scholarly journals Permission-based Analysis of Android Applications Using Categorization and Deep Learning Scheme

2019 ◽  
Vol 255 ◽  
pp. 05005 ◽  
Author(s):  
Alimardani Hamidreza ◽  
Nazeh Mohammed
Keyword(s):  
Operating System ◽  
Deep Learning ◽  
Mobile Devices ◽  
True Positive Rate ◽  
Research Community ◽  
True Positive ◽  
Daily Lives ◽  
Breaking News ◽  
Positive Rate ◽  
Android Applications

As mobile devices grow in popularity, they have become indispensable in people's daily lives, keeping us connected to social networks, breaking news, and the entire Internet. While there are multiple competing platforms, Google's Android is currently the most popular operating system for mobile devices. This popularity has drawn attention of hackers as well. Thus far, research works have analysed Android permissions individually, which makes analysis complex and time consuming. In this work, we propose categorizing Android permissions based on Google's recommendation and perform LSTM analysis on data. The used datasets are Drebin and AndroZoo, which are the most complete and well-respected among research community. The experiment results show that LSTM achieved 91% of true positive rate.

scholarly journals Actor Critic Deep Reinforcement Learning for Neural Malware Control

2020 ◽  
Vol 34 (01) ◽  
pp. 1005-1012
Author(s):  
Yu Wang ◽  
Jack Stokes ◽  
Mady Marinescu
Keyword(s):  
Operating System ◽  
Reinforcement Learning ◽  
False Positive ◽  
Partial Information ◽  
False Positive Rate ◽  
True Positive Rate ◽  
True Positive ◽  
New Model ◽  
Positive Rate ◽  
Execution Speed

In addition to using signatures, antimalware products also detect malicious attacks by evaluating unknown files in an emulated environment, i.e. sandbox, prior to execution on a computer's native operating system. During emulation, a file cannot be scanned indefinitely, and antimalware engines often set the number of instructions to be executed based on a set of heuristics. These heuristics only make the decision of when to halt emulation using partial information leading to the execution of the file for either too many or too few instructions. Also this method is vulnerable if the attackers learn this set of heuristics. Recent research uses a deep reinforcement learning (DRL) model employing a Deep Q-Network (DQN) to learn when to halt the emulation of a file. In this paper, we propose a new DRL-based system which instead employs a modified actor critic (AC) framework for the emulation halting task. This AC model dynamically predicts the best time to halt the file's execution based on a sequence of system API calls. Compared to the earlier models, the new model is capable of handling adversarial attacks by simulating their behaviors using the critic model. The new AC model demonstrates much better performance than both the DQN model and antimalware engine's heuristics. In terms of execution speed (evaluated by the halting decision), the new model halts the execution of unknown files by up to 2.5% earlier than the DQN model and 93.6% earlier than the heuristics. For the task of detecting malicious files, the proposed AC model increases the true positive rate by 9.9% from 69.5% to 76.4% at a false positive rate of 1% compared to the DQN model, and by 83.4% from 41.2% to 76.4% at a false positive rate of 1% compared to a recently proposed LSTM model.

scholarly journals Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4592
Author(s):  
Xin Zeng ◽  
Xiaomei Zhang ◽  
Shuqun Yang ◽  
Zhicai Shi ◽  
Chihung Chi
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
User Authentication ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Edge Computing ◽  
Behavior Modeling ◽  
Security And Privacy ◽  
Positive Rate ◽  
Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

Android for Enterprise Automated Systems

2016 ◽  
pp. 19-42
Author(s):  
Fahmi Ncibi ◽  
Habib Hamam ◽  
Ezzedine Ben Braiek
Keyword(s):  
Operating System ◽  
Mobile Devices ◽  
Operating Systems ◽  
Enterprise Management ◽  
Automated Systems ◽  
Industrial Control ◽  
Daily Lives ◽  
Android Os ◽  
Android Applications

In this chapter, various aspects pertaining to the open operating system Android OS such as its history, architecture, features, and utility for business purposes will be introduced, following which the role of Android in enterprise management will be explained. The chapter will be concluded by a detailed report of the BYOD approach that uses Android for industrial control and automation. Since mobile devices have become progressively more powerful and accessible, mobile computing has greatly changed our daily lives. As one of the most popular mobile operating systems, Android provides the tools and API for Android developers to develop Android applications. Android is an open source operating system for mobile devices. Today its primary use is lodged in the mobile phone industry. During the recent past years, many projects have been created, with the objective to elevate Android to other platforms, such as sub-notebooks or embedded systems.

scholarly journals Classifying Barako coffee leaf diseases using deep convolutional models

2020 ◽  
Vol 6 (2) ◽  
pp. 197
Author(s):  
Francis Jesmar Perez Montalbo ◽  
Alexander Arsenio Hernandez
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
True Positive Rate ◽  
Fine Tuning ◽  
Diagnostic Process ◽  
True Positive ◽  
Leaf Spots ◽  
Coffee Leaf Rust ◽  
Positive Rate ◽  
Classification Tasks

This work presents the application of recent Deep Convolutional Models (DCM) to classify Barako leaf diseases. Several selected DCMs performed image classification tasks using Transfer Learning and Fine-Tuning, together with data preprocessing and augmentation. The collected dataset used totals to 4,667. Each labeled into four different classes, which included Coffee Leaf Rust (CLR), Cercospora Leaf Spots (CLS), Sooty Molds (SM), and Healthy Leaves (HL). The DCMs were trained using the partial 4,023 images and validated with the remaining 644. The classification results of the trained models VGG16, Xception, and ResNetV2-152 attained overall accuracies of 97%, 95%, and 91%, respectively. By comparing in terms of True Positive Rate (TPR), we found that Xception has the highest number of correct classifications of CLR, VGG16 with SM, and CLS, while ResNetV2-152 with the lowest TPR for CLR. The evaluated results indicate that the use of Deep Convolutional Models with an adequate amount of data, proper fine-tuning, preprocessing, and transfer learning can yield efficient classifiers for identifying several Barako leaf diseases. This work primarily contributes to the growing field of deep learning, specifically for helping farmers improve their diagnostic process by providing a solution that can automatically classify Barako leaf diseases.

