The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Giuseppe Santucci - One of the best experts on this subject based on the ideXlab platform.
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Toward Multidimensional Geographical Performance Analysis for Telecommunications Network
2019 23rd International Conference Information Visualisation (IV), 2019Co-Authors: Marco Angelini, Giorgio Cazzetta, Marina Geymonat, Mario Mirabelli, Giuseppe SantucciAbstract:Management and maintenance of mobile networks is a challenging activity: key point indicators related to different dimensions need to be evaluated in order to make business decisions and/or to manage problems arising from the network and the relative impact on business. This paper presents a visual Analytics Solution able to geo-reference up to three key point indicators, aiming at supporting operators in charge of analyzing the mobile network, detecting possible mobile network failures and managing resulting business oriented decisions. The proposed system has been developed collaboratively by University of Rome “La Sapienza” and TIM.
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mad a visual Analytics Solution for multi step cyber attacks detection
Journal of Computer Languages, 2019Co-Authors: Marco Angelini, Simone Lenti, Giuseppe Santucci, Silvia Bonomi, S TaggiAbstract:Abstract Software vulnerabilities represent one of the main weaknesses of an Information Technology (IT) system w.r.t. cyber attacks and nowadays consolidated official data, like the Common Vulnerability Exposure (CVE) dictionary, provide precise and reliable details about them. This information, together with the identification of priority systems to defend allows for inspecting the network structure and the most probable paths an attacker is likely to follow to reach sensible resources, with the main goal of identify suitable mitigation actions that reduce the risk of an attack. Some of these mitigation actions can be applied without further delay, some of them, instead, imply a high operational impact on the organization business that makes their usage convenient only when an attack is really on the way. Dealing with this issue is particularly challenging in the context of critical infrastructure where, even if patches are available, organization mission constraints create obstacles to their straightforward application. In this scenario, security operators are forced to deal with known vulnerabilities that cannot be patched and they spend a noticeable effort in proactive analysis, devising countermeasures that can mitigate the effect of a possible attack. This paper presents a Multi-step cyber Attack Detection (MAD) Visual Analytics Solution aiming at assisting security operators in improving their network security by analyzing the possible attacks and identifying suitable mitigations. Moreover, during an attack, the system visually presents the security operator with the relevant pieces of information allowing a better comprehension of the attack status and its probable evolution, in order to make decisions on the possible countermeasures.
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Vulnus: Visual Vulnerability Analysis for Network Security
IEEE Transactions on Visualization and Computer Graphics, 2019Co-Authors: Marco Angelini, Simone Lenti, Graziano Blasilli, Tiziana Catarci, Giuseppe SantucciAbstract:Vulnerabilities represent one of the main weaknesses of IT systems and the availability of consolidated official data, like CVE (Common Vulnerabilities and Exposures), allows for using them to compute the paths an attacker is likely to follow. However, even if patches are available, business constraints or lack of resources create obstacles to their straightforward application. As a consequence, the security manager of a network needs to deal with a large number of vulnerabilities, making decisions on how to cope with them. This paper presents VULNUS (VULNerabilities visUal aSsessment), a visual Analytics Solution for dynamically inspecting the vulnerabilities spread on networks, allowing for a quick understanding of the network status and visually classifying nodes according to their vulnerabilities. Moreover, VULNUS computes the approximated optimal sequence of patches able to eliminate all the attack paths and allows for exploring sub-optimal patching strategies, simulating the effect of removing one or more vulnerabilities. VULNUS has been evaluated by domain experts using a lab-test experiment, investigating the effectiveness and efficiency of the proposed Solution.
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Cyber situational awareness: from geographical alerts to high-level management
Journal of Visualization, 2017Co-Authors: Marco Angelini, Giuseppe SantucciAbstract:This paper focuses on cyber situational awareness and describes a visual Analytics Solution for monitoring and putting in tight relation data from network level with the organization business. The goal of the proposed Solution is to make different security profiles (network security officer, network security manager, and financial security manager) aware of the actual network state (e.g., risk and attack progress) and the impact it actually has on the business tasks, making clear the relationships that exist between the network level and the business level. The proposed Solution is instantiated on the ACEA infrastructure, the Italian company that provides power and water purification services to cities in central Italy (millions of end users). Graphical Abstract
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CRUMBS: A cyber security framework browser
2017 IEEE Symposium on Visualization for Cyber Security (VizSec), 2017Co-Authors: Marco Angelini, Simone Lenti, Giuseppe SantucciAbstract:In the last years, several standards and frameworks have been developed to help organizations to increase the security of their Information Technology (IT) systems. In order to deal with the continuous evolution of the cyberattacks complexity, such Solutions have to cope with an overwhelming set of concepts, and are perceived as complex and hard to implement. This paper presents a visual Analytics Solution targeted at dealing with the Italian Adaptation of the Cyber Security Framework (IACSF), derived by the National Institute of Standards and Technology (NIST) proposal, adaptation that, in its full complexity, presents the security managers with hundreds of scattered concepts, like functions, categories, subcategories, priorities, maturity levels, current and target profiles, and controls, making its adoption a complex activity. The system has been designed together with the security experts of one of the largest Italian public organization and has the goal of providing a continuous overview of the adoption process, providing a prioritizing view that helps in effectively planning the required activities. A prototype is available at: http://awareserver.dis.uniroma1.it:11768/crumbs/.
