scholarly journals LP-SBA-XACML. (c2019)

2019 ◽  
Author(s):  
◽  
Mohamad A. Chehab
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Smart Devices ◽  
Time Behavior ◽  
User Privacy ◽  
Sensitive Data ◽  
Private Data ◽  
Personalized Services ◽  
Protection Mechanisms ◽  
Iot Devices

The wide applicability of Internet of Things (IoT) would truly enable the pervasiveness of smart devices for sensing data. IoT coupled with machine learning would enter us in an era of smart and personalized, services. In order to achieve service personalization, there is a need to collect sensitive data about the users. That yields to privacy concerns due to the possibility of abusing the data or having attackers to gain unauthorized access. Moreover, the nature of IoT devices, being resource and computationally constrained, makes it di cult to perform heavy protection mechanisms. Despite the presence of several solutions for protecting user privacy, they were not created for the purpose of running on small devices at a large scale. On top of that, existing solutions lack the customization of user privacy in which users have little to no control over their own private data. In this regards, we address the aforementioned issue of protecting user's privacy while taking into account e ciency as well as memory usage. The proposed scheme embeds an e cient and lightweight algebra based that targets user privacy and provides e cient policy evaluation. Moreover, an intelligent model to customize user's privacy based on real time behavior is integrated. Experiments conducted on synthetic and real-life scenarios to demonstrate the feasibility and relevance of our proposed framework within IoT environment.

scholarly journals A Comprehensive Assessment on IOT Devices with Data Mining Techniques

2021 ◽  
Author(s):  
G. Vijay Kumar ◽  
M. Sreedevi ◽  
Arvind Yadav ◽  
B. Aruna
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Comprehensive Assessment ◽  
Smart Devices ◽  
Heterogeneous Environments ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Entire World ◽  
Iot Devices ◽  
Cloud Technologies

Now at present development the entire world using vast variety of smart devices associated among sensors & handful of actuators. There is an enormous progress within the field of electronic communication; processing the data through devices and the bandwidth in internet technologies makes very easy to access and to interact with the variety of devices all over the whole world. There is a wide range research in the area of Internet of Things (IoT) along Cloud Technologies making to build incredible data which are creating from this type of heterogeneous environments and can be able to transform into a valuable knowledge with the help of data mining techniques. The knowledge that is generated will takes a crucial role in making intellectual decisions and also be a best possible resource management and services. In this paper we organized a comprehensive assessment on various data mining techniques engaged with small and large scale IoT applications to make the environment smart.

scholarly journals Towards Time-Sensitive and Verifiable Data Aggregation for Mobile Crowdsensing

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tao Zhang ◽  
Xiongfei Song ◽  
Lele Zheng ◽  
Yani Han ◽  
Kai Zhang ◽  
...  
Keyword(s):  
Access Control ◽  
Data Aggregation ◽  
Large Scale ◽  
Response Speed ◽  
Aggregated Data ◽  
Mobile Crowdsensing ◽  
Time Sensitivity ◽  
Attribute Based Encryption ◽  
Personalized Services ◽  
Iot Devices

Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.

scholarly journals A Forensic Analysis of Home Automation Devices (FAHAD) Model: Kasa Smart Light Bulb and Eufy Floodlight Camera as Case Studies

2021 ◽  
Vol 1 (1-3) ◽  
pp. 18-26
Author(s):  
Fahad E. Salamh
Keyword(s):  
Forensic Analysis ◽  
Sensor Data ◽  
Smart Devices ◽  
Sensitive Information ◽  
Home Automation ◽  
Security Assessment ◽  
Light Bulb ◽  
Sensitive Data ◽  
Data Set ◽  
Iot Devices

The adoption of Internet of Things (IoT) devices is rapidly increasing with the advancement of network technology, these devices carry sensitive data that require adherence to minimum security practices. The adoption of smart devices to migrate homeowners from traditional homes to smart homes has been noticeable. These smart devices share value with and are of potential interest to digital forensic investigators, as well. Therefore, in this paper, we conduct comprehensive security and forensic analysis to contribute to both fields—targeting a security enhancement of the selected IoT devices and assisting the current IoT forensics approaches. Our work follows several techniques such as forensic analysis of identifiable information, including connected devices and sensor data. Furthermore, we perform security assessment exploring insecure communication protocols, plain text credentials, and sensitive information. This will include reverse engineering some binary files and manual analysis techniques. The analysis includes a data-set of home automation devices provided by the VTO labs: (1) the eufy floodlight camera, and (2) the Kasa smart light bulb. The main goal of the technical experiment in this research is to support the proposed model.

