Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516608
Ionut Ficiu, Radu Stilpeanu, Cosmin Toca, A. Petre, C. Patrascu, M. Ciuc
In recent years, a wide variety of automatic, semiautomatic and manual approaches to image annotation have been proposed. These prerequisites have been driven by continuous advances of deep learning algorithms that often encounter the problem of insufficient or inappropriate training data, as well as sub-par markings’ accuracy which can have a direct impact on the model’s performance regardless. The main contribution of this paper is the development of a complex annotation framework able to automatically generate high-quality markings. The annotation work-flow aims to be an iterative process allowing automatic labeling of object bounding boxes, while simultaneously predicting the polygon outlining the object instance inside the box. The markings’ format is fully compatible with COCO Detection & Panoptic APIs that provide open-source interfaces for loading, parsing, and visualizing annotations. Following the completion of the research project funding this research, the code will be publicly available.
{"title":"Automatic Annotation of Object Instances by Region-Based Recurrent Neural Networks","authors":"Ionut Ficiu, Radu Stilpeanu, Cosmin Toca, A. Petre, C. Patrascu, M. Ciuc","doi":"10.1109/ICCP.2018.8516608","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516608","url":null,"abstract":"In recent years, a wide variety of automatic, semiautomatic and manual approaches to image annotation have been proposed. These prerequisites have been driven by continuous advances of deep learning algorithms that often encounter the problem of insufficient or inappropriate training data, as well as sub-par markings’ accuracy which can have a direct impact on the model’s performance regardless. The main contribution of this paper is the development of a complex annotation framework able to automatically generate high-quality markings. The annotation work-flow aims to be an iterative process allowing automatic labeling of object bounding boxes, while simultaneously predicting the polygon outlining the object instance inside the box. The markings’ format is fully compatible with COCO Detection & Panoptic APIs that provide open-source interfaces for loading, parsing, and visualizing annotations. Following the completion of the research project funding this research, the code will be publicly available.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116708564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516592
Maria-Iuliana Bocicor, A. Molnar, Iuliana Marin, N. Goga, Raul Valor Perez, D. Cuesta-Frau
The current trend of population ageing leads to an increasingly larger population of older adults, who understandabl desire to continue living an independent and fulfilling life in their home and within their communities. While traditionally seen as a societal issue, we are currently at a point where advancement in science and technology enables us to augment human help with ambient assisted living solutions. This paper is the result of research and development undertaken within the framework of a European Union research project targeting the development of a cyber-physical system for assisted living and home monitoring. The system integrates an unobtrusive networkof wireless sensors with server software to provide ambient monitoring, location detection and real-time alerting. The present paper is focused on the system’s intelligent software components.The first is a business rules engine that can be configured to send real-time alerts in the case of certain ambient conditions or whe the location of the monitored person shows signification alteration from their usual movement patterns. The second component is a artificial intelligence-based location predictor, used to provide the monitored person’s location. Discrepancies between actual an expected location are used by the rule engine to trigger real-time alerts to caregivers.
{"title":"Intelligent Decision Support for Pervasive Home Monitoring and Assisted Living","authors":"Maria-Iuliana Bocicor, A. Molnar, Iuliana Marin, N. Goga, Raul Valor Perez, D. Cuesta-Frau","doi":"10.1109/ICCP.2018.8516592","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516592","url":null,"abstract":"The current trend of population ageing leads to an increasingly larger population of older adults, who understandabl desire to continue living an independent and fulfilling life in their home and within their communities. While traditionally seen as a societal issue, we are currently at a point where advancement in science and technology enables us to augment human help with ambient assisted living solutions. This paper is the result of research and development undertaken within the framework of a European Union research project targeting the development of a cyber-physical system for assisted living and home monitoring. The system integrates an unobtrusive networkof wireless sensors with server software to provide ambient monitoring, location detection and real-time alerting. The present paper is focused on the system’s intelligent software components.The first is a business rules engine that can be configured to send real-time alerts in the case of certain ambient conditions or whe the location of the monitored person shows signification alteration from their usual movement patterns. The second component is a artificial intelligence-based location predictor, used to provide the monitored person’s location. Discrepancies between actual an expected location are used by the rule engine to trigger real-time alerts to caregivers.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516595
Dorin Moldovan, Adrian Olosutean, V. Chifu, C. Pop, T. Cioara, I. Anghel, I. Salomie
Dementia is still an incurable disease that affects a big part of the population nowadays. It affects millions of people worldwide and the number is expected to increase significantly in the next decades. The persons with dementia face difficulties in performing the daily living activities (DLAs) due to movement disorders, poor coordination and memory loss and they need support from family members or health care professionals. In this paper several Big Data techniques are explored for the analysis of the DLAs of the people that have dementia in order to identify behavioral patterns. In particular this paper: (1) presents how the K-Means Clustering algorithm can be used for the identification of the number of types of DLAs performed by a person with dementia in a day, (2) presents how to apply the Collaborative Filtering algorithm for the prediction of the frequency of the DLAs and (3) compares several classification and regression algorithms for the identification of the days with anomalies with respect to a baseline and for the prediction of the durations of the DLAs of the people with dementia using a prototype developed in-house. Two datasets used in the experiments are taken from literature and a third dataset is derived from one of the previous datasets and used as simulated data.
