IEEE Online
PrePrint: Research on iOS Data Recovery Rate using Low Level NAND Image
Security & Privacy - Tue, 05/21/2013 - 11:29
This paper presents a method of iOS data recovery by extracting data image directly from low level NAND storage and analyzing the redundancy caused by its FTL behavior. An on-device brute-force method is adopted to address the passcode encryption issue which is identified as a block on current iOS forensic procedure. Further analysis on Garbage Collection Strategy adopted by iOS devices could provide certain guidance to iOS data recovery personnel.
Categories: IEEE Online
PrePrint: Analysis of Safety-Critical Computer Failures in Medical Devices
Security & Privacy - Tue, 05/21/2013 - 11:29
Incidents due to malfunctioning medical devices are a major cause of serious injury and death in the United States. During 2006–2011, 5,294 recalls and around 1.2 million adverse events were reported to the U.S. Food and Drug Administration (FDA). Almost 23% of these recalls were due to computer-related failures, of which around 94% presented medium-to-high risk of severe health consequences (such as serious injury or death) to patients. This paper investigates the causes of failures in computer-based medical devices and their impact on patients, by analyzing human-written descriptions of recalls and adverse event reports, obtained from public FDA databases. We characterize computer-related failures by deriving fault classes, failure modes, recovery actions, and number of devices affected by the recalls. This analysis is used as a basis for identifying safety issues in life-critical medical devices and providing insights on the future challenges in the design of safety-critical medical devices.
Categories: IEEE Online
PrePrint: Two tales of privacy in online social networks
Security & Privacy - Tue, 05/21/2013 - 11:29
Privacy is one of the friction points that emerges when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the 'OSN privacy problem' as one of surveillance, institutional or social privacy. In tackling these problems they have also treated them as if they were independent. We argue that the different privacy problems are entangled and that research on privacy in OSNs would benefit from a more holistic approach. In this article, we first provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions, goals and methods. We then juxtapose the differences between these two approaches in order to understand their complementarity, and to identify potential integration challenges as well as research questions that so far have been left unanswered.
Categories: IEEE Online
PrePrint: Anonymous and Distributed Community Cyber Incident Detection
Security & Privacy - Tue, 05/21/2013 - 11:29
Communities are under attack from a variety of threat agents. The repercussions from these attacks will grow more severe as communities become increasingly reliant upon cyberspace. Communities must be prepared to prevent, detect, respond to, and recover from a wide variety of cyber incidents. The timely and useful detection of cyber attacks is a first step towards a fast and effective response and recovery. Centralized community cyber incident detection scales poorly. Additionally, community members are understandably hesitant to share sensitive security information. Anonymity is vital to protecting the privacy of participants, and thereby encouraging their participation. We present a useful community cyber incident detection framework based upon an anonymous, distributed, and scalable information sharing architecture.
Categories: IEEE Online
PrePrint: Must social networking conflict with privacy?
Security & Privacy - Tue, 05/21/2013 - 11:29
People often assume that to use online social networks is to give up on privacy. This assumption is sometimes justified by the cynical observation that "You're not the customer... you're the commodity." This business model is taken for granted, but does it have to be so? Must online social networks be incompatible with privacy, or is this just the way it is today? In this article, we investigate regions of the social networking design space that have largely been left unexplored because of a premature committment to particular performance--price--privacy trade-offs. We demonstrate that it is possible to build systems with different trade-offs, that require less trust from users and give users more control without necessarily sacrificing performance. These new trade-offs require the relaxation of built-in assumptions about cost, but they demonstrate that today's most popular business model is just one among many: privacy is not inherently incompatible with social networking.
Categories: IEEE Online
PrePrint: "All the Better to See You with, My Dear": Facial Recognition and Privacy in Online Social Networks
Security & Privacy - Tue, 05/21/2013 - 11:29
Focusing primarily on popular online social networks like Facebook, this article provides an overview of the main social and legal challenges attending the use of facial recognition technologies on these platforms and explores ways of governing the associated privacy implications, specifically from a European data protection perspective. It then discusses potential legal, technological, and business model responses to these developments.
Categories: IEEE Online
PrePrint: Twitsper: Tweeting Privately
Security & Privacy - Tue, 05/21/2013 - 11:29
While most OSNs today provide some form of privacy controls so that their users can protect their shared content from other users, these controls are typically not sufficiently expressive and/or do not provide fine-grained protection.In this article, we introduce Twitsper, which allows users to have fine-grained control over who sees their messages. Specifically, we demonstrate that such a privacy control can be offered to users of Twitter today without having to wait for Twitter to make changes. We do so by designing and implementing Twitsper as a wrapper around Twitter that enables private group communication while preserving Twitter’s commercial interests. Our design preserves the privacy of group information (i.e., who communicates with whom) both from the Twitsper server as well as from undesired Twitsper users. Our Twitsper client for Android-based devices has been downloaded by over 1000 users and its utility has been noted by several media articles.
