Massimo GuarascioItalian National Research Council | CNR · Institute for High Performance Computing and Networking ICAR
Massimo Guarascio
phd in System and Computer Engineering
About
66
Publications
6,254
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539
Citations
Citations since 2017
Introduction
Massimo Guarascio holds a Ph.D. in System and Computer Engineering. He is currently researcher at the italian National Council of Research (CNR). His research mainly focuses on deep learning and machine learning, anomaly detection and explanation, process mining, data analytics methods for geosciences and remote sensing, AI-Based techniques for cyber security and fraud detection.
He was involved in several projects including CyberSec4Europe, Italian Cyber Security District and Secure Open Nets.
Additional affiliations
February 2015 - present
September 2006 - January 2015
Education
November 2006 - February 2011
October 2003 - July 2006
Publications
Publications (66)
Predicting the final outcome of an ongoing process instance is a key problem in many real-life contexts. This problem has been addressed mainly by discovering a prediction model by using traditional machine learning methods and, more recently, deep learning methods, exploiting the supervision coming from outcome-class labels associated with histori...
Sharing threat events and Indicators of Compromise (IoCs) enables quick and crucial decision making relative to effective countermeasures against cyberattacks. However, the current threat information sharing solutions do not allow easy communication and knowledge sharing among threat detection systems (in particular Intrusion Detection Systems (IDS...
Accurate rainfall estimation is crucial to adequately assess the risk associated with extreme events capable of triggering floods and landslides. Data gathered from Rain Gauges (RGs), sensors devoted to measuring the intensity of the rain at individual points, are commonly used to feed interpolation methods (e.g., the Kriging geostatistical approac...
The widespread adoption of Artificial Intelligence and Machine Learning tools opens to security issues that can raise and occur when the underlying ML models integrated into advanced services. The models, in fact, can be compromised in both the learning and the deployment stage. In this work, we provide an overview of some strenuous security risks...
Classification-oriented Machine Learning methods are a precious tool, in modern Intrusion Detection Systems (IDSs), for discriminating between suspected intrusion attacks and normal behaviors. Many recent proposals in this field leveraged Deep Neural Network (DNN) methods, capable of learning effective hierarchical data representations automaticall...
Detecting deviant traces in business process logs is a crucial task in modern organizations due to the detrimental effect of certain deviant behaviors (e.g., attacks, frauds, faults). Training a Deviance Detection Model (DDM) only over labeled traces with supervised learning methods unfits real-life contexts where a small fraction of the traces are...
Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly orchestrate botnets composed of simple devices. Therefore, we illustrate a solution based on Deep Learning for the detection of cove...
Providing rich and accurate metadata for indexing media content is a crucial problem for all the companies offering streaming entertainment services. These metadata are typically used to improve the result of search engines and to feed recommendation algorithms in order to yield recommendation lists matching user interests. In particular, the probl...
Steganography is increasingly exploited by malware to avoid detection and to implement different advanced offensive schemes. An attack paradigm expected to become widely used in the near future concerns cloaking data in innocent-looking pictures, which are normally used by several devices and applications, for instance to enhance the user experienc...
Providing rich and accurate metadata for indexing media content represents a major issue for enterprises offering streaming entertainment services. Metadata information are usually exploited to boost the search capabilities for relevant contents and as such it can be used by recommendation algorithms for yielding recommendation lists matching user...
Intelligent Ticket Management Systems, equipped with automated ticket classification tools, are an advanced solution for handling customer-support activities. Some recent approaches to ticket classification leverage Deep Learning (DL) methods, in place of traditional ones using standard Machine Learning and feature engineering techniques. However,...
Steganographic techniques and covert channels are becoming exploited by a wide-range of malware to avoid detection and bypass network security tools. With the ubiquitous diffusion of IoT nodes, such offensive schemes are expected to be used to exfiltrate data or to covertly orchestrate botnets composed of resource-constrained nodes (e.g., as it hap...
This paper presents an ongoing work on project MAP4ID "Multipurpose Analytics Platform 4 Industrial Data", where one of the objectives is to propose suitable combinations of machine learning and Answer Set Programming (ASP) to cope with industrial problems. In particular, we focus on a specific use case of the project, where we combine deep learnin...
In recent years, steganographic techniques have become increasingly exploited by malware to avoid detection and remain unnoticed for long periods. Among the various approaches observed in real attacks, a popular one exploits embedding malicious information within innocent-looking pictures. In this paper, we present a machine learning technique for...
Providing an accurate rainfall estimate at individual points is a challenging problem in order to mitigate risks derived from severe rainfall events, such as floods and landslides. Dense networks of sensors, named rain gauges (RGs), are typically used to obtain direct measurements of precipitation intensity in these points. These measurements are u...
