Pub Date : 2015-08-20DOI: 10.1109/Trustcom.2015.353
Richard S. Weiss, L. Reznik, Yanyan Zhuang, Andrew Hoffman, Darrel Pollard, Albert Rafetseder, Tao Li, Justin Cappos
Mobile devices today, such as smartphones and tablets, have become both more complex and diverse. This paper presents a framework to evaluate the trustworthiness of the individual components in a mobile system, as well as the entire system. The major components are applications, devices and networks of devices. Given this diversity and multiple levels of a mobile system, we develop a hierarchical trust evaluation methodology, which enables the combination of trust metrics and allows us to verify the trust metric for each component based on the trust metrics for others. The paper first demonstrates this idea for individual applications and Android-based smartphones. The methodology involves two stages: initial trust evaluation and trust verification. In the first stage, an expert rule system is used to produce trust metrics at the lowest level of the hierarchy. In the second stage, the trust metrics are verified by comparing data from components and a trust evaluation is produced for the combined system. This paper presents the results of two empirical studies, in which this methodology is applied and tested. The first study involves monitoring resource utilization and evaluating trust based on resource consumption patterns. We measured battery voltage, CPU utilization and network communication for individual apps and detected anomalous behavior that could be indicative of malicious code. The second study involves verification of the trust evaluation by comparing the data from two different devices: the GPS location from an Android smartphone in an automobile and the data from an on-board diagnostics (OBD) sensor of the same vehicle.
{"title":"Trust Evaluation in Mobile Devices: An Empirical Study","authors":"Richard S. Weiss, L. Reznik, Yanyan Zhuang, Andrew Hoffman, Darrel Pollard, Albert Rafetseder, Tao Li, Justin Cappos","doi":"10.1109/Trustcom.2015.353","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.353","url":null,"abstract":"Mobile devices today, such as smartphones and tablets, have become both more complex and diverse. This paper presents a framework to evaluate the trustworthiness of the individual components in a mobile system, as well as the entire system. The major components are applications, devices and networks of devices. Given this diversity and multiple levels of a mobile system, we develop a hierarchical trust evaluation methodology, which enables the combination of trust metrics and allows us to verify the trust metric for each component based on the trust metrics for others. The paper first demonstrates this idea for individual applications and Android-based smartphones. The methodology involves two stages: initial trust evaluation and trust verification. In the first stage, an expert rule system is used to produce trust metrics at the lowest level of the hierarchy. In the second stage, the trust metrics are verified by comparing data from components and a trust evaluation is produced for the combined system. This paper presents the results of two empirical studies, in which this methodology is applied and tested. The first study involves monitoring resource utilization and evaluating trust based on resource consumption patterns. We measured battery voltage, CPU utilization and network communication for individual apps and detected anomalous behavior that could be indicative of malicious code. The second study involves verification of the trust evaluation by comparing the data from two different devices: the GPS location from an Android smartphone in an automobile and the data from an on-board diagnostics (OBD) sensor of the same vehicle.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126118634","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.423
Milica Milutinovic, Italo Dacosta, A. Put, B. Decker
Incentives systems are widely adopted to encourage user actions or contributions that benefit a service provider or a community. In exchange for their actions, users receive incentive points that can be used to obtain benefits or reputation. However, these systems require users to have a central account associated with all their activities. This approach allows providers to collect vast amounts of users' private information, even if pseudonyms are used. In this paper, we present uCentive, a flexible and efficient incentives scheme that allows users to earn and redeem incentives (uCents) that cannot be linked to their identities or actions. In addition, users can prove, if requested, ownership of their incentives without breaking unlinkability guarantees. uCentive also offers perfect forward unlinkability -- even if the user's secrets are compromised, redeemed uCents cannot be linked together or to the user's identity. Even though our scheme relies on heavy cryptography, experimental evaluation shows that it is adequate for mobile devices such as smartphones. We have also made our uCentive library and prototype apps publicly available for further assessment. In short, we provide a practical privacy-preserving incentives scheme that can eliminate users' growing privacy concerns when using such systems.
