Pub Date : 2017-12-01DOI: 10.1109/ICMLA.2017.00-65
Ayantha Randika, M. Wickramasinghe
Typeface spacing is a hard problem. It takes countless hours of manual labour to achieve an aesthetically pleasing font one frequently encounters in digital media. Inter-letter spacing defines the texture and the feel of a typeface and when done accurately yields an aesthetically balanced and an appealing typeface. Nevertheless, setting spacing in a typeface is a tedious and a time consuming task. Hence this paper presents an exploratory study investigating the potential of Neural Networks (NN) to fully automate the typeface spacing process. Even though the NN models investigated in this study yielded up to an accuracy of 47% when compared with typefaces spaced by the type designers, the visual differences were subtle. Thus, we conclude that neural models can indeed be used to model the typeface spacing problem. As one of the first attempts to apply neural models in this particular problem domain, this study lays the foundation to future research and studies.
{"title":"A Neural Network Approach to Derive the Horizontal Spaces in Typefaces","authors":"Ayantha Randika, M. Wickramasinghe","doi":"10.1109/ICMLA.2017.00-65","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-65","url":null,"abstract":"Typeface spacing is a hard problem. It takes countless hours of manual labour to achieve an aesthetically pleasing font one frequently encounters in digital media. Inter-letter spacing defines the texture and the feel of a typeface and when done accurately yields an aesthetically balanced and an appealing typeface. Nevertheless, setting spacing in a typeface is a tedious and a time consuming task. Hence this paper presents an exploratory study investigating the potential of Neural Networks (NN) to fully automate the typeface spacing process. Even though the NN models investigated in this study yielded up to an accuracy of 47% when compared with typefaces spaced by the type designers, the visual differences were subtle. Thus, we conclude that neural models can indeed be used to model the typeface spacing problem. As one of the first attempts to apply neural models in this particular problem domain, this study lays the foundation to future research and studies.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"16 1","pages":"769-773"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81505453","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.00079
Javier Rubio-Herrero, V. Chandan, C. Siegel, Abhinav Vishnu, D. Vrabie
Buildings consume almost 40% of energy in the US. In order to optimize the operation of buildings, models that describe the relationship between energy consumption and control knobs such as set-points with high predictive capability are required. Data driven modeling techniques have been investigated to a somewhat limited extent for optimizing the operation and control of buildings. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. This paper investigates the use of deep learning for modeling the power consumption of building heating, ventilation and air-conditioning (HVAC) systems. A preliminary analysis of the performance of the methodology for different architectures is conducted. Results show that the proposed methodology outperforms other data driven modeling techniques significantly.
{"title":"A Learning Framework for Control-Oriented Modeling of Buildings","authors":"Javier Rubio-Herrero, V. Chandan, C. Siegel, Abhinav Vishnu, D. Vrabie","doi":"10.1109/ICMLA.2017.00079","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00079","url":null,"abstract":"Buildings consume almost 40% of energy in the US. In order to optimize the operation of buildings, models that describe the relationship between energy consumption and control knobs such as set-points with high predictive capability are required. Data driven modeling techniques have been investigated to a somewhat limited extent for optimizing the operation and control of buildings. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. This paper investigates the use of deep learning for modeling the power consumption of building heating, ventilation and air-conditioning (HVAC) systems. A preliminary analysis of the performance of the methodology for different architectures is conducted. Results show that the proposed methodology outperforms other data driven modeling techniques significantly.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"32 1","pages":"473-478"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88453733","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.0-107
R. T. Pereira, Javier Caicedo Zambrano
The results of the research project that aims to identify patterns of student dropout from socioeconomic, academic, disciplinary and institutional data of students from undergraduate programs at the University of Nariño from Pasto city (Colombia), using data mining techniques are presented. Built a data repository with the records of students who were admitted in the period from the first half of 2004 and the second semester of 2006. Three complete cohorts were analyzed with an observation period of six years until 2011. Socioeconomic and academic student dropout profiles were discovered using classification technique based on decision trees. The knowledge generated will support effective decision-making of university staff focused to develop policies and strategies related to student retention programs that are currently set.
