Planning of safe trajectories with interventions in both lateral and longitudinal dynamics of vehicles has huge potential for increasing the road traffic safety. Main challenges for the development of such algorithms are the consideration of vehicle nonholonomic constraints and the efficiency in terms of implementation, so that algorithms run in real time in a vehicle. The recently introduced Augmented CL-RRT algorithm is an approach that uses analytical models for trajectory planning based on the brute force evaluation of many longitudinal acceleration profiles to find collision-free trajectories. The algorithm considers nonholonomic constraints of the vehicle in complex road traffic scenarios with multiple static and dynamic objects, but it requires a lot of computation time. This work proposes a hybrid machine learning approach for predicting suitable acceleration profiles in critical traffic scenarios, so that only few acceleration profiles are used with the Augmented CL-RRT to find a safe trajectory while reducing the computation time. This is realized using a convolutional neural network variant, introduced as 3D-ConvNet, which learns spatiotemporal features from a sequence of predicted occupancy grids generated from predictions of other road traffic participants. These learned features together with hand-designed features of the EGO vehicle are used to predict acceleration profiles. Simulations are performed to compare the brute force approach with the proposed approach in terms of efficiency and safety. The results show vast improvement in terms of efficiency without harming safety. Additionally, an extension to the Augmented CL-RRT algorithm is introduced for finding a trajectory with low severity of injury, if a collision is already unavoidable.
{"title":"A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios","authors":"Amit Chaulwar, M. Botsch, W. Utschick","doi":"10.1109/ICMLA.2016.0095","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0095","url":null,"abstract":"Planning of safe trajectories with interventions in both lateral and longitudinal dynamics of vehicles has huge potential for increasing the road traffic safety. Main challenges for the development of such algorithms are the consideration of vehicle nonholonomic constraints and the efficiency in terms of implementation, so that algorithms run in real time in a vehicle. The recently introduced Augmented CL-RRT algorithm is an approach that uses analytical models for trajectory planning based on the brute force evaluation of many longitudinal acceleration profiles to find collision-free trajectories. The algorithm considers nonholonomic constraints of the vehicle in complex road traffic scenarios with multiple static and dynamic objects, but it requires a lot of computation time. This work proposes a hybrid machine learning approach for predicting suitable acceleration profiles in critical traffic scenarios, so that only few acceleration profiles are used with the Augmented CL-RRT to find a safe trajectory while reducing the computation time. This is realized using a convolutional neural network variant, introduced as 3D-ConvNet, which learns spatiotemporal features from a sequence of predicted occupancy grids generated from predictions of other road traffic participants. These learned features together with hand-designed features of the EGO vehicle are used to predict acceleration profiles. Simulations are performed to compare the brute force approach with the proposed approach in terms of efficiency and safety. The results show vast improvement in terms of efficiency without harming safety. Additionally, an extension to the Augmented CL-RRT algorithm is introduced for finding a trajectory with low severity of injury, if a collision is already unavoidable.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122264999","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}
This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.
{"title":"Automatic Species Recognition Based on Improved Birdsong Analysis","authors":"Joshua Knapp, Guangzhi Qu, Feng Zhang","doi":"10.1109/ICMLA.2016.0037","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0037","url":null,"abstract":"This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367950","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}
Contextual aware matrix factorization has been widely used in recommender systems by learning latent feature vectors of users and items along with contextual information. While most of them add identical bias for each type of side information to represent systematic tendencies in users' rating behaviors, they are not able to capture the preference unique to users or items. In this paper, we propose a probabilistic generative model which allows the bias to vary among different types of users or items. We first use Gaussian Mixture Components to cluster the users (or items) based on corresponding latent feature vectors respectively. Biases are then distributed on these clusters along with categorical side information. Finally, they are jointed with latent feature vectors of the users and items to affect the generation of observed ratings. Experiments on MovieLens-100K and MovieLens-1M data sets have shown promising results compared with state-of-the-art contextual aware recommendation approaches. We also qualitatively analyze the preferences of users and items and demonstrate differences in preference among both users and items.
{"title":"Preference Aware Recommendation Based on Categorical Information","authors":"Zhiwei Rao, Jiangchao Yao, Ya Zhang, Rui Zhang","doi":"10.1109/ICMLA.2016.0155","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0155","url":null,"abstract":"Contextual aware matrix factorization has been widely used in recommender systems by learning latent feature vectors of users and items along with contextual information. While most of them add identical bias for each type of side information to represent systematic tendencies in users' rating behaviors, they are not able to capture the preference unique to users or items. In this paper, we propose a probabilistic generative model which allows the bias to vary among different types of users or items. We first use Gaussian Mixture Components to cluster the users (or items) based on corresponding latent feature vectors respectively. Biases are then distributed on these clusters along with categorical side information. Finally, they are jointed with latent feature vectors of the users and items to affect the generation of observed ratings. Experiments on MovieLens-100K and MovieLens-1M data sets have shown promising results compared with state-of-the-art contextual aware recommendation approaches. We also qualitatively analyze the preferences of users and items and demonstrate differences in preference among both users and items.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977283","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}
Mario Alessandro Bochicchio, A. Cuzzocrea, L. Vaira
Nowadays, a great deal of attention is being devoted to big data analytics in complex healthcare environments. Fetal growth curves, which are a classical case of big healthcare data, are used in prenatal medicine to early detect potential fetal growth problems, estimate the perinatal outcome and promptly treat possible complications. However, the currently adopted curves and the related diagnostic techniques have been criticized because of their poor precision. New techniques, based on the idea of customized growth curves, have been proposed in literature. In this perspective, the problem of building customized or personalized fetal growth curves by means of big data techniques is discussed in this paper. The proposed framework introduces the idea of summarizing the massive amounts of (input) big data via multidimensional views on top of which well-known Data Mining methods like clustering and classification are applied. This overall defines a multidimensional mining approach, targeted to complex healthcare environments. A preliminary analysis on the effectiveness of the framework is also proposed.