{"title":"Big Data Analytics for the Daily Living Activities of the People with Dementia","authors":"Dorin Moldovan, Adrian Olosutean, V. Chifu, C. Pop, T. Cioara, I. Anghel, I. Salomie","doi":"10.1109/ICCP.2018.8516595","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516595","url":null,"abstract":"Dementia is still an incurable disease that affects a big part of the population nowadays. It affects millions of people worldwide and the number is expected to increase significantly in the next decades. The persons with dementia face difficulties in performing the daily living activities (DLAs) due to movement disorders, poor coordination and memory loss and they need support from family members or health care professionals. In this paper several Big Data techniques are explored for the analysis of the DLAs of the people that have dementia in order to identify behavioral patterns. In particular this paper: (1) presents how the K-Means Clustering algorithm can be used for the identification of the number of types of DLAs performed by a person with dementia in a day, (2) presents how to apply the Collaborative Filtering algorithm for the prediction of the frequency of the DLAs and (3) compares several classification and regression algorithms for the identification of the days with anomalies with respect to a baseline and for the prediction of the durations of the DLAs of the people with dementia using a prototype developed in-house. Two datasets used in the experiments are taken from literature and a third dataset is derived from one of the previous datasets and used as simulated data.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121602023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516607
Liviu Nastase, Catalin Negru, Florin Pop
Maritime transport represents the primary transportation for the global economy, almost 90% of the goods worldwide are shipped by sea, including petrol, food, cars, electronic components and other raw materials. On the other hand, maritime transport is responsible for 3% to 4% of the total human-caused carbon emissions. The current marine infrastructure has systems in place to track and monitor ships during their voyages. One of those systems is the automatic identification system (AIS). This paper aims to create a system that use AIS data to offer an improved understanding and additional insights on maritime transport. Using this naval system traffic is better understood, and with time its efficiency would be improved. The proposed system presents ship’s details, destinations and locations data based on AIS data. We can use this system to create further functionalities for better and detailed analysis on maritime transport.
{"title":"Understanding and Making Sense of Maritime Navigation Datasets","authors":"Liviu Nastase, Catalin Negru, Florin Pop","doi":"10.1109/ICCP.2018.8516607","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516607","url":null,"abstract":"Maritime transport represents the primary transportation for the global economy, almost 90% of the goods worldwide are shipped by sea, including petrol, food, cars, electronic components and other raw materials. On the other hand, maritime transport is responsible for 3% to 4% of the total human-caused carbon emissions. The current marine infrastructure has systems in place to track and monitor ships during their voyages. One of those systems is the automatic identification system (AIS). This paper aims to create a system that use AIS data to offer an improved understanding and additional insights on maritime transport. Using this naval system traffic is better understood, and with time its efficiency would be improved. The proposed system presents ship’s details, destinations and locations data based on AIS data. We can use this system to create further functionalities for better and detailed analysis on maritime transport.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130129007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516640
Bianca-Cerasela-Zelia Blaga, S. Nedevschi
Pole-like structures such as the ones used for traffic lights, traffic signs, utility poles, lampposts or even trees are encountered everywhere in urban scenarios. Because they are robust landmarks, they can help solve problems from the autonomous driving domain, such as localization, mapping, and navigation. In this paper, we propose a method that extracts poles from stereo camera information. First, the intensity images are analyzed to find areas of interest that could contain the desired landmarks. Then, we build U- and V-disparity maps that are used to estimate the position of the poles on the road images. Finally, we cluster the candidate regions of interest, which are then further refined to eliminate outliers. We also use an algorithm for enhancing the illumination of nighttime images, so that we can detect the desired landmarks at different times of the day. Our system is able to extract poles from the same road, on different driving conditions, days, or lanes, it accounts for the possibility of occlusions, and we are able to obtain both a relative and an absolute localization.