{"title":"uCentive: An Efficient, Anonymous and Unlinkable Incentives Scheme","authors":"Milica Milutinovic, Italo Dacosta, A. Put, B. Decker","doi":"10.1109/Trustcom.2015.423","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.423","url":null,"abstract":"Incentives systems are widely adopted to encourage user actions or contributions that benefit a service provider or a community. In exchange for their actions, users receive incentive points that can be used to obtain benefits or reputation. However, these systems require users to have a central account associated with all their activities. This approach allows providers to collect vast amounts of users' private information, even if pseudonyms are used. In this paper, we present uCentive, a flexible and efficient incentives scheme that allows users to earn and redeem incentives (uCents) that cannot be linked to their identities or actions. In addition, users can prove, if requested, ownership of their incentives without breaking unlinkability guarantees. uCentive also offers perfect forward unlinkability -- even if the user's secrets are compromised, redeemed uCents cannot be linked together or to the user's identity. Even though our scheme relies on heavy cryptography, experimental evaluation shows that it is adequate for mobile devices such as smartphones. We have also made our uCentive library and prototype apps publicly available for further assessment. In short, we provide a practical privacy-preserving incentives scheme that can eliminate users' growing privacy concerns when using such systems.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126120005","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.565
Muhidin A. Mohamed, M. Oussalah
In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.
{"title":"Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources","authors":"Muhidin A. Mohamed, M. Oussalah","doi":"10.1109/Trustcom.2015.565","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.565","url":null,"abstract":"In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"152 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133617878","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.518
Kyoungsoo Bok, Eunkyung Ryu, Junho Park, Jaesoo Yoo
Wireless sensor networks have enabled multimedia data collection such as video or audio with the advancement of computer technology. In this paper, we propose an energy efficient congestion control scheme for multimedia data in wireless sensor networks. The proposed scheme extracts and transfers dynamic regions by considering monitoring characteristics over multimedia data to reduce the transferred data. Furthermore, it can reduce the packet size by deleting and transferring low-priority bit data by considering multimedia data characteristics during congestion situations to minimize packet loss.
{"title":"An Energy Efficient Congestion Control Scheme for Multimedia Data in Wireless Sensor Networks","authors":"Kyoungsoo Bok, Eunkyung Ryu, Junho Park, Jaesoo Yoo","doi":"10.1109/Trustcom.2015.518","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.518","url":null,"abstract":"Wireless sensor networks have enabled multimedia data collection such as video or audio with the advancement of computer technology. In this paper, we propose an energy efficient congestion control scheme for multimedia data in wireless sensor networks. The proposed scheme extracts and transfers dynamic regions by considering monitoring characteristics over multimedia data to reduce the transferred data. Furthermore, it can reduce the packet size by deleting and transferring low-priority bit data by considering multimedia data characteristics during congestion situations to minimize packet loss.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131424895","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.453
A. Ometov, K. Zhidanov, S. Bezzateev, R. Florea, S. Andreev, Y. Koucheryavy
Network-assisted device-to-device (D2D) communication is a next-generation wireless technology enabling direct connectivity between proximate user devices under the control of cellular infrastructure. It couples together the centralized and the distributed network architectures, and as such requires respective enablers for secure, private, and trusted data exchange especially when cellular control link is not available at all times. In this work, we conduct the state-of-the-art overview and propose a novel algorithm to maintain security functions of proximate devices in case of unreliable cellular connectivity, whether a new device joins the secure group of users or an existing device leaves it. Our proposed solution and its rigorous mathematical implementation detailed in this work open door to a novel generation of secure proximity-based services and applications in future wireless communication systems.
{"title":"Securing Network-Assisted Direct Communication: The Case of Unreliable Cellular Connectivity","authors":"A. Ometov, K. Zhidanov, S. Bezzateev, R. Florea, S. Andreev, Y. Koucheryavy","doi":"10.1109/Trustcom.2015.453","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.453","url":null,"abstract":"Network-assisted device-to-device (D2D) communication is a next-generation wireless technology enabling direct connectivity between proximate user devices under the control of cellular infrastructure. It couples together the centralized and the distributed network architectures, and as such requires respective enablers for secure, private, and trusted data exchange especially when cellular control link is not available at all times. In this work, we conduct the state-of-the-art overview and propose a novel algorithm to maintain security functions of proximate devices in case of unreliable cellular connectivity, whether a new device joins the secure group of users or an existing device leaves it. Our proposed solution and its rigorous mathematical implementation detailed in this work open door to a novel generation of secure proximity-based services and applications in future wireless communication systems.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132511363","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.538
F. Jacob, Jens Mittag, H. Hartenstein
The emergence of decentralized crypto currencies such as Bitcoin and the success of the anonymizing network TOR lead to an increased interest in peer-to-peer based technologies lately - not only due to the prevalent deployment of mass network surveillance technologies by authorities around the globe. While today's application services typically employ centralized client/server architectures that require the user to trust the service provider, new decentralized platforms that eliminate this need of trust are on their rise. In this paper we critically analyze a fully decentralized alternative to today's digital ecosystem - MaidSafe - that drops most of the commonly applied principles. The MaidSafe network implements a fully decentralized personal data storage platform on which user applications can be built. The network is made up by individual users who contribute storage, computing power and bandwidth. All communication between network nodes is encrypted, yet users only have to remember a username and password. To guarantee these objectives, MaidSafe combines mechanisms such as Self-Authentication, Self-Encryption, and a P2P-based public key infrastructure. This paper provides a condensed description of MaidSafe's key protocol mechanisms, derives the underlying identity and access management architecture, and evaluates it with respect to security and privacy aspects.