{"title":"A Big Data Analytics Framework for Supporting Multidimensional Mining over Big Healthcare Data","authors":"Mario Alessandro Bochicchio, A. Cuzzocrea, L. Vaira","doi":"10.1109/ICMLA.2016.0090","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0090","url":null,"abstract":"Nowadays, a great deal of attention is being devoted to big data analytics in complex healthcare environments. Fetal growth curves, which are a classical case of big healthcare data, are used in prenatal medicine to early detect potential fetal growth problems, estimate the perinatal outcome and promptly treat possible complications. However, the currently adopted curves and the related diagnostic techniques have been criticized because of their poor precision. New techniques, based on the idea of customized growth curves, have been proposed in literature. In this perspective, the problem of building customized or personalized fetal growth curves by means of big data techniques is discussed in this paper. The proposed framework introduces the idea of summarizing the massive amounts of (input) big data via multidimensional views on top of which well-known Data Mining methods like clustering and classification are applied. This overall defines a multidimensional mining approach, targeted to complex healthcare environments. A preliminary analysis on the effectiveness of the framework is also proposed.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214295","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}
We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued binary datasets. Assuming that several simultaneously acting sources or elementary causes are generating the observed data, the objective of our approach is to uncover the underlying sources as well as their individual contribution to each observation with a minimum number of assumptions in an unsupervised fashion. We motivate, develop and demonstrate our method in the context of binary wafer test data which evolve during microchip fabrication. In this application, we also have to deal with incomplete datasets which can occur due to the commonly used stop-on-first-fail testing procedure or result from the aggregation of several distinct tests into BIN categories.
{"title":"A Nonnegative Tensor Factorization Approach for Three-Dimensional Binary Wafer-Test Data","authors":"T. Siegert, R. Schachtner, G. Pöppel, E. Lang","doi":"10.1109/ICMLA.2016.0151","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0151","url":null,"abstract":"We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued binary datasets. Assuming that several simultaneously acting sources or elementary causes are generating the observed data, the objective of our approach is to uncover the underlying sources as well as their individual contribution to each observation with a minimum number of assumptions in an unsupervised fashion. We motivate, develop and demonstrate our method in the context of binary wafer test data which evolve during microchip fabrication. In this application, we also have to deal with incomplete datasets which can occur due to the commonly used stop-on-first-fail testing procedure or result from the aggregation of several distinct tests into BIN categories.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130850037","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}
Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.
{"title":"Validation of a Quantifier-Based Fuzzy Classification System for Breast Cancer Patients on External Independent Cohorts","authors":"D. Soria, J. Garibaldi","doi":"10.1109/ICMLA.2016.0101","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0101","url":null,"abstract":"Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261360","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}
In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here, the disease incidence and disease severity. We present a classification system that trains a 5 class classification system to determine the state of disease of a plant. The 5 classes represent a health class and 4 disease classes. We further extend the classification system to classify different severity levels for any of the 4 diseases. Severity levels are assigned classes 1 - 5, 1 being a healthy plant, 5 being a severely diseased plant. We present ways of extracting different features from leaf images and show how different extraction methods result in different performance of the classifier. We finally present the smartphone-based system that uses the classification model learnt to do real-time prediction of the state of health of a farmers garden. This works by the farmer uploading an image of a plant in his garden and obtaining a disease score from a remote server.
{"title":"Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images","authors":"Ernest Mwebaze, Godliver Owomugisha","doi":"10.1109/ICMLA.2016.0034","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0034","url":null,"abstract":"In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here, the disease incidence and disease severity. We present a classification system that trains a 5 class classification system to determine the state of disease of a plant. The 5 classes represent a health class and 4 disease classes. We further extend the classification system to classify different severity levels for any of the 4 diseases. Severity levels are assigned classes 1 - 5, 1 being a healthy plant, 5 being a severely diseased plant. We present ways of extracting different features from leaf images and show how different extraction methods result in different performance of the classifier. We finally present the smartphone-based system that uses the classification model learnt to do real-time prediction of the state of health of a farmers garden. This works by the farmer uploading an image of a plant in his garden and obtaining a disease score from a remote server.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133249202","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}
A. Marzuoli, H. Kingravi, David Dewey, Robert S. Pienta
Robocalling, voice phishing, and caller ID spoofing are common cybercrime techniques used to launch scam campaigns through the telephony channel, which unsuspecting users have long trusted. More reliable than online complaints, a telephony honeypot provides complete, accurate and timely information about unwanted phone calls across the United States. Our first goal is to provide a large-scale data-driven analysis of the telephony spam and fraud ecosystem. Our second goal is to uniquely identify bad actors potentially operating several phone numbers. We collected about 40,000 unsolicited calls. Our results show that only a few bad actors, robocallers or telemarketers, are responsible for the majority of the spam and scam calls, and that they can be uniquely identified based on audio features from their calls. This discovery has major implications for law enforcement and businesses that are presently engaged in combatting the rise of telephony fraud. In particular, since our system allows endusers to detect fraudulent behavior and tie it back to existing fraud and spam campaigns, it can be used as the first step towards designing and deploying intelligent defense strategies.
