Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Program analysis for android security and reliability

The recent, widespread growth and adoption of mobile devices have revolutionized the way users interact with technology. As mobile apps have become increasingly prevalent, concerns regarding their security and reliability have gained significant attention. The ever-expanding mobile app ecosystem presents unique challenges in ensuring the protection of user data and maintaining app robustness. This dissertation expands the field of program analysis with techniques and abstractions tailored explicitly to enhancing Android security and reliability. This research introduces approaches for addressing critical issues related to sensitive information leakage, device and user fingerprinting, mobile medical score calculators, as well as termination-induced data loss. Through a series of comprehensive studies and employing novel approaches that combine static and dynamic analysis, this work provides valuable insights and practical solutions to the aforementioned challenges. In summary, this dissertation makes the following contributions: (1) precise identifier leak tracking via a novel algebraic representation of leak signatures, (2) identifier processing graphs (IPGs), an abstraction for extracting and subverting user-based and device-based fingerprinting schemes, (3) interval-based verification of medical score calculator correctness, and (4) identifying potential data losses caused by app termination

Fraud Patterns Classification: A study of Fraud in business Process of Indonesian Online Sales Transaction

Fraud detection has become an important research topic in recent years. In online sales transaction, fraud can occur on a business process. Fraud which occurs on business process is popularly known as process-based fraud (PBF). Previous studies have proposed PBF detection on process business model, however, false decisions are still often made because of new fraud pattern in online sales transactions. False decision mostly occurs since the method cannot identify the attributes of fraud in online sales transaction. This research proposes new fraud attributes and fraud patterns in online transactions. The attributes can be identified by exploring the event logs and Standard Operating Procedure (SOP) of online sales transactions. First, this is conducted by collecting event logs and creating SOP of online sales transaction; then, performing conformance between event logs and SOP; further, discussing with fraud experts about the result of SOP deviations which have been identified; moreover, determining convention value of the SOP deviation to fuzzy value, and classifying the SOP deviation; and at last, establishing fraud attributes and fraud patterns based on classification result. The new fraud attribute and fraud patterns are expected to increase accuracy of fraud detection in online sales transaction. Based on the evaluation, this method resulted a better accuracy 0.03 than the previous one

Raising awareness of smartphone overuse among university students: a persuasive systems approach

Smartphone overuse can lead to a series of physical, mental and social disturbances. This problem is more prevalent among young adults as compared to other demographic groups. Additionally, university students are already undergoing high cognitive loads and stress conditions; therefore, they are more susceptible to smartphone addiction and its derived problems. In this paper, we present a novel approach where a conversational mobile agent uses persuasive messages exploring the reflective mind to raise users’ awareness of their usage and consequently induce reduction behaviors. We conducted a four-week study with 16 university students undergoing stressful conditions—a global lockdown during their semester—and evaluated the impact of the agent on smartphone usage reduction and the perceived usefulness of such an approach. Results show the efficacy of self-tracking in the behavior change process: 81% of the users reduced their usage time, and all of them mentioned that having a conversational agent alerting them about their usage was useful. Before this experiment, only 68% of them considered such an approach could be useful. In conclusion, users deemed it essential to have an engaging conversational agent on their smartphones, in terms of helping them become more aware of usage times.info:eu-repo/semantics/publishedVersio

Vers une détection automatique des applications malveillantes dans les environnements Android

National audienceDans ce papier, nous présentons l'état de l'art sur les attaques et les menaces dans les environnements Android ainsi que les approches de détection associées. La plupart de ces approches utilisent des informations obtenues par instrumentation de la machine virtuelle ou par rétro-ingénierie du bytecode des applications. Nous proposons ainsi une nouvelle méthode moins coûteuse qui repose sur l'analyse des journaux des événements applicatifs et systèmes générés par la plate-forme Android. Cette analyse nous permettra d'établir des signatures des applications Android associant leurs structures et leurs comportements dynamiques

Permission-based Risk Signals for App Behaviour Characterization in Android Apps

With the parallel growth of the Android operating system and mobile malware, one of the ways to stay protected from mobile malware is by observing the permissions requested. However, without careful consideration of these permissions, users run the risk of an installed app being malware, without any warning that might characterize its nature. We propose a permission-based risk signal using a taxonomy of sensitive permissions. Firstly, we analyse the risk of an app based on the permissions it requests, using a permission sensitivity index computed from a risky permission set. Secondly, we evaluate permission mismatch by checking what an app requires against what it requests. Thirdly, we evaluate security rules based on our metrics to evaluate corresponding risks. We evaluate these factors using datasets of benign and malicious apps (43580 apps) and our result demonstrates that the proposed framework can be used to improve risk signalling of Android apps with a 95% accuracy

REVIEW AND ANALYSIS OF MACHINE LEARNING AND SOFT COMPUTING APPROACHES FOR USER MODELING

ABSTRAC

A Multi-Criteria-Based Evaluation of Android Applications

Android users can face the risk of downloading and installing bad applications on their devices. In fact, many applications may either hide malware, or their expected behavior do not fully follow the user\u27s expectation. This happens because, at install-time, even if the user is warned with the potential security threat of the application, she often skips this alert message. On Android this is due to the complexity of the permission system, which may be tricky to fully understand. We propose a multi-criteria evaluation of Android applications, to help the user to easily understand the trustworthiness degree of an application, both from a security and a functional side. We validate our approach by testing it on more than 180 real applications found either on official and unofficial markets

Cyberspace and Real-World Behavioral Relationships: Towards the Application of Internet Search Queries to Identify Individuals At-risk for Suicide

The Internet has become an integral and pervasive aspect of society. Not surprisingly, the growth of ecommerce has led to focused research on identifying relationships between user behavior in cyberspace and the real world - retailers are tracking items customers are viewing and purchasing in order to recommend additional products and to better direct advertising. As the relationship between online search patterns and real-world behavior becomes more understood, the practice is likely to expand to other applications. Indeed, Google Flu Trends has implemented an algorithm that accurately charts the relationship between the number of people searching for flu-related topics on the Internet, and the number of people who actually have flu symptoms in that region. Because the results are real-time, studies show Google Flu Trends estimates are typically two weeks ahead of the Center for Disease Control. The Air Force has devoted considerable resources to suicide awareness and prevention. Despite these efforts, suicide rates have remained largely unaffected. The Air Force Suicide Prevention Program assists family, friends, and co-workers of airmen in recognizing and discussing behavioral changes with at-risk individuals. Based on other successes in correlating behaviors in cyberspace and the real world, is it possible to leverage online activities to help identify individuals that exhibit suicidal or depression-related symptoms? This research explores the notion of using Internet search queries to classify individuals with common search patterns. Text mining was performed on user search histories for a one-month period from nine Air Force installations. The search histories were clustered based on search term probabilities, providing the ability to identify relationships between individuals searching for common terms. Analysis was then performed to identify relationships between individuals searching for key terms associated with suicide, anxiety, and post-traumatic stress