Message Streams Research Articles

Automotive Electrical and Electronic Architecture Security via Distributed In-Vehicle Traffic Monitoring

Due to the growing interconnectedness and complexity of in-vehicle networks, in addition to safety, security is becoming an increasingly important topic in the automotive domain. In this paper, we study techniques for detecting security infringements in automotive electrical and electronic (E/E) architectures. Toward this we propose in-vehicle network traffic monitoring to detect increased transmission rates of manipulated message streams. Attacks causing timing violations can disrupt safety-critical functions and have severe consequences. To reduce costs and prevent single points of failure, our approach enables an automatic distribution of detection tasks among selected E/E architecture components, such as a subset of electronic control units. First, we analyze a concrete E/E system architecture to determine the communication parameters and properties necessary for detecting security attacks. These are then used for a parametrization of the corresponding detection algorithms and the distribution of attack detection tasks. We use a lightweight message monitoring method and optimize the placement of detection tasks to ensure a full-coverage of the E/E system architecture and a timely detection of an attack.

Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks

With the ever increasing acceptance of Online Social Networks (OSNs), a new dimension has evolved for communication amongst humans. People now share continuous streams of messages to contribute their interests, indications, discuss activities, status, latest trends and much more. This gives an opportunity to monitor and mine the opinions of a large number of online active population in real time. On one side where researchers can find out a pattern to judge the mood of the user, a serious problem of detection of irony and sarcasm in textual data poses threat to the accuracy of the techniques evolved till date. In this paper, we are proposing a deep learning based convolutional neural network method that focuses on the skip gram technique to convert words to vectors and further to detect and identify the sarcasm and irony from the normal polarized text taken from Twitter. We try to discover patterns from the textual datasets based on the identifiable features. Our approach is giving an overall accuracy of 89.9%.

Quoting and retweeting as communicative practices in computer mediated discourse

In this paper, quoting in e-mail communication and retweeting in Twitter message streams is analysed as users’ recontextualization practices which share formal features but serve different communicative needs. After a short discussion of user related push factors and medium specific pull factors and their interplay in computer mediated discourse, quoting in e-mail discussions is characterised as a CMD practice through which users manage interactional and topical coherence problems caused by technological factors. Retweeting, conversely, is characterised as a platform-specific variant of “sharing” digital objects which is afforded through social network sites. A formal and functional analysis of edited and unedited retweets reveals that this recontextualization practice shares formal properties with quoting but that it is mainly used for interpersonal rather than for topic- and interaction-oriented ends. The results lay the basis for some generalisations concerning the conceptualization and the interplay of communicative push and pull factors in current accounts of the adaptation of existing and the emergence of new social practices and genres in CMD.

A Factorial Survey Study on the Influence of Advertising Place and the Use of Personal Data on User Acceptance of Facebook Ads

Advertisements on social networking sites are typically unrequested messages placed in between the user-generated content. What makes users accept these ads (or not)? A factorial survey was conducted to uncover the impact of different factors related to the use of personal data (e.g., sensitivity of personal data) and advertising place (e.g., ad location) of Facebook ads on user acceptance (i.e., fairness and appropriateness) of the advertisement. Placement of the advertisement was the only factor to significantly affect user acceptance. Moreover, the effect of ad placement largely depended on the degree of involvement with the advertised product. Among low-involved subjects, sidebar ad placement was better accepted. Moreover, the influence of ad placement on user acceptance was fully mediated by perceived intrusiveness of the ad. This indirect effect was not significant in the high-involved subgroup, where message stream placement was better accepted. Implications for targeting advertising on Facebook are discussed.

Designing a Social Support System for College Adjustment and Social Support

A main characteristic of current social systems is that they are designed to never-endingly captivate the attention of users, but are rarely designed to give support to those that need it. We describe the development process of Buddy, a social support mobile app for college students. We created a message stream algorithmically designed to give more attention to those with low psychological wellbeing, hidden feedback metrics to discourage competition or social comparison, and bots to deliver automatic one-click feedback in the form of virtual hugs. Across two field studies, we found that these small design changes can facilitate different types of behavior and community norms.