{"title":"Uncovering the Landscape of Fraud and Spam in the Telephony Channel","authors":"A. Marzuoli, H. Kingravi, David Dewey, Robert S. Pienta","doi":"10.1109/ICMLA.2016.0153","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0153","url":null,"abstract":"Robocalling, voice phishing, and caller ID spoofing are common cybercrime techniques used to launch scam campaigns through the telephony channel, which unsuspecting users have long trusted. More reliable than online complaints, a telephony honeypot provides complete, accurate and timely information about unwanted phone calls across the United States. Our first goal is to provide a large-scale data-driven analysis of the telephony spam and fraud ecosystem. Our second goal is to uniquely identify bad actors potentially operating several phone numbers. We collected about 40,000 unsolicited calls. Our results show that only a few bad actors, robocallers or telemarketers, are responsible for the majority of the spam and scam calls, and that they can be uniquely identified based on audio features from their calls. This discovery has major implications for law enforcement and businesses that are presently engaged in combatting the rise of telephony fraud. In particular, since our system allows endusers to detect fraudulent behavior and tie it back to existing fraud and spam campaigns, it can be used as the first step towards designing and deploying intelligent defense strategies.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123158860","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}
R. Lachmann, Sandro Schulze, Manuel Nieke, C. Seidl, Ina Schaefer
Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods for test case prioritization exist, they mostly require access to source code (i.e., white-box). However, in industrial practice, system-level testing is an important task that usually grants no access to source code (i.e., black-box). Hence, for an effective regression testing process, other information has to be employed. In this paper, we introduce a novel technique for test case prioritization for manual system-level regression testing based on supervised machine learning. Our approach considers black-box meta-data, such as test case history, as well as natural language test case descriptions for prioritization. We use the machine learning algorithm SVM Rank to evaluate our approach by means of two subject systems and measure the prioritization quality. Our results imply that our technique improves the failure detection rate significantly compared to a random order. In addition, we are able to outperform a test case order given by a test expert. Moreover, using natural language descriptions improves the failure finding rate.
{"title":"System-Level Test Case Prioritization Using Machine Learning","authors":"R. Lachmann, Sandro Schulze, Manuel Nieke, C. Seidl, Ina Schaefer","doi":"10.1109/ICMLA.2016.0065","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0065","url":null,"abstract":"Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods for test case prioritization exist, they mostly require access to source code (i.e., white-box). However, in industrial practice, system-level testing is an important task that usually grants no access to source code (i.e., black-box). Hence, for an effective regression testing process, other information has to be employed. In this paper, we introduce a novel technique for test case prioritization for manual system-level regression testing based on supervised machine learning. Our approach considers black-box meta-data, such as test case history, as well as natural language test case descriptions for prioritization. We use the machine learning algorithm SVM Rank to evaluate our approach by means of two subject systems and measure the prioritization quality. Our results imply that our technique improves the failure detection rate significantly compared to a random order. In addition, we are able to outperform a test case order given by a test expert. Moreover, using natural language descriptions improves the failure finding rate.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123172170","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}
Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, S. Gupta
Machine learning algorithms are widely used in cyber forensic biometric systems to analyze a subject's truthfulness in an interrogation. An analytical method (rather than experimental) to evaluate the security strength of these systems under potential cyber attacks is essential. In this paper, we formalize a theoretical method for analyzing the immunity of a machine learning based cyber forensic system against evidence tampering attack. We apply our theory on brain signal based forensic systems that use neural networks to classify responses from a subject. Attack simulation is run to validate our theoretical analysis results.
{"title":"Toward Parametric Security Analysis of Machine Learning Based Cyber Forensic Biometric Systems","authors":"Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, S. Gupta","doi":"10.1109/ICMLA.2016.0110","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0110","url":null,"abstract":"Machine learning algorithms are widely used in cyber forensic biometric systems to analyze a subject's truthfulness in an interrogation. An analytical method (rather than experimental) to evaluate the security strength of these systems under potential cyber attacks is essential. In this paper, we formalize a theoretical method for analyzing the immunity of a machine learning based cyber forensic system against evidence tampering attack. We apply our theory on brain signal based forensic systems that use neural networks to classify responses from a subject. Attack simulation is run to validate our theoretical analysis results.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128859940","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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