{"title":"A Method for Automatic Pole Detection from Urban Video Scenes using Stereo Vision","authors":"Bianca-Cerasela-Zelia Blaga, S. Nedevschi","doi":"10.1109/ICCP.2018.8516640","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516640","url":null,"abstract":"Pole-like structures such as the ones used for traffic lights, traffic signs, utility poles, lampposts or even trees are encountered everywhere in urban scenarios. Because they are robust landmarks, they can help solve problems from the autonomous driving domain, such as localization, mapping, and navigation. In this paper, we propose a method that extracts poles from stereo camera information. First, the intensity images are analyzed to find areas of interest that could contain the desired landmarks. Then, we build U- and V-disparity maps that are used to estimate the position of the poles on the road images. Finally, we cluster the candidate regions of interest, which are then further refined to eliminate outliers. We also use an algorithm for enhancing the illumination of nighttime images, so that we can detect the desired landmarks at different times of the day. Our system is able to extract poles from the same road, on different driving conditions, days, or lanes, it accounts for the possibility of occlusions, and we are able to obtain both a relative and an absolute localization.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129374309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516632
Nichita Utiu, Vlad-Sebastian Ionescu
Content extraction is the process that aims to separate the main content of web pages from the bulk of template and decorative components. We present a method of doing this which achieves competitive performance on the Cleaneval dataset and sets a new state-of-the-art with an F1 score of 0.96 on the Dragnet dataset. We accomplish this by modeling the task as a classification problem over HTML tags using features based on information from the DOM tree. Not only do we obtain a performance increase over current methods, but we do so with minimal feature engineering and without the extensive preprocessing steps of other methods.
{"title":"Learning Web Content Extraction with DOM Features","authors":"Nichita Utiu, Vlad-Sebastian Ionescu","doi":"10.1109/ICCP.2018.8516632","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516632","url":null,"abstract":"Content extraction is the process that aims to separate the main content of web pages from the bulk of template and decorative components. We present a method of doing this which achieves competitive performance on the Cleaneval dataset and sets a new state-of-the-art with an F1 score of 0.96 on the Dragnet dataset. We accomplish this by modeling the task as a classification problem over HTML tags using features based on information from the DOM tree. Not only do we obtain a performance increase over current methods, but we do so with minimal feature engineering and without the extensive preprocessing steps of other methods.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127689903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516583
Nicusor Iancu-Puiu, M. Marcu
Bluetooth technology is widely used nowadays by mobile, embedded and IoT devices. One important aspect that should be addressed by battery powered solutions is energy efficiency, where communication takes an important share or the total power consumption. The main goal of our research is to analyze and profile Bluetooth communication from power consumption and performance perspectives. This analysis is further used to optimize communication on battery powered devices in order to increase the battery lifetime of the application. In our work we managed to identify and measure power consumption and timings of BLE communication at event level with high accuracy.
{"title":"Highly Accurate Power Profiling of Bluetooth Low Energy Communication","authors":"Nicusor Iancu-Puiu, M. Marcu","doi":"10.1109/ICCP.2018.8516583","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516583","url":null,"abstract":"Bluetooth technology is widely used nowadays by mobile, embedded and IoT devices. One important aspect that should be addressed by battery powered solutions is energy efficiency, where communication takes an important share or the total power consumption. The main goal of our research is to analyze and profile Bluetooth communication from power consumption and performance perspectives. This analysis is further used to optimize communication on battery powered devices in order to increase the battery lifetime of the application. In our work we managed to identify and measure power consumption and timings of BLE communication at event level with high accuracy.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124065725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516646
Mircea Paul Muresan, S. Nedevschi
one of the key components of the perception system in an autonomous vehicle or ADAS is the target tracking module. Using target tracking in the sea of clutter, self-driving cars are able to better understand the environment and make predictions about the surrounding objects. Cuboids obtained from a sparse LIDAR often exhibit a fluctuating behavior due to segmentation problems and errors accumulated from the motion correction module. Furthermore, targets in real life scenarios do not move in a predictable manner, so it is very difficult for a classical motion model to describe the complex behavior of any road objects in such cases. In this paper we propose a two-step data association scheme that efficiently and effectively finds correspondences between tracks and measurements. Then we aim to generate better position estimates for objects with an ambiguous dynamic behavior by associating and combining the results from two different motion models. The proposed solution runs in real time and it was validated using a high precision GPS, and also by projecting the prediction results in the corresponding intensity image and assessing whether the prediction falls on the correct item.