Distributed Ledger technologies are becoming a standard for the management of online transactions, mainly due to their capability to ensure data privacy, trustworthiness and security. Still, they are not immune to security issues, as witnessed by recent successful cyber-attacks. Under a statistical perspective, attacks can be characterized as anoma...
In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Although
the blockchain is a powerful tool, it is not immune to cyber attacks: for instance, recently
(January 2019) a successful 51% attack on Ethereum Classic has revealed security v...
Mining deviances from expected behaviors in process logs is a relevant problem in modern organizations, owing to their negative impact in terms of monetary/reputation losses. Most proposals to deviance mining combine the extraction of behavioral features from log traces with the induction of standard classifiers. Difficulties in capturing the multi...
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Process Mining (PM) is meant to extract knowledge on the behavior of business processes from historical log data. Lately, an increasing attention has been gained by the Predictive Process Monitoring, a field of PM that tries to extend process monitoring systems with prediction capabilities and, in particular. Several current proposals in literature...
Rain gauges are sensors providing direct measurement of precipitation intensity at individual point sites, and, usually, spatial interpolation methods are used to obtain an estimate of the precipitation field over the entire area of interest. Among them, Kriging with External Drift (KED) is a largely used and well-recognized method in this field. H...
Evolutionary algorithms, i.e., Genetic Programming (GP), have been successfully used for the task of classification, mainly because they are less likely to get stuck in the local optimum, can operate on chunks of data and allow to compute more solutions in parallel. Ensemble techniques are usually more accurate than component learners constituting...
Process Discovery techniques, allowing to extract graph-like models from large process logs, are a valuable mean for grasping a summarized view of real business processes’ behaviors. If augmented with statistics on process performances (e.g., processing times), such models help study the evolution of process performances across different processing...
Business Process Intelligence (BPI) and Process Mining, two very active research areas of research, share a great interest towards the issue of discovering an effective Deviance Detection Model (DDM), computed via accessing log data. The DDM model allows us to understand whether novel instances of the target business process are deviant or not, thu...
In many application contexts, a business process' executions are subject to performance constraints expressed in an aggregated form, usually over predefined time windows, and detecting a likely violation to such a constraint in advance could help undertake corrective measures for preventing it. This paper illustrates a prediction-aware event proces...
Monitoring the performances of a business process is a key issue in many organizations, especially when the process must comply with predefined performance constraints. In such a case, empowering the monitoring system with prediction capabilities would allow us to know in advance a constraint violation, and possibly trigger corrective measures to e...
The chapter is devoted at illustrating the basic principles and the current results which characterize the research on Deep Learning. The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks. Within the chapter, we illustrate the basic principle underlying the idea of a single...
The Human Behavioral Analysis is a growing research area due to its big impact on several scientific and industrial applications. One of the most popular family of techniques addressing this problem is the Latent Factor Modeling which aims at identifying interesting features that determine human behavior. In most cases, latent factors are used to r...
Increasing attention has been paid to the problem of explaining and analyzing "deviant cases" generated by a business process, i.e. instances of the process that diverged from prescribed/expected behavior (e.g. frauds, faults, SLA violations). In many real settings, such cases are labelled with a numerical deviance measure, and the analyst wants to...
Increasing attention has been paid to the detection and analysis of “deviant” instances of a business process that are connected with some kind of “hidden” undesired behavior (e.g. frauds and faults). In particular, several recent works faced the problem of inducing a binary classification model (here named deviance detection model) that can discri...
This paper presents a framework for analyzing and predicting the performances of a business process, based on historical data gathered during its past enactments. The framework hinges on an inductive-learning technique for
discovering a special kind of predictive process models, which can support the run-time prediction of a given performance measu...
Increasing attention has been paid of late to the problem of detecting and explaining “deviant” process instances, i.e. instances diverging from normal/desired outcomes (e.g., frauds, faults, SLA violations), based on log data. Current solutions allow to discriminate between deviant and normal instances, by combining the extraction of (sequence-bas...
Predicting the fix time (i.e. the time needed to eventually solve a case) is a key task in an issue tracking system, which attracted the attention of data-mining researchers in recent years. Traditional approaches only try to forecast the overall fix time of a case when it is reported, without updating this preliminary estimate as long as the case...
Process discovery techniques are a precious tool for analyzing the real behavior of a business process. However, their direct application to lowly structured logs may yield unreadable and inaccurate models. Current solutions rely on event abstraction or trace clustering, and assume that log events refer to well-defined (possibly low-level) process...