Categories: IEEE Online
PrePrint: The Personal Data Store Approach to Personal Data Security
Security & Privacy - Tue, 05/21/2013 - 11:29
Personal Data Stores (PDS) are considered by a growing number of actors to be the solution to the issue of online privacy. The PDS promise is that people can choose to share or restrict access to specific personal information with other interested parties. Ascertaining the extent to which users are willing to adopt PDS was the objective of a small-scale test involving job applicants and employers. After describing the context leading to the PDS solution developed within the European Framework 7 project TAS3, this paper explores whether PDS are a practical solution to addressing personal data insecurity on the web. Can PDSs respond to actual user needs? Are users ready to adopt PDS technology to ‘claim data back’? To what extent can PDS really enforce online privacy? What other approaches are emerging as alternatives to PDS?
Categories: IEEE Online
PrePrint: Mitigating XML Injection Zero-Day Attack through Strategy-based Detection System
Security & Privacy - Tue, 05/21/2013 - 11:29
WEB services have increasingly been adopted nowadays and therefore been targeted by attackers. The underlying technologies used by them bring known vulnerabilities to this new environment. The classical approach for attack detection either produce high false positive detection rates or cannot detect attack variations − leading to zero-day attacks. This paper applies ontology to build a strategy-based knowledge attack database. It is a novel hybrid attack detection engine, bringing together the main advantages of signature and knowledge-based classical approaches. Moreover, it is capable of mitigating zero-day attacks for XML injection, with no false positive detection rate.
Categories: IEEE Online
IEEE Security and Privacy -
Security & Privacy - Tue, 05/21/2013 - 11:28
IEEE Security and Privacy
Categories: IEEE Online
PrePrint: Bird Flu Outbreak Prediction via Satellite Tracking
Intelligent Systems - Tue, 05/21/2013 - 11:28
Advanced satellite tracking technologies have collected huge amounts of wild birds’ migration data. These data are very useful for biologists to understand birds’ dynamic migration patterns, to study correlations between the habitats, and to predict global spread trends of avian influenza. We transform the biological problem into a machine learning problem by converting the migratory paths of wild birds into graphs. Our first step of H5N1 outbreak prediction is to discover weighted closed cliques from the graphs by our mining algorithm HELEN (short for High-wEight cLosed cliquE miNing), which are then used by our learning algorithm HELEN-p to predict potential H5N1 outbreaks at habitats. We show that the prediction is more accurate in comparison with that by the traditional method on a migration data set obtained through a real satellite bird-tracking system. It is also confirmed by our empirical analysis that H5N1 spreads in a manner of high-weight closed cliques and frequent cliques.
Categories: IEEE Online
PrePrint: A Language Model Approach for Retrieving Product Features and Opinions from Customer Reviews
Intelligent Systems - Tue, 05/21/2013 - 11:28
In this paper, we introduce a new methodology for the retrieval of product features and opinions from a collection of free-text customer reviews about a product or service. The proposal relies on a language modeling framework that can be applied to reviews in any domain and language provided with a minimal knowledge source of sentiments or opinions (e.g., a seed set of opinion words). The methodology combines both a kernel- based model of opinion words (learned from the knowledge source of sentiments or opinions) and a statistical mapping between words to approximate a model of product features from which the retrieval is carried out. To validate the usefulness of the proposal, we carried out experiments over several collections of customer reviews about products from different industry domains and languages (specifically, English and Spanish). We also compare the obtained results on the retrieval of product features to closely related work on extracting product features from customer reviews.
Categories: IEEE Online
PrePrint: Developing corpora for sentiment analysis and opinion mining: the case of irony and Senti-TUT
Intelligent Systems - Tue, 05/21/2013 - 11:28
In recent years several efforts were devoted to automatically mining opinions and sentiments from natural language in social media messages, news and commercial product reviews. Since this task involves a deep understanding of the explicit and implicit information conveyed by the language, most of the approaches refer to annotated corpora. However, the development of this kind of resource raises several new challenges due both to the specificity of the data from such domains and text genres, and to the knowledge to be annotated. This paper focusses on the main issues related to the development of a corpus for opinion and sentiment analysis, with a special attention to irony, and presents as a case study Senti-TUT, an ongoing project for Italian aimed at investigating sentiment and irony about politics in social media. We introduce and analyze the Senti-TUT corpus, a collection of texts from Twitter annotated morpho-syntactically and with sentiment polarity. We describe the dataset, the annotation, the methodologies applied and our investigations on two important features of irony: polarity reversing and emotion expressions.
Categories: IEEE Online
PrePrint: Optimal Design and Control of Smart Space Structures: A Memetic Evolution Approach
Intelligent Systems - Tue, 05/21/2013 - 11:28
Optimal design and control of smart space structures are computational intensive. In traditional design methods, numbers of sensors and actuators required, their positions, and parameters of controllers are selected and optimized sequentially. Hence, only local optimality can be achieved by these methods. In order to reach the global optimal performance, a new approach is proposed in this article that will implement the concurrent design for smart space structures. In this approach, the quantity and placement of sensors/actuators and parameters of controllers are simultaneously optimized by a memetic evolutionary algorithm. A solar array smart structure has been used for computational experiments and the corresponding results indicate that the proposed concurrent design can obtain better performance than the sequential one.
