Secure Distributed Mobile Volunteer Computing with Android

2022 ◽  
Vol 22 (1) ◽  
pp. 1-21
Author(s):  
Iram Bibi ◽  
Adnan Akhunzada ◽  
Jahanzaib Malik ◽  
Muhammad Khurram Khan ◽  
Muhammad Dawood
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Comprehensive Evaluation ◽  
Smart Devices ◽  
Volunteer Computing ◽  
Detection Accuracy ◽  
Distributed Computing Systems ◽  
Android Malware ◽  
Rapid Deployment ◽  
Computing Infrastructure

Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.

scholarly journals Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1186
Author(s):  
Ranjana Koshy ◽  
Ausif Mahmood
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Anisotropic Diffusion ◽  
Short Term Memory ◽  
Total Error ◽  
Detection Accuracy ◽  
Test Accuracy ◽  
Replay Attack ◽  
Liveness Detection

Face liveness detection is a critical preprocessing step in face recognition for avoiding face spoofing attacks, where an impostor can impersonate a valid user for authentication. While considerable research has been recently done in improving the accuracy of face liveness detection, the best current approaches use a two-step process of first applying non-linear anisotropic diffusion to the incoming image and then using a deep network for final liveness decision. Such an approach is not viable for real-time face liveness detection. We develop two end-to-end real-time solutions where nonlinear anisotropic diffusion based on an additive operator splitting scheme is first applied to an incoming static image, which enhances the edges and surface texture, and preserves the boundary locations in the real image. The diffused image is then forwarded to a pre-trained Specialized Convolutional Neural Network (SCNN) and the Inception network version 4, which identify the complex and deep features for face liveness classification. We evaluate the performance of our integrated approach using the SCNN and Inception v4 on the Replay-Attack dataset and Replay-Mobile dataset. The entire architecture is created in such a manner that, once trained, the face liveness detection can be accomplished in real-time. We achieve promising results of 96.03% and 96.21% face liveness detection accuracy with the SCNN, and 94.77% and 95.53% accuracy with the Inception v4, on the Replay-Attack, and Replay-Mobile datasets, respectively. We also develop a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to classify the video sequence as real or fake. Even though the use of CNN followed by LSTM is not new, combining it with diffusion (that has proven to be the best approach for single image liveness detection) is novel. Performance evaluation of our architecture on the REPLAY-ATTACK dataset gave 98.71% test accuracy and 2.77% Half Total Error Rate (HTER), and on the REPLAY-MOBILE dataset gave 95.41% accuracy and 5.28% HTER.

scholarly journals Calibrating Network Traffic with One-Dimensional Convolutional Neural Network with Autoencoder and Independent Recurrent Neural Network for Mobile Malware Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Songjie Wei ◽  
Zedong Zhang ◽  
Shasha Li ◽  
Pengfei Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Traffic ◽  
Malware Detection ◽  
Smart Devices ◽  
Detection Accuracy ◽  
One Dimensional ◽  
Mobile Malware ◽  
Mobile Malware Detection

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.

scholarly journals Intent Detection-Based Lithuanian Chatbot Created via Automatic DNN Hyper-Parameter Optimization

2020 ◽  
Author(s):  
Jurgita Kapočiūtė-Dzikienė
Keyword(s):  
Neural Network ◽  
Error Analysis ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Parameter Optimization ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Detection Accuracy ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we tackle an intent detection problem for the Lithuanian language with the real supervised data. Our main focus is on the enhancement of the Natural Language Understanding (NLU) module, responsible for the comprehension of user’s questions. The NLU model is trained with a properly selected word vectorization type and Deep Neural Network (DNN) classifier. During our experiments, we have experimentally investigated fastText and BERT embeddings. Besides, we have automatically optimized different architectures and hyper-parameters of the following DNN approaches: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Convolutional Neural Network (CNN). The highest accuracy=∼0.715 (∼0.675 and ∼0.625 over random and majority baselines, respectively) was achieved with the CNN classifier applied on a top of BERT embeddings. The detailed error analysis revealed that prediction accuracies degrade for the least covered intents and due to intent ambiguities; therefore, in the future, we are planning to make necessary adjustments to boost the intent detection accuracy for the Lithuanian language even more.