{"title":"Application of Decision Trees for Detection of Student Dropout Profiles","authors":"R. T. Pereira, Javier Caicedo Zambrano","doi":"10.1109/ICMLA.2017.0-107","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-107","url":null,"abstract":"The results of the research project that aims to identify patterns of student dropout from socioeconomic, academic, disciplinary and institutional data of students from undergraduate programs at the University of Nariño from Pasto city (Colombia), using data mining techniques are presented. Built a data repository with the records of students who were admitted in the period from the first half of 2004 and the second semester of 2006. Three complete cohorts were analyzed with an observation period of six years until 2011. Socioeconomic and academic student dropout profiles were discovered using classification technique based on decision trees. The knowledge generated will support effective decision-making of university staff focused to develop policies and strategies related to student retention programs that are currently set.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"20 1","pages":"528-531"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87448704","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.00-63
T. Nguyen, S. Mukhopadhyay
Multidisciplinary Optimization (MDO) is one of the most popular techniques in aerospace engineering, where the system is complex and includes the knowledge from multiple fields. However, according to the best of our knowledge, MDO has not been widely applied in decentralized reinforcement learning (RL) due to the ‘unknown’ nature of the RL problems. In this work, we apply the MDO in decentralized RL. In our MDO design, each learning agent uses system identification to closely approximate the environment and tackle the ‘unknown’ nature of the RL. Then, the agents apply the MDO principles to compute the control solution using Monte Carlo and Markov Decision Process techniques. We examined two options of MDO designs: the multidisciplinary feasible and the individual discipline feasible options, which are suitable for multi-agent learning. Our results show that the MDO individual discipline feasible option could successfully learn how to control the system. The MDO approach shows better performance than the completely decentralization and centralization approaches.
{"title":"Multidisciplinary Optimization in Decentralized Reinforcement Learning","authors":"T. Nguyen, S. Mukhopadhyay","doi":"10.1109/ICMLA.2017.00-63","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-63","url":null,"abstract":"Multidisciplinary Optimization (MDO) is one of the most popular techniques in aerospace engineering, where the system is complex and includes the knowledge from multiple fields. However, according to the best of our knowledge, MDO has not been widely applied in decentralized reinforcement learning (RL) due to the ‘unknown’ nature of the RL problems. In this work, we apply the MDO in decentralized RL. In our MDO design, each learning agent uses system identification to closely approximate the environment and tackle the ‘unknown’ nature of the RL. Then, the agents apply the MDO principles to compute the control solution using Monte Carlo and Markov Decision Process techniques. We examined two options of MDO designs: the multidisciplinary feasible and the individual discipline feasible options, which are suitable for multi-agent learning. Our results show that the MDO individual discipline feasible option could successfully learn how to control the system. The MDO approach shows better performance than the completely decentralization and centralization approaches.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"41 1","pages":"779-784"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79898904","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.0-158
Fady Medhat, D. Chesmore, John A. Robinson
Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms. This may not efficiently harness the time-frequency representation of the signal. The ConditionaL Neural Network (CLNN) takes into consideration the interrelation between the temporal frames, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN by forcing a systematic sparseness over the network’s weights using a binary mask. The masking allows the network to learn about frequency bands rather than bins, mimicking a filterbank used in signal transformations such as MFCC. Additionally, the Mask is designed to consider various combinations of features, which automates the feature hand-crafting process. We applied the MCLNN for the Environmental Sound Recognition problem using the Urbansound8k, YorNoise, ESC-10 and ESC-50 datasets. The MCLNN have achieved competitive performance compared to state-of-the-art Convolutional Neural Networks and hand-crafted attempts.
{"title":"Recognition of Acoustic Events Using Masked Conditional Neural Networks","authors":"Fady Medhat, D. Chesmore, John A. Robinson","doi":"10.1109/ICMLA.2017.0-158","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-158","url":null,"abstract":"Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms. This may not efficiently harness the time-frequency representation of the signal. The ConditionaL Neural Network (CLNN) takes into consideration the interrelation between the temporal frames, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN by forcing a systematic sparseness over the network’s weights using a binary mask. The masking allows the network to learn about frequency bands rather than bins, mimicking a filterbank used in signal transformations such as MFCC. Additionally, the Mask is designed to consider various combinations of features, which automates the feature hand-crafting process. We applied the MCLNN for the Environmental Sound Recognition problem using the Urbansound8k, YorNoise, ESC-10 and ESC-50 datasets. The MCLNN have achieved competitive performance compared to state-of-the-art Convolutional Neural Networks and hand-crafted attempts.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"43 1","pages":"199-206"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80555502","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.00-41
Esteban Ricalde, W. Banzhaf
An important challenge for traffic signal control is adapting to irregular changes in traffic. In recent years, different heuristics have been developed to address this issue. However, most of them are tested in artificial scenarios under controlled circumstances. In this paper, we present the first implementation of Genetic Programming in the evolution of traffic signal controllers for a real-world scenario. The evolved controllers are compared with a static control and an actuated control. The results indicate a significant improvement over traditional methods. Moreover, additional experiments indicate that the evolved controllers have the ability to adapt to unplanned changes in traffic conditions.