Robust Secure Transmission of Using Main-Lobe-Integration-Based Leakage Beamforming in Directional Modulation MU-MIMO Systems

In the paper, we make an investigation of robust beamforming for secure directional modulation in the multi-user multiple-input and multiple output (MU-MIMO) systems in the presence of direction angle measurement errors. When statistical knowledge of direction angle measurement errors is unavailable, a novel robust beamforming scheme of combining main-lobe-integration (MLI) and leakage is proposed to simultaneously transmit multiple different independent parallel confidential message streams to the associated multiple distinct desired users. The proposed scheme includes two steps: designing the beamforming vectors of the useful confidential messages and constructing artificial noise (AN) projection matrix. Here, in its first step, the beamforming vectors for the useful confidential messages of desired user k are given by minimizing the useful signal power leakage from main-lobe of desired user k to the sum of main-lobes of the remaining desired directions plus main-lobes of all eavesdropper directions. In its second step, the AN projection matrix is constructed by simultaneously maximizing the AN power leakage to all eavesdropper directions such that all eavesdroppers are disrupted seriously, where AN is viewed by the transmitter as a useful signal for eavedroppers. Due to independent beamforming vectors for different desired users, a more secure transmission is achieved. Compared with conventional non-robust methods, the proposed method can provide a significant improvement in bit error rate along the desired directions and secrecy-sum-rate towards multiple desired users without needing statistical property or distribution of angle measurement errors.

Discover trends in public emotion using social sensing

Social networks, such as Twitter and Facebook, are increasingly used by individuals to share their opinions and feelings on current issues and events in the form of text messages. This results in massive amounts of text stream data rich with emotional content. Such data provides a great opportunity for identifying and analyzing people's emotions in response to various public events, such as epidemics, terrorist attacks and political elections. However, the high volume and fast pace of social data make it challenging to analyze public emotions in social networks in real-time. In this paper we propose an online method to measure public emotion and detect emotion-intensive moments during real-life events. We first classify emotions expressed in text stream messages using a supervised learning approach. Then we aggregate each emotion class to discover emotionevolving patterns over time and detect emotion-intensive moments. Our emotion analysis method is shown to present a fast and robust approach of analyzing online streams of text messages.

SmartMom Text Messaging for Prenatal Education: A Qualitative Focus Group Study to Explore Canadian Women’s Perceptions

BackgroundWe engaged Canadian women in the development of a prenatal education program delivered via one-way text messaging called SmartMom. SmartMom is the first peer-reviewed, evidence-based mHealth program for prenatal education in Canada and the first to be endorsed by the Society of Obstetricians and Gynaecologists of Canada.ObjectiveTo explore women’s preferences for a prenatal education program by text messaging.MethodsWe conducted a qualitative focus group study in three Canadian communities in the Northern Health Authority. Women completed a demographic questionnaire, participated in a guided discussion about their pregnancy information-seeking behavior, reviewed a printed copy of the SmartMom text messages, and then engaged in a moderated discussion about their perceptions of the usability of the SmartMom program. Open-ended questions explored women’s perceptions regarding the message content, acceptability of receiving information by text message, positive health behaviors they might engage in after receiving a message, modifiable program factors, and intention to use the program. Thematic analysis of transcribed audio recordings was undertaken and modifications were made to the SmartMom program based on these findings.ResultsA total of 40 women participated in seven focus groups in three rural northern communities. The vast majority had a mobile phone (39/40, 98%), used text messages “all the time” (28/40, 70%), and surfed the Internet on their phone (37/40, 93%). Participants perceived SmartMom to be highly acceptable and relevant. The text message modality reflected how participants currently sought pregnancy-related information and provided them with local information tailored to their gestational age, which they had not received through other pregnancy resources. Women recommended adding the opportunity to receive supplemental streams of messages tailored to their individual needs, for example, depression, pregnancy after previous cesarean, >35 years of age, new immigrants, and harm reduction for smoking and alcohol.ConclusionsThis formative qualitative evaluation provides evidence that a prenatal education program by text messaging, SmartMom, is acceptable to the end users. These findings support the usability of the SmartMom program at a population level and the development of an evaluation program exploring the effects of the text messages on adoption of health-promoting behaviors and maternal-child health outcomes.

Structured I/O streams in Clive: a toolbox approach for wide area network computing

Most distributed applications and tools used in wide area networks and Cloud computing environments use the UNIX I/O framework. In this framework, processes use file descriptors for standard I/O and file access, with the traditional open/close/read/write interface. Although this design has proven to be excellent since the 1970s, it is not appropriate for today wide-area systems because of the implied RPCs and network latency. There are systems relying on message streams that perform well in such environments, but they depart from the toolbox approach embraced by UNIX, making it harder to combine existing programs to solve new tasks. In this paper we describe the design, implementation, and usage of a new I/O framework, built to enable the construction of services in environments with high latency, while preserving the programmability of the system as a whole and making it convenient to combine existing tools and programs. The framework relies on named channels for I/O. Each channel carries a stream of typed data including directory entries, raw bytes, and other application-specific data. Separate commands using the framework may be combined as in UNIX, but still tolerate high latencies as found in distributed and Cloud computing environments, enabling a toolbox approach in such environments.