{"title":"A Security Analysis of the Emerging P2P-Based Personal Cloud Platform MaidSafe","authors":"F. Jacob, Jens Mittag, H. Hartenstein","doi":"10.1109/Trustcom.2015.538","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.538","url":null,"abstract":"The emergence of decentralized crypto currencies such as Bitcoin and the success of the anonymizing network TOR lead to an increased interest in peer-to-peer based technologies lately - not only due to the prevalent deployment of mass network surveillance technologies by authorities around the globe. While today's application services typically employ centralized client/server architectures that require the user to trust the service provider, new decentralized platforms that eliminate this need of trust are on their rise. In this paper we critically analyze a fully decentralized alternative to today's digital ecosystem - MaidSafe - that drops most of the commonly applied principles. The MaidSafe network implements a fully decentralized personal data storage platform on which user applications can be built. The network is made up by individual users who contribute storage, computing power and bandwidth. All communication between network nodes is encrypted, yet users only have to remember a username and password. To guarantee these objectives, MaidSafe combines mechanisms such as Self-Authentication, Self-Encryption, and a P2P-based public key infrastructure. This paper provides a condensed description of MaidSafe's key protocol mechanisms, derives the underlying identity and access management architecture, and evaluates it with respect to security and privacy aspects.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868190","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.500
S. Rao, S. Holtmanns, Ian Oliver, T. Aura
The increase in usage of mobile phones and the relative increase in the number of mobile phone thefts have imposed an overhead on securely retrieving the stolen or missing devices. While the mobile security researchers try to figure out various mechanisms to track such devices, attackers on the other hand are trying to exploit weaknesses in the mobile network system to dissipate into the dark side with stolen devices. In this paper, we present how the SS7- MAP protocol can be misused to help an attacker to unblock the device from the stolen list and use it normally.
{"title":"Unblocking Stolen Mobile Devices Using SS7-MAP Vulnerabilities: Exploiting the Relationship between IMEI and IMSI for EIR Access","authors":"S. Rao, S. Holtmanns, Ian Oliver, T. Aura","doi":"10.1109/Trustcom.2015.500","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.500","url":null,"abstract":"The increase in usage of mobile phones and the relative increase in the number of mobile phone thefts have imposed an overhead on securely retrieving the stolen or missing devices. While the mobile security researchers try to figure out various mechanisms to track such devices, attackers on the other hand are trying to exploit weaknesses in the mobile network system to dissipate into the dark side with stolen devices. In this paper, we present how the SS7- MAP protocol can be misused to help an attacker to unblock the device from the stolen list and use it normally.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131971610","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.560
Moufida Rehab Adjout, F. Boufarès
The large volumes of information emerging by the progress of technology and the growing individual needs of data mining, makes training of very large scale of data a challenging task. However, this information cannot be practically analyzed on a single machine due to the sheer size of the data to fit in memory. For this purpose, the process of such data requires the use of high-performance analytical systems running on distributed environments. To this end standard analytics algorithms need to be adapted to take advantage of cloud computing models which provide scalability and flexibility. This paper introduces a new distributed training method, which combines the widely used framework, MapReduce, for Multiple Linear Regression which will be based on the QR decomposition and the ordinary least squares method adapted to MapReduce. Our platform is deployed on Cloud Amazon EMR service. Experimental results demonstrate that our parallel version of the Multiple Linear Regression can efficiently handle very large datasets with different parameter settings (number, size and structure of machines).