{"title":"Evolving Adaptive Traffic Signal Controllers for a Real Scenario Using Genetic Programming with an Epigenetic Mechanism","authors":"Esteban Ricalde, W. Banzhaf","doi":"10.1109/ICMLA.2017.00-41","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-41","url":null,"abstract":"An important challenge for traffic signal control is adapting to irregular changes in traffic. In recent years, different heuristics have been developed to address this issue. However, most of them are tested in artificial scenarios under controlled circumstances. In this paper, we present the first implementation of Genetic Programming in the evolution of traffic signal controllers for a real-world scenario. The evolved controllers are compared with a static control and an actuated control. The results indicate a significant improvement over traditional methods. Moreover, additional experiments indicate that the evolved controllers have the ability to adapt to unplanned changes in traffic conditions.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"29 1","pages":"897-902"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80756891","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.00-13
Aaron W. Dennis, D. Ventura
Sum-product networks (SPNs) are probabilistic models that guarantee exact inference in time linear in the size of the network. We use autoencoders in concert with SPNs to model high-dimensional, high-arity random vectors (e.g., image data). Experiments show that our proposed model, the autoencoder-SPN (AESPN), which combines two SPNs and an autoencoder, produces better samples than an SPN alone. This is true whether we sample all variables, or whether a set of unknown query variables is sampled, given a set of known evidence variables.
{"title":"Autoencoder-Enhanced Sum-Product Networks","authors":"Aaron W. Dennis, D. Ventura","doi":"10.1109/ICMLA.2017.00-13","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-13","url":null,"abstract":"Sum-product networks (SPNs) are probabilistic models that guarantee exact inference in time linear in the size of the network. We use autoencoders in concert with SPNs to model high-dimensional, high-arity random vectors (e.g., image data). Experiments show that our proposed model, the autoencoder-SPN (AESPN), which combines two SPNs and an autoencoder, produces better samples than an SPN alone. This is true whether we sample all variables, or whether a set of unknown query variables is sampled, given a set of known evidence variables.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"1041-1044"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83330178","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.00-98
Bhavani Anantapur Bache, Omar Iftikhar, O. Dehzangi
Scoliosis is a medical condition which occurs in adolescents, where an individual’s spine develops curvature. A Thoracolumbosacral orthosis (TLSO) is a type of brace used to control the lateral curvature of the spine in scoliosis. It is a nonsurgical treatment with the goal of preventing curve progression in patients with idiopathic scoliosis. To successfully monitor compliance with brace treatment, we designed and developed a wearable multi-modal sensor solution is embedded into the patient’s brace. The custom designed hardware consists of a sensor board, a force sensor, an accelerometer and a gyroscope. The force sensor collects the force being exerted on the patient’s back, while the accelerometer and gyroscope generate cues to determine the patient’s activities and lifestyle. In this paper, we propose a novel data-mining method to identify patient activities and evaluate the effectiveness of the brace treatment pervasively based on fusion of continuous force and inertial motion recordings. Our aim is to design a context-aware remote monitoring system for ubiquitous evaluation and enhancement of brace treatment compliance of adolescent idiopathic scoliosis patients. We investigated experimental scenario in which, the patient performs a series of pre-defined activities at home during day long segments of brace wear, during pervasive sensor data recordings. The experimental results demonstrated that we achieved an overall accuracy of a 100% for semi-supervised activity detection. The level of tightness of brace-fit reduced gradually over a period of 4 weeks by 33%.