scholarly journals Hunting Android Malware Using Multimodal Deep Learning and Hybrid Analysis Data

2021 ◽  
Author(s):  
Angelo Schranko Oliveira ◽  
Renato José Sassi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Dynamic Properties ◽  
Malware Detection ◽  
Analysis Data ◽  
Knowledge Discovery In Databases ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection

In this work, we propose a new multimodal Deep Learning (DL) Android malware detection method, Chimera, that combines both manual and automatic feature engineering by using the DL architectures, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Transformer Networks (TN) to perform feature learning from raw data (Dalvik Executables (DEX)), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences) respectively. To train and evaluate our model, we implemented the Knowledge Discovery in Databases (KDD) process and used the publicly available Android benchmark dataset Omnidroid. By leveraging a hybrid source of information to learn high-level feature representations for both the static and dynamic properties of Android applications, Chimera’s detection Accuracy, Precision, and Recall outperform classical Machine Learning (ML) algorithms, state-of-the-art Ensemble, and Voting Ensembles ML methods, as well as unimodal DL methods using CNNs, DNNs, TNs, and Long-Short Term Memory Networks (LSTM). To the best of our knowledge, this is the first work that successfully applies multimodal DL to combine those three different modalities of data using DNNs, CNNs, and TNs to learn a shared representation that can be used in Android malware detection tasks.

scholarly journals A Hybrid Modeling of Mobile App Dynamics on Serial Causality for Malware Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Meilin Liu ◽  
Songjie Wei ◽  
Pengfei Jiang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Network Flows ◽  
Network Reliability ◽  
Feature Learning ◽  
Original Data ◽  
Mobile App ◽  
Android Malware

The popularity of smart phones has brought significant convenience to people’s lives, but also there are many security problems. In recent years, malicious applications are increasingly rampant, which threaten users and society as security challenges to network reliability and management. However, due to neglecting the sequential features between network flows, existing malicious application recognition methods based on network traffic analysis have low recognition accuracy. Based on the network traffic characteristics of Android applications, this paper firstly applies Long Short-Term Memory network-based variational Auto-Encoder to extract the sequential feature of the application running time. Then, we design the BP neural network for initial classification and connect the class vector output of the BP neural network with the original data. The output is fed into the cascade forest for further feature learning and classification. The integrated methods are easy to implement with data independency and efficiency. We conduct experiments to evaluate the proposed with Android malware dataset CICAndMal2017, with a 97.29% high accuracy, comparatively significant precision and recall rates when benchmarked against other methods.

scholarly journals Android Malware Detection Using TCN with Bytecode Image

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1107
Author(s):  
Wenhui Zhang ◽  
Nurbol Luktarhan ◽  
Chao Ding ◽  
Bei Lu
Keyword(s):  
Neural Network ◽  
Malware Detection ◽  
Texture Features ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Image Size ◽  
Android Malware ◽  
Detection Model ◽  
Android Malware Detection ◽  
First Time

With the rapid increase in the number of Android malware, the image-based analysis method has become an effective way to defend against symmetric encryption and confusing malware. At present, the existing Android malware bytecode image detection method, based on a convolution neural network (CNN), relies on a single DEX file feature and requires a large amount of computation. To solve these problems, we combine the visual features of the XML file with the data section of the DEX file for the first time, and propose a new Android malware detection model, based on a temporal convolution network (TCN). First, four gray-scale image datasets with four different combinations of texture features are created by combining XML files and DEX files. Then the image size is unified and input to the designed neural network with three different convolution methods for experimental validation. The experimental results show that adding XML files is beneficial for Android malware detection. The detection accuracy of the TCN model is 95.44%, precision is 95.45%, recall rate is 95.45%, and F1-Score is 95.44%. Compared with other methods based on the traditional CNN model or lightweight MobileNetV2 model, the method proposed in this paper, based on the TCN model, can effectively utilize bytecode image sequence features, improve the accuracy of detecting Android malware and reduce its computation.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

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