scholarly journals A Survey on Access Control in the Age of IoT

2018 ◽  
Author(s):  
Jing Qiu ◽  
Chunlai Du ◽  
Shen Su ◽  
Qi Zuo ◽  
Zhihong Tian
Keyword(s):  
Access Control ◽  
Social Relations ◽  
Large Scale ◽  
Control Mechanism ◽  
Real Life ◽  
Information Resources ◽  
Personal Health Information ◽  
Accurate Information ◽  
User Privacy ◽  
Access Control Mechanism

With the development of IoT technology, various information resources, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance and other forms of IoT information Service model gradually change people's daily life. Facing the vast amounts of IoT information data, IoT search technology is used to quickly find accurate information to meet real-time search needs of users. However, IoT search requires to use a large number of user privacy Information, such as personal health information, location information, social relations information, to provide personalized services. User privacy information will meet security problems if an effective access control mechanism is missing during the IoT search process. Access control mechanism can effectively monitor the access activities of resources, and ensure authorized users to access information resources under legitimate conditions. This survey examines the growing literature on access control for IoT search. Problems and challenges of access control mechanism are analyzed to facilitate the adoption of access control solutions in real-life settings. This paper aims to provides theoretical, methodological and technical guidance for IoT search access control mechanism in large-scale dynamic heterogeneous environment. Based on the literature study, we also analyzed future development direction of access control in the age of IoT.

scholarly journals To Permit or Not to Permit, That is the Usability Question: Crowdsourcing Mobile Apps’ Privacy Permission Settings

2017 ◽  
Vol 2017 (4) ◽  
pp. 119-137 ◽  
Author(s):  
Qatrunnada Ismail ◽  
Tousif Ahmed ◽  
Kelly Caine ◽  
Apu Kapadia ◽  
Michael Reiter
Keyword(s):  
Large Scale ◽  
Previous Analysis ◽  
Mobile Apps ◽  
User Testing ◽  
User Privacy ◽  
Sensitive Data ◽  
Contact Information ◽  
The Impact ◽  
User Experiment ◽  
Insight Into

Abstract Millions of apps available to smartphone owners request various permissions to resources on the devices including sensitive data such as location and contact information. Disabling permissions for sensitive resources could improve privacy but can also impact the usability of apps in ways users may not be able to predict. We study an efficient approach that ascertains the impact of disabling permissions on the usability of apps through large-scale, crowdsourced user testing with the ultimate goal of making recommendations to users about which permissions can be disabled for improved privacy without sacrificing usability. We replicate and significantly extend previous analysis that showed the promise of a crowdsourcing approach where crowd workers test and report back on various configurations of an app. Through a large, between-subjects user experiment, our work provides insight into the impact of removing permissions within and across different apps (our participants tested three apps: Facebook Messenger (N=218), Instagram (N=227), and Twitter (N=110)). We study the impact of removing various permissions within and across apps, and we discover that it is possible to increase user privacy by disabling app permissions while also maintaining app usability.

scholarly journals Enhancing Security on IoT Devices via Machine Learning on Conditional Power Dissipation

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1799
Author(s):  
Dimitrios Myridakis ◽  
Stefanos Papafotikas ◽  
Konstantinos Kalovrektis ◽  
Athanasios Kakarountas
Keyword(s):  
Machine Learning ◽  
Power Dissipation ◽  
Low Cost ◽  
Rapid Development ◽  
Abnormal Behavior ◽  
Smart Devices ◽  
Side Channel ◽  
Sensitive Data ◽  
Wide Range ◽  
Iot Devices

The rapid development of connected devices and the sensitive data, which they produce, is a major challenge for manufacturers seeking to fully protect their devices from attack. Consumers expect their IoT devices and data to be adequately protected against a wide range of vulnerabilities and exploits. Successful attacks target IoT devices, cause security problems, and pose new challenges. Successful attacks from botnets residing on mastered IoT devices increase significantly in number and the severity of the damage they cause is similar to that of a war. The characteristics of attacks vary widely from attack to attack and from time to time. The warnings about the severity of the attacks indicate that there is a need for solutions to address the attacks from birth. In addition, there is a need to quarantine infected IoT devices, preventing the spread of the virus and thus the formation of the botnet. This work introduces the exploitation of side-channel attack techniques to protect the low-cost smart devices intuitively, and integrates a machine learning-based algorithm for Intrusion Detection, exploiting current supply characteristic dissipation. The results of this work showed successful detection of abnormal behavior of smart IoT devices.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

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