Android for Enterprise Automated Systems

2018 ◽  
pp. 468-491
Author(s):  
Fahmi Ncibi ◽  
Habib Hamam ◽  
Ezzedine Ben Braiek
Keyword(s):  
Operating System ◽  
Mobile Devices ◽  
Operating Systems ◽  
Enterprise Management ◽  
Automated Systems ◽  
Industrial Control ◽  
Daily Lives ◽  
Android Os ◽  
Android Applications

In this chapter, various aspects pertaining to the open operating system Android OS such as its history, architecture, features, and utility for business purposes will be introduced, following which the role of Android in enterprise management will be explained. The chapter will be concluded by a detailed report of the BYOD approach that uses Android for industrial control and automation. Since mobile devices have become progressively more powerful and accessible, mobile computing has greatly changed our daily lives. As one of the most popular mobile operating systems, Android provides the tools and API for Android developers to develop Android applications. Android is an open source operating system for mobile devices. Today its primary use is lodged in the mobile phone industry. During the recent past years, many projects have been created, with the objective to elevate Android to other platforms, such as sub-notebooks or embedded systems.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 166
Author(s):  
Jakub T. Wilk ◽  
Beata Bąk ◽  
Piotr Artiemjew ◽  
Jerzy Wilde ◽  
Maciej Siuda
Keyword(s):  
Gas Sensors ◽  
Laboratory Tests ◽  
Test Chamber ◽  
Ambient Air ◽  
True Positive Rate ◽  
True Positive ◽  
Positive Rate ◽  
Parameters Of Accuracy ◽  
Selection Of

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

The Heartbeat Classification Based on PCA

2012 ◽  
Vol 195-196 ◽  
pp. 402-406
Author(s):  
Xue Qin Chen ◽  
Rui Ping Wang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Task ◽  
Principal Component ◽  
True Positive Rate ◽  
True Positive ◽  
Heartbeat Classification ◽  
Positive Rate ◽  
Electrocardiogram Ecg ◽  
Pattern Recognition Task

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

scholarly journals The influence of digital PET/CT on diagnostic certainty and interrater reliability in [68Ga]Ga-PSMA-11 PET/CT for recurrent prostate cancer

2021 ◽  
Author(s):  
Ian Alberts ◽  
Jan-Niklas Hünermund ◽  
Christos Sachpekidis ◽  
Clemens Mingels ◽  
Viktor Fech ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Interrater Reliability ◽  
True Positive Rate ◽  
True Positive ◽  
Recurrent Prostate Cancer ◽  
Pathological Lesions ◽  
Diagnostic Certainty ◽  
Pet Ct ◽  
Positive Rate ◽  
Digital Pet

Abstract Objective To investigate the impact of digital PET/CT on diagnostic certainty, patient-based sensitivity and interrater reliability. Methods Four physicians retrospectively evaluated two matched cohorts of patients undergoing [68Ga]Ga-PSMA-11 PET/CT on a digital (dPET/CT n = 65) or an analogue scanner (aPET/CT n = 65) for recurrent prostate cancer between 11/2018 and 03/2019. The number of equivocal and pathological lesions as well as the frequency of discrepant findings and the interrater reliability for the two scanners were compared. Results dPET/CT detected more lesions than aPET/CT (p < 0.001). A higher number of pathological scans were observed for dPET/CT (83% vs. 57%, p < 0.001). The true-positive rate at follow-up was 100% for dPET/CT compared to 84% for aPET/CT (p < 0.001). The proportion of lesions rated as non-pathological as a total of all PSMA-avid lesions detected for dPET/CT was comparable to aPET/CT (61.8% vs. 57.0%, p = 0.99). Neither a higher rate of diagnostically uncertain lesions (11.5% dPET/CT vs. 13.7% aPET/CT, p = 0.95) nor discrepant scans (where one or more readers differed in opinion as to whether the scan is pathological) were observed (18% dPET/CT vs. 17% aPET/CT, p = 0.76). Interrater reliability for pathological lesions was excellent for both scanner types (Cronbach’s α = 0.923 dPET/CT; α = 0.948 aPET/CT) and interrater agreement was substantial for dPET/CT (Krippendorf’s α = 0.701) and almost perfect in aPET/CT (α = 0.802). Conclusions A higher detection rate for pathological lesions for dPET/CT compared with aPET/CT in multiple readers was observed. This improved sensitivity was coupled with an improved true-positive rate and was not associated with increased diagnostic uncertainty, rate of non-specific lesions, or reduced interrater reliability. Key Points • New generation digital scanners detect more cancer lesions in men with prostate cancer. • When using digital scanners, the doctors are able to diagnose prostate cancer lesions with better certainty • When using digital scanners, the doctors do not disagree with each other more than with other scanner types.

scholarly journals Face Recognition Using the SR-CNN Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4237 ◽  
Author(s):  
Yu-Xin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen ◽  
Guan-Qun Sheng ◽  
...  
Keyword(s):  
Face Recognition ◽  
Target Position ◽  
Operation Time ◽  
True Positive Rate ◽  
Processing Unit ◽  
True Positive ◽  
Face Database ◽  
Acceleration Ratio ◽  
Computational Performance ◽  
Positive Rate

In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

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