scholarly journals An Efficient and Packing-Resilient Two-Phase Android Cloned Application Detection Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Fang Lyu ◽  
Yaping Lin ◽  
Junfeng Yang
Keyword(s):  
Real World ◽  
Large Scale ◽  
Detection System ◽  
Malicious Code ◽  
Security And Privacy ◽  
Two Phase ◽  
Android Malware ◽  
Detection Approach ◽  
Application Industry ◽  
Selection Phase

The huge benefit of mobile application industry has attracted a large number of developers and attendant attackers. Application repackaging provides help for the distribution of most Android malware. It is a serious threat to the entire Android ecosystem, as it not only compromises the security and privacy of the app users but also plunders app developers’ income. Although massive approaches have been proposed to address this issue, plagiarists try to fight back through packing their malicious code with the help of commercial packers. Previous works either do not consider the packing issue or rely on time-consuming computations, which are not scalable for large-scale real-world scenario. In this paper, we propose FUIDroid, a novel two-phase app clones detection system that can detect the packed cloned app. FUIDroid includes a function-based fast selection phase to quickly select suspicious apps by analyzing apps’ description and a further UI-based accurate detection phase to refine the detection result. We evaluate our system on two sets of apps. The result from experiment on 320 packed samples demonstrates that FUIDroid is resilient to packed apps. The evaluation on more than 150,000 real-world apps shows the efficiency of FUIDroid in large-scale scenario.

scholarly journals A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

2019 ◽  
Vol 9 (15) ◽  
pp. 3141
Author(s):  
Li Bai ◽  
Mi Hu ◽  
Yunlong Ma ◽  
Min Liu
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Two Phase ◽  
Group Buying ◽  
Large Scale Data ◽  
User Clustering ◽  
Scale Data

The last two decades have witnessed an explosive growth of e-commerce applications. Existing online recommendation systems for e-commerce applications, particularly group-buying applications, suffer from scalability and data sparsity problems when confronted with exponentially increasing large-scale data. This leads to a poor recommendation effect of traditional collaborative filtering (CF) methods in group-buying applications. In order to address this challenge, this paper proposes a hybrid two-phase recommendation (HTPR) method which consists of offline preparation and online recommendation, combining clustering and collaborative filtering techniques. The user-item category tendency matrix is constructed after clustering items, and then users are clustered to facilitate personalized recommendation where items are generated by collaborative filtering technology. In addition, a parallelized strategy was developed to optimize the recommendation process. Extensive experiments on a real-world dataset were conducted by comparing HTPR with other three recommendation methods: traditional CF, user-clustering based CF, and item-clustering based CF. The experimental results show that the proposed HTPR method is effective and can improve the accuracy of online recommendation systems for group-buying applications.

Optical detection of alpha emitting radionuclides in the environment

2021 ◽  
Author(s):  
Faton Krasniqi
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Detection System ◽  
Optical Detection ◽  
Alpha Particles ◽  
Remote Detection ◽  
Incident Management ◽  
Detection Approach ◽  
Significant Damage

<p>Radiological emergencies involving accidental or deliberate dispersion of alpha emitting radionuclides in the environment can cause significant damage to humans and societies in general. A detection system to measure large-scale contamination of these radionuclides is currently not available. In case of a contamination, the only option is to evacuate the population from the affected areas and then run diagnostics by hand due to the short range of alpha particles in air, exposing thus the emergency teams to considerable risk. Even then, the results of emergency field applications are notoriously ambiguous, time consuming and tedious due to the centimetre range of the alpha particles in air. A novel detection approach which is capable of remote detection of alpha-emitting radionuclides in the environment will be reported. This approach will assist the on-site incident management and will enable detection of contamination threats without contact—from safe distances—avoiding thus contamination of operators and equipment.</p>

scholarly journals Intelligent Botnet Detection Approach in Modern Applications

2021 ◽  
Vol 15 (16) ◽  
pp. 113
Author(s):  
Khattab M. Ali Alheeti ◽  
Ibrahim Alsukayti ◽  
Mohammed Alreshoodi
Keyword(s):  
Detection System ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Learning Networks ◽  
Detection Systems ◽  
Testing Dataset ◽  
Detection Approach ◽  
Secure Environment ◽  
Iot Devices ◽  
The Internet Of Things

<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>

scholarly journals An Optimized CNN based Real World Anomaly Detection in Surveillance Videos

2019 ◽  
Vol 8 (9S) ◽  
pp. 465-473
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Anomaly Detection ◽  
Real World ◽  
Large Scale ◽  
Detection System ◽  
Adaptive Genetic Algorithm ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Anomaly Detection System

