Smartphone User Privacy Preserving through Crowdsourcing

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary private data breach. However, the majority of regular users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose a permission control framework based on crowdsourcing. At its core, our framework runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or not the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using a transitional Bayesian inference model. The recommendation is based on aggregated expert responses and their confidence level. As a complete framework design of the system, this thesis also includes a solution for Android app risks estimation based on behaviour analysis. To eliminate the negative impact from dishonest app owners, we also proposed a bot user detection to make it harder to utilize false recommendations through bot users to impact the overall recommendations. This work also covers a multi-view permission notification design to customize the app safety notification interface based on users\u27 need and an app recommendation method to suggest safe and usable alternative apps to users

A Survey on Query-based API Recommendation

Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks has gained increasing attention among researchers and developers in recent years. To comprehensively understand this research domain, we have surveyed to analyze API recommendation studies published in the last 10 years. Our study begins with an overview of the structure of API recommendation tools. Subsequently, we systematically analyze prior research and pose four key research questions. For RQ1, we examine the volume of published papers and the venues in which these papers appear within the API recommendation field. In RQ2, we categorize and summarize the prevalent data sources and collection methods employed in API recommendation research. In RQ3, we explore the types of data and common data representations utilized by API recommendation approaches. We also investigate the typical data extraction procedures and collection approaches employed by the existing approaches. RQ4 delves into the modeling techniques employed by API recommendation approaches, encompassing both statistical and deep learning models. Additionally, we compile an overview of the prevalent ranking strategies and evaluation metrics used for assessing API recommendation tools. Drawing from our survey findings, we identify current challenges in API recommendation research that warrant further exploration, along with potential avenues for future research

Advanced Topics in Systems Safety and Security

This book presents valuable research results in the challenging field of systems (cyber)security. It is a reprint of the Information (MDPI, Basel) - Special Issue (SI) on Advanced Topics in Systems Safety and Security. The competitive review process of MDPI journals guarantees the quality of the presented concepts and results. The SI comprises high-quality papers focused on cutting-edge research topics in cybersecurity of computer networks and industrial control systems. The contributions presented in this book are mainly the extended versions of selected papers presented at the 7th and the 8th editions of the International Workshop on Systems Safety and Security—IWSSS. These two editions took place in Romania in 2019 and respectively in 2020. In addition to the selected papers from IWSSS, the special issue includes other valuable and relevant contributions. The papers included in this reprint discuss various subjects ranging from cyberattack or criminal activities detection, evaluation of the attacker skills, modeling of the cyber-attacks, and mobile application security evaluation. Given this diversity of topics and the scientific level of papers, we consider this book a valuable reference for researchers in the security and safety of systems

‎An Artificial Intelligence Framework for Supporting Coarse-Grained Workload Classification in Complex Virtual Environments