{"title":"Brace Treatment Monitoring Solution for Idiopathic Scoliosis Patients","authors":"Bhavani Anantapur Bache, Omar Iftikhar, O. Dehzangi","doi":"10.1109/ICMLA.2017.00-98","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-98","url":null,"abstract":"Scoliosis is a medical condition which occurs in adolescents, where an individual’s spine develops curvature. A Thoracolumbosacral orthosis (TLSO) is a type of brace used to control the lateral curvature of the spine in scoliosis. It is a nonsurgical treatment with the goal of preventing curve progression in patients with idiopathic scoliosis. To successfully monitor compliance with brace treatment, we designed and developed a wearable multi-modal sensor solution is embedded into the patient’s brace. The custom designed hardware consists of a sensor board, a force sensor, an accelerometer and a gyroscope. The force sensor collects the force being exerted on the patient’s back, while the accelerometer and gyroscope generate cues to determine the patient’s activities and lifestyle. In this paper, we propose a novel data-mining method to identify patient activities and evaluate the effectiveness of the brace treatment pervasively based on fusion of continuous force and inertial motion recordings. Our aim is to design a context-aware remote monitoring system for ubiquitous evaluation and enhancement of brace treatment compliance of adolescent idiopathic scoliosis patients. We investigated experimental scenario in which, the patient performs a series of pre-defined activities at home during day long segments of brace wear, during pervasive sensor data recordings. The experimental results demonstrated that we achieved an overall accuracy of a 100% for semi-supervised activity detection. The level of tightness of brace-fit reduced gradually over a period of 4 weeks by 33%.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"76 1","pages":"580-585"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91261523","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.0-114
R. Benkercha, S. Moulahoum, I. Colak
The Grid Connected Photovoltaic System (GCPV) has become more used system in renewable energy. Several researches have been carried out to improve the efficiency and the decrease of energy losses. One of the important components used to increase the efficiency is the DC/DC boost converter. In this paper, a new hybrid model is proposed to control the DC/DC converter, this new controller is built on the fuzzy logic controller (FLC) and artificial neural network (ANN). The pathway taken to build the model is divided into three steps, the first step is to generate a data based on the FLC, the next step is to choose an ANN structure for modeling the FLC and the last step is the test and the validation of the obtained model. The phase of building an ANN is achieved by supervised learning based on back-propagation algorithm. This algorithm is used to train the ANN model by searching of the optimal weights and thresholds that has been a minimal root mean square error between the FLC output and the ANN model. The validation test was performed with various irradiation values between the both intelligent controllers and classical P&O algorithm simultaneously.
{"title":"Modelling of Fuzzy Logic Controller of a Maximum Power Point Tracker Based on Artificial Neural Network","authors":"R. Benkercha, S. Moulahoum, I. Colak","doi":"10.1109/ICMLA.2017.0-114","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-114","url":null,"abstract":"The Grid Connected Photovoltaic System (GCPV) has become more used system in renewable energy. Several researches have been carried out to improve the efficiency and the decrease of energy losses. One of the important components used to increase the efficiency is the DC/DC boost converter. In this paper, a new hybrid model is proposed to control the DC/DC converter, this new controller is built on the fuzzy logic controller (FLC) and artificial neural network (ANN). The pathway taken to build the model is divided into three steps, the first step is to generate a data based on the FLC, the next step is to choose an ANN structure for modeling the FLC and the last step is the test and the validation of the obtained model. The phase of building an ANN is achieved by supervised learning based on back-propagation algorithm. This algorithm is used to train the ANN model by searching of the optimal weights and thresholds that has been a minimal root mean square error between the FLC output and the ANN model. The validation test was performed with various irradiation values between the both intelligent controllers and classical P&O algorithm simultaneously.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"68 1","pages":"485-492"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78644867","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 : 2017-12-01DOI: 10.1109/ICMLA.2017.0-109
Y. Yilmaz, S. Uludag
The impact of cybersecurity attacks on the Smart Grid may cause cyber as well as physical damages, as clearly shown in the recent attacks on the power grid in Ukraine where consumers were left without power. A set of recent successful Distributed Denial-of-Service (DDoS) attacks on the Internet, facilitated by the proliferation of the Internet-of-Things powered botnets, shows that it is just a matter of time before the Smart Grid, as one of the most attractive critical infrastructure systems, becomes the target and likely victim of similar attacks, potentially leaving catastrophic disruption of power service to millions of people. It is in this context that we propose a scalable mitigation approach, referred to as Minimally Invasive Attack Mitigation via Detection Isolation and Localization (MIAMI-DIL), under a hierarchical data collection infrastructure. We provide a proofof- concept by means of simulations which show the efficacy and scalability of the proposed approach.
{"title":"Mitigating IoT-based Cyberattacks on the Smart Grid","authors":"Y. Yilmaz, S. Uludag","doi":"10.1109/ICMLA.2017.0-109","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-109","url":null,"abstract":"The impact of cybersecurity attacks on the Smart Grid may cause cyber as well as physical damages, as clearly shown in the recent attacks on the power grid in Ukraine where consumers were left without power. A set of recent successful Distributed Denial-of-Service (DDoS) attacks on the Internet, facilitated by the proliferation of the Internet-of-Things powered botnets, shows that it is just a matter of time before the Smart Grid, as one of the most attractive critical infrastructure systems, becomes the target and likely victim of similar attacks, potentially leaving catastrophic disruption of power service to millions of people. It is in this context that we propose a scalable mitigation approach, referred to as Minimally Invasive Attack Mitigation via Detection Isolation and Localization (MIAMI-DIL), under a hierarchical data collection infrastructure. We provide a proofof- concept by means of simulations which show the efficacy and scalability of the proposed approach.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"61 1","pages":"517-522"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76811393","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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