Process discovery (i.e. the automated induction of a behavioral process model from execution logs) is an important tool for business process analysts/managers, who can exploit the extracted knowledge in key process improvement and (re-)design tasks. Unfortunately, when directly applied to the logs of complex and/or lowly-structured processes, such...
This paper presents a framework for analyzing and predicting the performances of a business process, based on historical data gathered during its past enactments. The framework hinges on an inductive-learning technique for discovering a special kind of predictive process models, which can support the run-time prediction of a given performance measu...
Process Mining techniques have been gaining attention, especially as concerns the discovery of predictive process models. Traditionally focused on workflows, they usually assume that process tasks are clearly specified, and referred to in the logs. This limits however their application to many real-life BPM environments (e.g. issue tracking systems...
Fix-time prediction is a key task in bug tracking systems, which was recently faced through predictive data mining approaches, trying to estimate the time needed to solve a case, at the very moment when it is reported. And yet, the actions performed on a bug, along its life, can help refine the prediction of its (remaining) fix-time, by leveraging...
Predicting run-time performances is a hot issue in ticket resolution processes. Recent efforts to take account for the sequence of resolution steps, suggest that predictive Process Mining (PM) techniques could be applied in this field, if suitably adapted to the peculiarities of ticket systems. In particular, the performances of a ticket instance u...
This paper presents a novel approach to the discovery of predictive process models, which are meant to support the run-time prediction of some performance indicator (e.g., the remaining processing time) on new ongoing processinstances. To this purpose,we combine a series of data mining techniques(ranging from pattern mining,to non-parametric regres...
Process Mining techniques have been gaining attention, owing to their potentiality to extract compact process models from massive logs. Traditionally focused on workflows, they often assume that process tasks are clearly specified, and referred to in the logs. This limits how- ever their application to many real-life BPM environments (e.g. issue tr...
The discovery of predictive models for process performances is an emerging topic, which poses a series of difficulties when considering complex and flexible processes, whose behaviour tend to change over time depending on context factors. We try to face such a situation by proposing a predictive-clustering approach, where different context-related...
Discovering predictive models for run-time support is an emerging topic in Process Mining research, which can effectively help optimize business process enactments. However, making accurate estimates is not easy especially when considering fine-grain performance measures (e.g., processing times) on a complex and flexible business process, where per...
Process mining is an established approach for analyzing and model- ing complex business processes. In this paper we showcase ProMetheuS, a flex- ible and scalable suite for process mining natively designed for industrial appli- cations. Moving from the experience of the ProM framework, the state-of-art process mining tool, ProMetheuS introduces thr...
This paper presents a probabilistic co-clustering approach to pattern discovery in preference data. We extended the original formulation of the block mixture model to handle rating data, the resulting model allows the simultaneous clustering of users and items in homogeneous user communities and item categories. The parameter of the model are deter...
A new technique, SNIPER, is proposed for learning a model that deals with continuous values of exceptionality. Specifically, given some training objects associated with a continuous attribute F, SNIPER induces a rule-based model for the identification of those objects likely to score the maximum values for F. The purpose of SNIPER differs from the...
In this paper we describe an experience resulting from the collaboration among data mining researchers, domain experts of the Italian revenue agency, and IT professionals, aimed at detecting fraudulent VAT credit claims. The outcome is an auditing methodology based on a rule-based system, which is capable of trading among conflicting issues, such a...
A hierarchical classification framework is proposed for discriminating rare classes in imprecise domains, characterized by
rarity (of both classes and cases), noise and low class separability. The devised framework couples the rules of a rule-based
classifier with as many local probabilistic generative models. These are trained over the coverage of...
A new hierarchical framework is preliminarily proposed for accurate classification in imprecise multi-class domains inherently characterized by rarity and noise. The key idea behind the devised framework is coupling the individual rules of an associative classifier with as many local probabilistic generative models. These are trained over the cover...
Projects
Projects (5)
Funded by Italian Ministry of economic development, the project aims at developing methodologies, techniques, architectures, software Solutions based on advanced AI technologies, Machine and Deep Learning, which then allow the creation of an Innovative Platform capable of managing large data flows from multiple contexts at high frequency of production and variation in order to make analyzes and forecasts useful for new forms of Customer Engagement and Customer Satisfaction.
Funded by Italian Ministry of economic development, the project aims at developing methodologies, techniques, architectures, software Solutions based on advanced AI technologies, Machine and Deep Learning, which then allow the creation of an Innovative Platform capable of managing large data flows from multiple contexts at high frequency of production and variation in order to make analyzes and forecasts useful for new forms of Customer Engagement and Customer Satisfaction.
The project intends to study new methods, techniques and software solutions able to play an enabling role for Blockchain technologies in privacy, sharing economy and digital rights application contexts.