{"title":"Scalable Massively Parallel Learning of Multiple Linear Regression Algorithm with MapReduce","authors":"Moufida Rehab Adjout, F. Boufarès","doi":"10.1109/Trustcom.2015.560","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.560","url":null,"abstract":"The large volumes of information emerging by the progress of technology and the growing individual needs of data mining, makes training of very large scale of data a challenging task. However, this information cannot be practically analyzed on a single machine due to the sheer size of the data to fit in memory. For this purpose, the process of such data requires the use of high-performance analytical systems running on distributed environments. To this end standard analytics algorithms need to be adapted to take advantage of cloud computing models which provide scalability and flexibility. This paper introduces a new distributed training method, which combines the widely used framework, MapReduce, for Multiple Linear Regression which will be based on the QR decomposition and the ordinary least squares method adapted to MapReduce. Our platform is deployed on Cloud Amazon EMR service. Experimental results demonstrate that our parallel version of the Multiple Linear Regression can efficiently handle very large datasets with different parameter settings (number, size and structure of machines).","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485410","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.577
Jesús Maillo, I. Triguero, F. Herrera
The k-Nearest Neighbor classifier is one of the most well known methods in data mining because of its effectiveness and simplicity. Due to its way of working, the application of this classifier may be restricted to problems with a certain number of examples, especially, when the runtime matters. However, the classification of large amounts of data is becoming a necessary task in a great number of real-world applications. This topic is known as big data classification, in which standard data mining techniques normally fail to tackle such volume of data. In this contribution we propose a MapReduce-based approach for k-Nearest neighbor classification. This model allows us to simultaneously classify large amounts of unseen cases (test examples) against a big (training) dataset. To do so, the map phase will determine the k-nearest neighbors in different splits of the data. Afterwards, the reduce stage will compute the definitive neighbors from the list obtained in the map phase. The designed model allows the k-Nearest neighbor classifier to scale to datasets of arbitrary size, just by simply adding more computing nodes if necessary. Moreover, this parallel implementation provides the exact classification rate as the original k-NN model. The conducted experiments, using a dataset with up to 1 million instances, show the promising scalability capabilities of the proposed approach.
{"title":"A MapReduce-Based k-Nearest Neighbor Approach for Big Data Classification","authors":"Jesús Maillo, I. Triguero, F. Herrera","doi":"10.1109/Trustcom.2015.577","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.577","url":null,"abstract":"The k-Nearest Neighbor classifier is one of the most well known methods in data mining because of its effectiveness and simplicity. Due to its way of working, the application of this classifier may be restricted to problems with a certain number of examples, especially, when the runtime matters. However, the classification of large amounts of data is becoming a necessary task in a great number of real-world applications. This topic is known as big data classification, in which standard data mining techniques normally fail to tackle such volume of data. In this contribution we propose a MapReduce-based approach for k-Nearest neighbor classification. This model allows us to simultaneously classify large amounts of unseen cases (test examples) against a big (training) dataset. To do so, the map phase will determine the k-nearest neighbors in different splits of the data. Afterwards, the reduce stage will compute the definitive neighbors from the list obtained in the map phase. The designed model allows the k-Nearest neighbor classifier to scale to datasets of arbitrary size, just by simply adding more computing nodes if necessary. Moreover, this parallel implementation provides the exact classification rate as the original k-NN model. The conducted experiments, using a dataset with up to 1 million instances, show the promising scalability capabilities of the proposed approach.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133112800","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 : 2015-08-20DOI: 10.1109/Trustcom.2015.386
Lars Baumgärtner, Jonas Höchst, M. Leinweber, Bernd Freisleben
Electronic mail is one of the oldest and widely used services in the Internet. In this paper, an empirical study of the security properties of email server communication within the German IP address space range is presented. Instead of investigating end-user security or end-to-end encryption, we focus on the connections between SMTP servers relying on transport layer security. We analyze the involved ciphers suites, the certificates used and certificate authorities, and the behavior of email providers when communicating with improperly secured email servers. Conclusions drawn from this analysis lead to several recommendations to mitigate the security issues currently present in the email system as it is deployed in the Internet.
{"title":"How to Misuse SMTP over TLS: A Study of the (In) Security of Email Server Communication","authors":"Lars Baumgärtner, Jonas Höchst, M. Leinweber, Bernd Freisleben","doi":"10.1109/Trustcom.2015.386","DOIUrl":"https://doi.org/10.1109/Trustcom.2015.386","url":null,"abstract":"Electronic mail is one of the oldest and widely used services in the Internet. In this paper, an empirical study of the security properties of email server communication within the German IP address space range is presented. Instead of investigating end-user security or end-to-end encryption, we focus on the connections between SMTP servers relying on transport layer security. We analyze the involved ciphers suites, the certificates used and certificate authorities, and the behavior of email providers when communicating with improperly secured email servers. Conclusions drawn from this analysis lead to several recommendations to mitigate the security issues currently present in the email system as it is deployed in the Internet.","PeriodicalId":277092,"journal":{"name":"2015 IEEE Trustcom/BigDataSE/ISPA","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441252","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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