Cloud-based machine learning tools for enhanced Big Data applications}‎, ‎where the main idea is that of predicting the ``\emph{next}'' \emph{workload} occurring against the target Cloud infrastructure via an innovative \emph{ensemble-based approach} that combines the effectiveness of different well-known \emph{classifiers} in order to enhance the whole accuracy of the final classification‎, ‎which is very relevant at now in the specific context of \emph{Big Data}‎. ‎The so-called \emph{workload categorization problem} plays a critical role in improving the efficiency and reliability of Cloud-based big data applications‎. ‎Implementation-wise‎, ‎our method proposes deploying Cloud entities that participate in the distributed classification approach on top of \emph{virtual machines}‎, ‎which represent classical ``commodity'' settings for Cloud-based big data applications‎. ‎Given a number of known reference workloads‎, ‎and an unknown workload‎, ‎in this paper we deal with the problem of finding the reference workload which is most similar to the unknown one‎. ‎The depicted scenario turns out to be useful in a plethora of modern information system applications‎. ‎We name this problem as \emph{coarse-grained workload classification}‎, ‎because‎, ‎instead of characterizing the unknown workload in terms of finer behaviors‎, ‎such as CPU‎, ‎memory‎, ‎disk‎, ‎or network intensive patterns‎, ‎we classify the whole unknown workload as one of the (possible) reference workloads‎. ‎Reference workloads represent a category of workloads that are relevant in a given applicative environment‎. ‎In particular‎, ‎we focus our attention on the classification problem described above in the special case represented by \emph{virtualized environments}‎. ‎Today‎, ‎\emph{Virtual Machines} (VMs) have become very popular because they offer important advantages to modern computing environments such as cloud computing or server farms‎. ‎In virtualization frameworks‎, ‎workload classification is very useful for accounting‎, ‎security reasons‎, ‎or user profiling‎. ‎Hence‎, ‎our research makes more sense in such environments‎, ‎and it turns out to be very useful in a special context like Cloud Computing‎, ‎which is emerging now‎. ‎In this respect‎, ‎our approach consists of running several machine learning-based classifiers of different workload models‎, ‎and then deriving the best classifier produced by the \emph{Dempster-Shafer Fusion}‎, ‎in order to magnify the accuracy of the final classification‎. ‎Experimental assessment and analysis clearly confirm the benefits derived from our classification framework‎. ‎The running programs which produce unknown workloads to be classified are treated in a similar way‎. ‎A fundamental aspect of this paper concerns the successful use of data fusion in workload classification‎. ‎Different types of metrics are in fact fused together using the Dempster-Shafer theory of evidence combination‎, ‎giving a classification accuracy of slightly less than $80\%$‎. ‎The acquisition of data from the running process‎, ‎the pre-processing algorithms‎, ‎and the workload classification are described in detail‎. ‎Various classical algorithms have been used for classification to classify the workloads‎, ‎and the results are compared‎

A Learning-based Approach to Exploiting Sensing Diversity in Performance Critical Sensor Networks

Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting and classifying events while maximizing system lifetime. to meet high accuracy requirements and maximize system lifetime, we must address sensing diversity: sensing capability differences among both heterogeneous and homogeneous sensors in a specific deployment. Existing approaches either ignore sensing diversity entirely and assume all sensors have similar capabilities or attempt to overcome sensing diversity through calibration. Instead, we use machine learning to take advantage of sensing differences among heterogeneous sensors to provide high accuracy and energy savings for performance critical applications.;In this dissertation, we provide five major contributions that exploit the nuances of specific sensor deployments to increase application performance. First, we demonstrate that by using machine learning for event detection, we can explore the sensing capability of a specific deployment and use only the most capable sensors to meet user accuracy requirements. Second, we expand our diversity exploiting approach to detect multiple events using a distributed manner. Third, we address sensing diversity in body sensor networks, providing a practical, user friendly solution for activity recognition. Fourth, we further increase accuracy and energy savings in body sensor networks by sharing sensing resources among neighboring body sensor networks. Lastly, we provide a learning-based approach for forwarding event detection decisions to data sinks in an environment with mobile sensor nodes

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Advanced Information Systems and Technologies

This book comprises the proceedings of the VI International Scientific Conference “Advanced Information Systems and Technologies, AIST-2018”. The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing, computer networking and telecomunications, modern methods and information technologies of sustainable development. They will be useful for students, graduate students, researchers who interested in computer science

Towards Adjusting Mobile Devices To User's Behaviour

Mobile devices are a special class of resource-constrained em- bedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained re- sources require extensive optimization of a mobile system compared to larger systems. Any needless operation has to be avoided. Time- consuming operations have to be started early on. For instance, load- ing files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system’s operation. Our goal is to ad- just such strategies on the basis of logged system data. Optimization is then achieved by predicting an application’s behavior based on facts learned from earlier runs on the same system. In this paper, we ana- lyze system-calls on operating system level and compare two paradigms, namely server-based and device-based learning. The results could be used to optimize the runtime behaviour of mobile devices