Xavier Sevillano - One of the best experts on this subject based on the ideXlab platform.
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quickspot a video Analytics Solution for on street vacant parking spot detection
Multimedia Tools and Applications, 2016Co-Authors: E. Marmol, Xavier SevillanoAbstract:Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of available vacant parking spots. A scalable Solution would consist in exploiting the existing and widely-deployed video surveillance camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven video Analytics Solution for on-street vacant parking spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting conditions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the computational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison the other car-driven approaches reported in the literature.
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QuickSpot: a video Analytics Solution for on-street vacant parking spot detection
Multimedia Tools and Applications, 2016Co-Authors: E. Marmol, Xavier SevillanoAbstract:Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environ- ments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of avail- able vacant parking spots. A scalable Solution would consist in exploiting the existing and widely-deployed video surveillance camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven video Analytics Solution for on-street vacant park- ing spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting condi- tions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the com- putational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison
Marco Angelini - One of the best experts on this subject based on the ideXlab platform.
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Toward Multidimensional Geographical Performance Analysis for Telecommunications Network
2019 23rd International Conference Information Visualisation (IV), 2019Co-Authors: Marco Angelini, Giorgio Cazzetta, Marina Geymonat, Mario Mirabelli, Giuseppe SantucciAbstract:Management and maintenance of mobile networks is a challenging activity: key point indicators related to different dimensions need to be evaluated in order to make business decisions and/or to manage problems arising from the network and the relative impact on business. This paper presents a visual Analytics Solution able to geo-reference up to three key point indicators, aiming at supporting operators in charge of analyzing the mobile network, detecting possible mobile network failures and managing resulting business oriented decisions. The proposed system has been developed collaboratively by University of Rome “La Sapienza” and TIM.
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mad a visual Analytics Solution for multi step cyber attacks detection
Journal of Computer Languages, 2019Co-Authors: Marco Angelini, Simone Lenti, Giuseppe Santucci, Silvia Bonomi, S TaggiAbstract:Abstract Software vulnerabilities represent one of the main weaknesses of an Information Technology (IT) system w.r.t. cyber attacks and nowadays consolidated official data, like the Common Vulnerability Exposure (CVE) dictionary, provide precise and reliable details about them. This information, together with the identification of priority systems to defend allows for inspecting the network structure and the most probable paths an attacker is likely to follow to reach sensible resources, with the main goal of identify suitable mitigation actions that reduce the risk of an attack. Some of these mitigation actions can be applied without further delay, some of them, instead, imply a high operational impact on the organization business that makes their usage convenient only when an attack is really on the way. Dealing with this issue is particularly challenging in the context of critical infrastructure where, even if patches are available, organization mission constraints create obstacles to their straightforward application. In this scenario, security operators are forced to deal with known vulnerabilities that cannot be patched and they spend a noticeable effort in proactive analysis, devising countermeasures that can mitigate the effect of a possible attack. This paper presents a Multi-step cyber Attack Detection (MAD) Visual Analytics Solution aiming at assisting security operators in improving their network security by analyzing the possible attacks and identifying suitable mitigations. Moreover, during an attack, the system visually presents the security operator with the relevant pieces of information allowing a better comprehension of the attack status and its probable evolution, in order to make decisions on the possible countermeasures.