Anomaly detection in automated surveillance video is an extremely monotonous process for monitoring for crowded scenes and surveillance videos are capable to incarcerate a mixture of sensible anomalies. An appropriate machine learning technique can help to train the Anomaly Detection System (ADS) in identifying anomalous activities during surveillance. To this end, we present an anomaly detection system that can be used as a tool for anomaly detection in surveillance videos using the concept of artificial intelligence. The main intention of the proposed anomaly detection system is to improve the detection time and accuracy by using the concept of Convolutional Neural Network (CNN) as artificial intelligence technique. In this paper we present a CNN based Anomaly Detection System (CNN-ADS), which is the combination of multiple layer of hidden unit with the optimized MSER feature by using Genetic Algorithm (GA). Here CNN is used for classifying the activity into normal and abnormal from the surveillance videos based on the fitness function of GA which is used for the selection of optimal MSER feature sets. Further, Self adaptive genetic algorithm (SAGA) is adopted to efficiently solve optimization problems in the continuous search domain to select the best possible feature to segregate the pattern of normal and abnormal activities. The main contribution of this research is validation of proposed system for the large scale data and we introduce a new large-scale dataset of 128 hours of videos. Dataset consists of 1900 long and untrimmed real-world surveillance videos, with 13 sensible anomalies such as road accident, burglary, fighting, robbery, etc. as well as normal activities. The experimental results of the planned system show that our CNN-ADS for anomaly detection achieve essential improvement on anomaly detection presentation as compared to the state-of-the-art approaches. The dataset is available at: https://webpages.uncc.edu/cchen62/dataset.html. In this paper, to validate the proposed ADS we provide the comparison of existing results of several recent deep learning baselines on anomalous activity detection. The real-time ADS in surveillance video sequences using SAGA based CNN with MSER feature extraction technique is implemented using Image Processing Toolbox within Matlab Software.

scholarly journals Using a commercially available app for self-management of hypertension: Acceptance and usability study in Saudi Arabia (Preprint)

2020 ◽  
Author(s):  
Tourkiah Alessa ◽  
Mark S Hawley ◽  
Nouf Alsulamy ◽  
Luc de Witte
Keyword(s):  
Real World ◽  
Large Scale ◽  
The Self ◽  
Security And Privacy ◽  
Self Management ◽  
Usability Study ◽  
Gulf Region ◽  
Usability Test ◽  
App Development ◽  
Study Participants

BACKGROUND The use of smartphone apps to assist in the self-management of hypertension is becoming increasingly common, but very few commercially available apps have the potential to be with adequate security and privacy safeguards and effective. In a previous study, we identified 5 apps that are potentially effective and safe, and, based on the preferences of doctors and patients, one (Cora Health) was selected as most suitable for use in a Saudi context. However, there is currently no evidence on its usability and acceptance among potential users. Indeed, there has been very little research into usability and acceptance of hypertension apps in general, and even less that considers the Gulf Region. OBJECTIVE To evaluate the acceptance and usability of the selected app in the Saudi context. METHODS This research used a mixed-methods approach with two studies: 1) a usability test involving patients in a controlled setting performing predefined tasks; and 2) a real-world usability study where patients used the app for four weeks. In the usability test, participants were asked to think aloud while performing the tasks, and an observer recorded how many tasks they completed. At the end of the real-world pilot study, participants were interviewed and the mHealth App Usability Questionnaire (MAUQ) was completed. Descriptive statistics were used to analyze quantitative data and thematic analysis was used to analyze qualitative data RESULTS A total of 10 patients completed study 1. The study found that app usability was moderate and participants needed some familiarization time before they could use the app proficiently. Some usability issues were revealed, related to app accessibility, navigation, etc. and a few tasks remained uncompleted by most people. Twenty patients completed study 2, with a mean age of 51.6. Study 2 found that the app was generally acceptable and easy to use, with some similar usability issues identified. Participants stressed the importance of practice and training to use it more easily and proficiently. Participants had a good engagement level with 48% retention at the end of study 2, with most participants’ engagement being classed as meaningful. The most recorded data was BP, followed by stress and medication, and the most accessed feature was viewing graphs of data trends. CONCLUSIONS This study showed that a commercially available app can be usable and acceptable in the self-management of hypertension, but also found a considerable number of possibilities for improvement, which need to be considered in future app development. The results show there is potential for a commercially-available app to be used in large-scale studies of hypertension self-management if suggestions for improvements are addressed. CLINICALTRIAL

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

scholarly journals Thermoelectric Properties of Thin Films from Sorted Single-Walled Carbon Nanotubes

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3808 ◽  
Author(s):  
Blazej Podlesny ◽  
Bogumila Kumanek ◽  
Angana Borah ◽  
Ryohei Yamaguchi ◽  
Tomohiro Shiraki ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Thermoelectric Properties ◽  
Large Scale ◽  
Single Walled Carbon Nanotubes ◽  
Parent Material ◽  
Free Standing ◽  
Two Phase ◽  
Single Walled Carbon ◽  
Walled Carbon Nanotubes ◽  
Aqueous Two Phase Extraction

Single-walled carbon nanotubes (SWCNTs) remain one of the most promising materials of our times. One of the goals is to implement semiconducting and metallic SWCNTs in photonics and microelectronics, respectively. In this work, we demonstrated how such materials could be obtained from the parent material by using the aqueous two-phase extraction method (ATPE) at a large scale. We also developed a dedicated process on how to harvest the SWCNTs from the polymer matrices used to form the biphasic system. The technique is beneficial as it isolates SWCNTs with high purity while simultaneously maintaining their surface intact. To validate the utility of the metallic and semiconducting SWCNTs obtained this way, we transformed them into thin free-standing films and characterized their thermoelectric properties.

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