{"title":"Multimodal sparse LIDAR object tracking in clutter","authors":"Mircea Paul Muresan, S. Nedevschi","doi":"10.1109/ICCP.2018.8516646","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516646","url":null,"abstract":"one of the key components of the perception system in an autonomous vehicle or ADAS is the target tracking module. Using target tracking in the sea of clutter, self-driving cars are able to better understand the environment and make predictions about the surrounding objects. Cuboids obtained from a sparse LIDAR often exhibit a fluctuating behavior due to segmentation problems and errors accumulated from the motion correction module. Furthermore, targets in real life scenarios do not move in a predictable manner, so it is very difficult for a classical motion model to describe the complex behavior of any road objects in such cases. In this paper we propose a two-step data association scheme that efficiently and effectively finds correspondences between tracks and measurements. Then we aim to generate better position estimates for objects with an ambiguous dynamic behavior by associating and combining the results from two different motion models. The proposed solution runs in real time and it was validated using a high precision GPS, and also by projecting the prediction results in the corresponding intensity image and assessing whether the prediction falls on the correct item.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"56 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516609
Ioan Stan, Dan Toderici, R. Potolea
Nowadays traffic congestion in cities is one of the main challenges that drivers are facing. Cities administration doesn’t always manage to handle this challenge with success and the increasing number of cars makes the situation worse. Navigation Systems, besides generating routes between a starting and ending point, can be used to predict and avoid traffic congestion. In this paper we propose a novel solution for traffic information representation in connected cars context. The strategy is based on segment trees data structure and was integrated into an industry navigation system by enhancing routing algorithm to support traffic congestion prediction and avoidance. The experimental results proves that connected cars information can be used to predict and optimize traffic flow in a city.
{"title":"Segment Trees based Traffic Congestion Avoidance in Connected Cars Context","authors":"Ioan Stan, Dan Toderici, R. Potolea","doi":"10.1109/ICCP.2018.8516609","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516609","url":null,"abstract":"Nowadays traffic congestion in cities is one of the main challenges that drivers are facing. Cities administration doesn’t always manage to handle this challenge with success and the increasing number of cars makes the situation worse. Navigation Systems, besides generating routes between a starting and ending point, can be used to predict and avoid traffic congestion. In this paper we propose a novel solution for traffic information representation in connected cars context. The strategy is based on segment trees data structure and was integrated into an industry navigation system by enhancing routing algorithm to support traffic congestion prediction and avoidance. The experimental results proves that connected cars information can be used to predict and optimize traffic flow in a city.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131476400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ICCP.2018.8516636
F. Moldovan, Ciprian Oprișa
The ability to analyze network threats is very important in security research. Traditional approaches, involving sandboxing technology are limited to simulating a single host, missing local network attacks. This issue is addressed by designing a threat analysis framework that uses software-defined networking for simulating arbitrary networks. The presented system offers flexibility, allowing a security researcher to define a virtual network that is able to capture malicious actions and to be restored to the initial state afterwards. Both the framework design and common usage scenarios are described. By providing this framework, we aim to ease the analysis effort in combating cyberthreats.
{"title":"A Framework for Threats Analysis Using Software-Defined Networking","authors":"F. Moldovan, Ciprian Oprișa","doi":"10.1109/ICCP.2018.8516636","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516636","url":null,"abstract":"The ability to analyze network threats is very important in security research. Traditional approaches, involving sandboxing technology are limited to simulating a single host, missing local network attacks. This issue is addressed by designing a threat analysis framework that uses software-defined networking for simulating arbitrary networks. The presented system offers flexibility, allowing a security researcher to define a virtual network that is able to capture malicious actions and to be restored to the initial state afterwards. Both the framework design and common usage scenarios are described. By providing this framework, we aim to ease the analysis effort in combating cyberthreats.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128867622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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