Categories: IEEE Online
PrePrint: Multimodal Sentiment Analysis of Spanish Online Videos
Intelligent Systems - Tue, 05/21/2013 - 11:28
The number of videos available online and elsewhere is continuously growing, and with this the need for effective methods to process the vast amount of multimodal information shared through this media. This paper addresses the task of multimodal sentiment analysis, and presents a method that integrates linguistic, audio, and visual features for the purpose of identifying sentiment in online videos. We focus our experiments on a new dataset consisting of Spanish videos collected from the social media website YouTube and annotated for sentiment polarity. Through comparative experiments, we show that the joint use of visual, audio, and textual features greatly improves over the use of only one modality at a time. Moreover, we also test the portability of our multimodal method, and run evaluations on a second dataset of English videos.
Categories: IEEE Online
PrePrint: Summarizing On-line Product and Service Reviews Using Aspect Rating Distributions and Language Modeling
Intelligent Systems - Tue, 05/21/2013 - 11:28
Reviews about products and services are abundantly available online. However, selecting information relevant to a potential buyer involves a significant amount of time reading user's reviews and weeding out comments unrelated to the important aspects of the reviewed entity. In this work, we present Starlet, a novel approach to extractive multi-document summarization for evaluative text that considers aspect rating distributions and language modeling as summarization features. These features encourage the inclusion of sentences in the summary that preserve the overall opinion distribution expressed across the original reviews and whose language best reflects the language of reviews. We demonstrate how this method offers improvements over traditional summarization techniques and other approaches to multi-document summarization of evaluative text.
Categories: IEEE Online
PrePrint: An Intelligent System for Large-scale Disaster Behavior Analysis and Reasoning
Intelligent Systems - Tue, 05/21/2013 - 11:28
Most severe disasters cause large human population movements and evacuations. Understanding and predicting these movements is critical for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we present an intelligent system called DBAPRS for analyzing and simulating of human evacuation behaviors during large-scale disasters in Japan. DBAPRS stores the GPS records from mobile devices used by approximately 1.6 million people throughout Japan from 1 August 2010 to 31 July 2011. By mining this enormous set of Auto-GPS mobile sensor data, the short-term and long-term evacuation behaviors during the Great East Japan Earthquake and the Fukushima nuclear accident throughout the whole country are able to be automatically discovered and analyzed. Meanwhile, DBAPRS utilizes the discovered evacuations to effectively learn a probabilistic model to better understand and simulate human mobility during the disasters. Based on the training model, population mobility in various cities impacted by the disasters throughout Japan is able to be automatically simulated or predicted. On the basis of such kind of intelligent system, it is easy for us to find some new features or population mobility patterns after the recent severe earthquake, tsunami and release of radioactivity in Japan, which are likely to play a vital role in future disaster relief and management worldwide.
Categories: IEEE Online
PrePrint: YouTube Movie Reviews: In, Cross, and Open-domain Sentiment Analysis in an Audiovisual Context
Intelligent Systems - Tue, 05/21/2013 - 11:28
In this contribution we focus on the task of automatically analyzing a speaker's sentiment in on-line videos containing movie reviews. In addition to textual information, we consider adding audio features as typically used in speech-based emotion recognition as well as video features encoding valuable valence information conveyed by the speaker. We combine this multi-modal experimental setup with a detailed analysis of different methods for linguistic sentiment analysis by gradually increasing the level of domain-independence: First, we consider in-domain analysis by examining a cross-validation setup applied on a novel database named Multi-Modal Movie Opinion (ICT-MMMO) corpus. Next, we concentrate on cross-domain analysis by using a large corpus of written movie reviews for training. Finally, we explore the application of on-line knowledge sources for inferring the speaker's sentiment. Our experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyses spoken movie review videos and that language-independent audiovisual analysis can compete with linguistic analysis.
Categories: IEEE Online
PrePrint: Co-Transfer Learning Using Coupled Markov Chains with Restart
Intelligent Systems - Tue, 05/21/2013 - 11:28
This paper studies a machine learning strategy called co-transfer learning. Unlike many previous transfer learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. Our idea is to model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed by using the intra-relationships based on affinity metric among instances in the same space, and the inter-relationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multi-class image-text and English-Spanish-French classification data sets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.
Categories: IEEE Online
PrePrint: Using Shared Procedural Knowledge for Virtual Collaboration Support in Emergency Management
Intelligent Systems - Tue, 05/21/2013 - 11:28
This paper describes a framework that allows the collaborative development and deployment of procedural knowledge for task support in emergency situations. In this framework, procedural knowledge is represented in a wiki using an informal, textual description that is marked up with formal tags based on the <I-N-C-A> representation for hierarchical task networks used in AI planning. Procedural knowledge in the wiki can be used for task support by way of enhanced browsing facilities and the planning capabilities of an HTN planner. The latter supports the automatic composition of procedures to form plans for specific tasks. The tight integration of collaborative editing with deployment is new in this system and advances knowledge engineering for planning domain knowledge, that is, procedural knowledge. An experimental evaluation has shown that the explicit availability of procedural knowledge in emergency situations can reduce procedural uncertainty.
Categories: IEEE Online
