different architectures for malicious family detection in the Android environment: LeNet, AlexNet, a Convolutional Neural Network model designed by authors, VGG16 and a Hybrid Quantum Convolutional Neural Network i.e., a model where the first layer is a quantum convolution that uses transformations in circuits to simulate the behavior of a quantum computer. Experiments performed on a real-world dataset composed of 8446 Android malicious and legitimate applications allow us to compare the various models, with particular regard to the quantum model concerning the other ones.

Cardiovascular diseases include a very long series of diseases that afflict many people in the world. Many of them can be diagnosed by listening to the heartbeat, however in the face of the large number of patients performing checks, great delays can occur given the few doctors available. In this paper we propose a convolutional neural network aimed to discriminate regular heartbeats from abnormal ones, making a first screening of patients. Moreover we provide classification explainability through activation maps. The experimental analysis consists of 3240 (regular and abnormal) heartbeat phonocardiogram signals, showing the effectiveness of the proposed method.

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.

Deep Learning models demonstrated high accuracies performance in malware classification, but they are still lacking "explainability" to ensure robustness and reliability in the generated prediction. In this short contribution, we summarize the researches that we conducted in the latest years in the Malware Analysis field.

The new coronavirus disease (COVID-19), declared a pandemic on 11 March 2020 by the World Health Organization, has caused over 6 million victims worldwide. Because of the rapid spread of the virus, with the aim to perform screening we exploit deep learning model to quickly diagnose altered respiratory conditions. In this paper, we propose a method to recognize and classify cough audio files into three classes to distinguish patients with COVID-19 disease, symptomatic ones and healthy subjects, with the use of a convolutional neural network (CNN). Cough audios were recorded by using a smartphone and its built-in microphone. From cough recordings, we generate spectrogram images and we obtain an accuracy equal to 0.82 with a deep learning network developed by authors. Our method also provides heatmaps, which show the relevant input areas used by the model for the final forecast, and this aspect ensures the explainability of the method.

Over the years the number of fraud credential attacks increased drastically. To avoid this type of attack many types of authentication methods have been introduced, in particular, recently taken hold biometric-based such as physical-biometric and behavior-biometric. The idea at the bottom of the latter is that each person has a unique behavior. Starting from the touch dynamics, and keyboard dynamics nowadays, the main topic of study is mouse dynamics. Unlike the other techniques, mouse dynamics require simpler hardware to capture the biometric data without using sensitive data from the users. In this paper, we propose a method based on mouse dynamics and explainable deep learning for continuous and silent user authentication. We propose four different images obtained starting from mouse dynamics and we submit these images to several deep learning models obtaining interesting results in user behavior detection. Moreover, we propose the adoption of the Gradient-weighted Class Activation Mapping to highlight the areas of the images under study that is responsible for a specific categorization to explain the model decision.

Diabetic retinopathy is a disease afflicting the retina and currently is manually diagnosed by specialists through eye tomography inspection. In order to assist the clinician in this time-consuming task, in this paper, we propose a method aimed to automatically diagnose the (proliferative and non-proliferative) diabetic retinopathy by exploiting deep learning. Furthermore, we investigate the possibility to automatically localise the areas related to the disease by exploiting class activation maps. We evaluate different deep learning models from a quantitative point of view (i.e, using metrics like accuracy, precision and recall) and a qualitative point of view (by exploiting class activation maps and image similarity metrics) with the aim to understand the quality of predictions performed by a model in retinopathy diagnosis, reducing the amount of knowledge required to assess the model performance. From the experimental analysis is emerging that deep learning shows an interesting diagnostic potential in the retinopathy disease localisation and can effectively help the clinician in retinopathy diagnosis. Moreover, the adoption of the class activation maps and its comparison evaluation can help the developers to debug the training step of the model without medical expertise.

Shallow machine learning is massively applied by researchers with the aim to detect (novel and unseen) malicious applications. Machine learning models are typically evaluated using malicious and trusted applications generated over a short period. In the real world, these models aim to identify malware that were not seen previously during the training phase. In this paper, we investigate how well machine learning-based malware detectors can actually detect malware in the real-world environment. By representing an Android application in terms of image, we evaluate the resilience of several popular supervised machine learning algorithms exploited by current literature for the malware detection task. The experimental results demonstrate the poor resilience of the machine learning models used for malware detection.

As a result of the spread in the mobile market, new kinds of malware have developed. Many malicious users began to produce harmful applications for open-source operating systems such as Android, and closed-source operating systems like iOS. For this reason, new anti-malware methodologies are necessary to identify them. Currently, most of them base their approach on the signature, and it does not allow users to defend themselves from current threats. In this article, we propose an automatic tool able to identify dangerous iOS applications by defining a system model through Milner's Calculus of Communicating Systems and the consequence use of the State Transitions System to evaluate the behaviors of an application, to categorize if it is malware or a legitimate application. As a result of the experiments conducted, we obtained relevant performances with good precision and recall levels.

We propose a malware classification system that is shown to be robust to some common intra-procedural obfuscation techniques.Indeed, by training the Contextual Graph Markov Model on the call graph representation of a program, we classify it using only topological information, which is unaffected by such obfuscations.In particular, we show that the structure of the call graph is sufficient to achieve good accuracy on a multi-class classification benchmark.

Cybercriminals are continually working to develop increasingly aggressive malicious code to steal sensitive and private information from mobile devices. Antimalware are not always able to detect all threats, especially when they do not have previous knowledge of the malware signature. Moreover, malware code analysis remains a time-consuming process for security analysts. In this regard, we propose a method aimed to detect the malware belonging family and automatically pointing out a subset of potentially malicious classes. The rationale behind this work aims (i) to save valuable time for the security analyst by decreasing the amount of code to analyse, and (ii) to improve the interpretability of image-based deep learning model for malware family detection. We represent an application as an image and classify it with a deep learning model aimed to predict the belonging family; then, exploiting the use of activation maps, the approach points out potentially malicious classes to help the security analysts in the malicious behaviour recognition. The proposed method obtains an overall accuracy of 0.944 in the evaluation of a dataset composed of 8430 real-world Android malware, showing also that the use of activation maps can provide explainability about the deep learning model decision.