Comparative case study of life usage and data-driven prognostics techniques using aircraft fault messages

Prognostics are a key activity in repair and maintenance operations. A recent approach to condition-based maintenance is the data-driven approach. This approach has been mostly based on past failure time measures, and sensed measurements of component degradation to derive estimates of the remaining useful life of equipment. An alternative source of data, rarely used in these models, is the stream of automatic messages derived from diagnostics systems, which consist of fault codes indicating abnormal events or deviations from optimal operation. Despite the richness and concise nature of these messages, their difficult interpretation poses significant challenges to its use in prognostics. This paper aims to show that data-driven prognostics based on this type of messages can be better suited to maintenance than time-based approaches. We illustrate this comparison with an industrial case study involving the removal times of a bleed valve from the aircraft air management system. Our experimental results reveal a significant accuracy improvement over the contrasting time-based models. We also establish the contribution to this improvement of the data-driven methods and message-related predictors.

Исследование функции распределения почтового трафика для подтверждения гипотезы о его сезонности

In this paper, mail traffic distribution functions are studied using various statistical methods. Streams of unwanted e-mail messages (spam) and legitimate mail traffic are studied separately. The current state of the problem and similar studies are considered. It is concluded that there has been a significant increase in mail traffic in recent years and a change in its structure. As a hypothesis, an assumption is made about the "seasonality"(repetition of rise and fall time periods) of incoming e-mails. This hypothesis has been partially confirmed. The R/S analysis and the Hurst method are used to confirm the hypothesis. The analysis shows the presence of stable trends of legitimate mail traffic and wandering series tendency for unwanted e-mail traffic. In conclusion, there is a discussion of practical problems for which the obtained results can be applied.DOI 10.14258/izvasu(2017)4-28

Beating the Artificial Chaos: Fighting OSN Spam Using Its Own Templates

Online social networks (OSNs) are extremely popular among Internet users. However, spam originating from friends and acquaintances not only reduces the joy of Internet surfing but also causes damage to less security-savvy users. Prior countermeasures combat OSN spam from different angles. Due to the diversity of spam, there is hardly any existing method that can independently detect the majority or most of OSN spam. In this paper, we empirically analyze the textual pattern of a large collection of OSN spam. An inspiring finding is that the majority (e.g., 76.4% in 2015) of the collected spam is generated with underlying templates. Based on the analysis, we propose tangram, an OSN spam filtering system that performs online inspection on the stream of user-generated messages. Tangram extracts the templates of spam detected by existing methods and then matching messages against the templates toward the accurate and the fast spam detection. It automatically divides the OSN spam into segments and uses the segments to construct templates to filter future spam. Experimental results on Twitter and Facebook data sets show that tangram is highly accurate and can rapidly generate templates to throttle newly emerged campaigns. Furthermore, we analyze the behavior of detected OSN spammers. We find a series of spammer properties-such as spamming accounts are created in bursts and a single active organization orchestrates more spam than all other spammers combined-that promise more comprehensive spam countermeasures.

An approach to developing the SIEM system for the Internet of Things

This article suggests an approach to building systems for analyzing security incidents in the Internet of Things as a self-similar system within the ambit of the normal operation of its constituent objects. The graph model of the Internet of Things as a cyberphysical system has been developed and used as basis to prove that the analysis of paired relations selected from a discrete message stream from devices will be enough to detect security incidents.

Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

In this paper we present a methodology to analyze and visualize streams of Social Media messages and apply it to a case in which Twitter is used as a backchannel, i.e. as a communication medium through which participants follow an event in the real world as it unfolds. Unlike other methods based on social networks or theories of information diffusion, we do not assume proximity or a pre-existing social structure to model content generation and diffusion by distributed users; instead we refer to concepts and theories from discourse psychology and conversational analysis to track online interaction and discover how people collectively make sense of novel events through micro-blogging. In particular, the proposed methodology extracts concept maps from twitter streams and uses a mix of sentiment and topological metrics computed over the extracted concept maps to build visual devices and display the conversational flow represented as a trajectory through time of automatically extracted topics. We evaluated the proposed method through data collected from the analysis of Twitter users’ reactions to the March 2015 Apple Keynote during which the company announced the official launch of several new products.

A Concise Queuing Model for Controller Performance in Software-Defined Networks

Software-defined Networking (SDN) is commonly seen as a promising way towards future Internet. Understanding the performance and limitation of its logically centralized controllers is a prerequisite of SDN deployments. For this end, this paper proposes a concise queuing model for SDN controller performance. We first investigate that the input of a SDN controller is a hybrid Poisson stream of packet-in messages. Then, we analyze the message processing of a SDN controller to achieve a concise queuing model M/M/1 for its performance, and further derive its performance parameters from queuing theory. Finally, our proposed model is evaluated by measuring a well-known controller with the benchmark Cbench. Experimental results indicate that our model is a better approximation of the controller performance than the existing ones.