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Vulnus: Visual Vulnerability Analysis for Network Security
IEEE Transactions on Visualization and Computer Graphics, 2019Co-Authors: Marco Angelini, Simone Lenti, Graziano Blasilli, Tiziana Catarci, Giuseppe SantucciAbstract:Vulnerabilities represent one of the main weaknesses of IT systems and the availability of consolidated official data, like CVE (Common Vulnerabilities and Exposures), allows for using them to compute the paths an attacker is likely to follow. However, even if patches are available, business constraints or lack of resources create obstacles to their straightforward application. As a consequence, the security manager of a network needs to deal with a large number of vulnerabilities, making decisions on how to cope with them. This paper presents VULNUS (VULNerabilities visUal aSsessment), a visual Analytics Solution for dynamically inspecting the vulnerabilities spread on networks, allowing for a quick understanding of the network status and visually classifying nodes according to their vulnerabilities. Moreover, VULNUS computes the approximated optimal sequence of patches able to eliminate all the attack paths and allows for exploring sub-optimal patching strategies, simulating the effect of removing one or more vulnerabilities. VULNUS has been evaluated by domain experts using a lab-test experiment, investigating the effectiveness and efficiency of the proposed Solution.
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Cyber situational awareness: from geographical alerts to high-level management
Journal of Visualization, 2017Co-Authors: Marco Angelini, Giuseppe SantucciAbstract:This paper focuses on cyber situational awareness and describes a visual Analytics Solution for monitoring and putting in tight relation data from network level with the organization business. The goal of the proposed Solution is to make different security profiles (network security officer, network security manager, and financial security manager) aware of the actual network state (e.g., risk and attack progress) and the impact it actually has on the business tasks, making clear the relationships that exist between the network level and the business level. The proposed Solution is instantiated on the ACEA infrastructure, the Italian company that provides power and water purification services to cities in central Italy (millions of end users). Graphical Abstract
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CRUMBS: A cyber security framework browser
2017 IEEE Symposium on Visualization for Cyber Security (VizSec), 2017Co-Authors: Marco Angelini, Simone Lenti, Giuseppe SantucciAbstract:In the last years, several standards and frameworks have been developed to help organizations to increase the security of their Information Technology (IT) systems. In order to deal with the continuous evolution of the cyberattacks complexity, such Solutions have to cope with an overwhelming set of concepts, and are perceived as complex and hard to implement. This paper presents a visual Analytics Solution targeted at dealing with the Italian Adaptation of the Cyber Security Framework (IACSF), derived by the National Institute of Standards and Technology (NIST) proposal, adaptation that, in its full complexity, presents the security managers with hundreds of scattered concepts, like functions, categories, subcategories, priorities, maturity levels, current and target profiles, and controls, making its adoption a complex activity. The system has been designed together with the security experts of one of the largest Italian public organization and has the goal of providing a continuous overview of the adoption process, providing a prioritizing view that helps in effectively planning the required activities. A prototype is available at: http://awareserver.dis.uniroma1.it:11768/crumbs/.
E. Marmol - One of the best experts on this subject based on the ideXlab platform.
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quickspot a video Analytics Solution for on street vacant parking spot detection
Multimedia Tools and Applications, 2016Co-Authors: E. Marmol, Xavier SevillanoAbstract:Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of available vacant parking spots. A scalable Solution would consist in exploiting the existing and widely-deployed video surveillance camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven video Analytics Solution for on-street vacant parking spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting conditions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the computational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison the other car-driven approaches reported in the literature.
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QuickSpot: a video Analytics Solution for on-street vacant parking spot detection
Multimedia Tools and Applications, 2016Co-Authors: E. Marmol, Xavier SevillanoAbstract:Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environ- ments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of avail- able vacant parking spots. A scalable Solution would consist in exploiting the existing and widely-deployed video surveillance camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven video Analytics Solution for on-street vacant park- ing spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting condi- tions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the com- putational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison
Enrico Bertini - One of the best experts on this subject based on the ideXlab platform.
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Lessons Learned Developing a Visual Analytics Solution for Investigative Analysis of Scamming Activities
IEEE Transactions on Visualization and Computer Graphics, 2019Co-Authors: Jay Koven, Cristian Felix, Hossein Siadati, Markus Jakobsson, Enrico BertiniAbstract:The forensic investigation of communication datasets which contain unstructured text, social network information, and metadata is a complex task that is becoming more important due to the immense amount of data being collected. Currently there are limited approaches that allow an investigator to explore the network, text and metadata in a unified manner. We developed Beagle as a forensic tool for email datasets that allows investigators to flexibly form complex queries in order to discover important information in email data. Beagle was successfully deployed at a security firm which had a large email dataset that was difficult to properly investigate. We discuss our experience developing Beagle as well as the lessons we learned applying visual analytic techniques to a difficult real-world problem.
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Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Jorge Poco, Enrico Bertini, Aritra Dasgupta, William Hargrove, Christopher R. Schwalm, Deborah N. Huntzinger, Robert Cook, Claudio T. SilvaAbstract:Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual Analytics Solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science.