Efficient online extraction of keywords for localized events in twitter

Messages published via social media sites, such as Twitter, Facebook, and Foursquare hide a considerable amount of information about real world events. The timely identification of such events from this huge, unstructured, and noisy user-generated content plays an important role in increasing situation awareness and in supporting useful applications such as recommendation systems. Interestingly, a large number of these messages are enriched with location information, due to the recent advancements of today's location acquisition techniques. This, in turn, enables location-aware event mining, i.e., the detection and tracking of localized events such as sport events, demonstrations, or traffic jams, to name but a few. The main building blocks of a localized event are local keywords that exhibit a surge in usage at the event location. In this paper, we propose an approach that aims at extracting local keywords from a stream of Twitter messages by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction procedure is performed in an online fashion using a sliding window over the Twitter stream. Additionally, we address the problem of spatial outliers that adversely affect a sound identification of local keywords. Spatial outliers occur when people far away from the location of an event use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. To ensure scalability, we utilize a hierarchical spatial index to gradually prune the geographic space and thus to efficiently perform complex spatial computations. Extensive comparative experiments are conducted using Twitter data. The analysis of the experimental results demonstrates the superiority of our approach over existing methods in terms of efficiency and precision of the obtained results.

Social Media Conversation Monitoring: Visualize Information Contents of Twitter Messages Using Conversational Metrics

In this paper we present a novel method to extract and visualize actionable information from streams of social media messages, analyzed as conversational elements.Our method has been applied to over 4 million messages related to more than 35 different events, demonstrating good results identifying conversational patterns.

PFC-CTR, PFC-OCB: Efficient stream cipher modes of authencryption

ABSTRACTAuthencryption is a cryptographic process of providing confidentiality and integrity protection of messages in a single pass, simultaneously, without any support of conventional checksum, MAC, or hash function. This article encompasses the process to construct the stream cipher-based authencryption environment, where the keystream is generated from any secure block algorithm like AES. Accordingly, in order to demonstrate the working principle of authencryption in the stream cipher environment, the authors introduce two stream cipher modes of authencryption, namely, PFC-CTR and PFC-OCB. PFC-CTR denotes the counter-based authencryption environment, and PFC-OCB specifies the authencryption environment built upon the underlying foundation of OCB. They argue that both of the proposed stream cipher modes of authencryption are quite robust against several active attacks (e.g., message stream modification attacks, known-plain-text attacks, and chosen-plain-text attacks). At the same time, they can efficiently deal with other issues like “limited error propagation,” and so on, existing in several conventional stream cipher modes of operation like CFB, OFB, and CTR.

Robust stream‐cipher mode of authenticated encryption for secure communication in wireless sensor network

AbstractAuthenticated Encryption is a cryptographic process of providing confidentiality and integrity protection of messages in a single pass without any support of conventional checksum, Message Authentication Code (MAC), or hash function. In this paper, at first, we show that how to construct the stream‐cipher‐based Authenticated Encryption environment, where the key‐stream is generated from any secure block algorithm like Advanced Encryption Standard. In order to do that, we introduce two stream‐cipher modes of Authenticated Encryption, namely, PFC‐CTR (Counter‐based Authenticated Encryption environment) and PFC‐OCB (OCB‐based Authenticated Encryption environment), which are quite robust against several active attacks, for example, message stream modification attacks, known‐plain‐text attacks, and chosen‐plain‐text attacks, and at the same time, they can also efficiently deal with some other issues like “limited error propagation” existing in several conventional stream‐cipher modes of operation like Cipher Feedback, Output Feedback, and Counter. Finally, we enforce the concept of Authenticated Encryption in the sense of data communication security of the wireless sensor network (WSN), Global Mobility Networks (GLOMONET), and Cloud Computing environment, where only encryption can guarantee both the privacy and integrity in a single pass with the assurance of reasonable computational overhead. Copyright © 2015 John Wiley & Sons, Ltd.

Interpreting information security culture: An organizational transformation case study

When two companies merge, technical infrastructures change, formal security policies get rewritten, and normative structures clash. The resultant changes typically disrupt the prevalent security culture, thus making the new organization highly vulnerable. Literature in this area has been rather scant, and there is a lack of empirical studies. In this paper, we use Hall's (1959) theory of cultural message streams to evaluate disruptions in security culture following a merger. We carry out an extensive case study of a telecom firm. Data were collected whilst the merger was taking place, which allowed us to evaluate the changing structures in real time. Findings from our analysis will be beneficial for researchers and practitioners alike. For researchers, it provides an opportunity to theorize about security culture formulation during a merger. At a practical level, decision makers will find this analysis useful for engaging in strategic security planning.