scholarly journals A Client/Server Malware Detection Model Based on Machine Learning for Android Devices

IoT ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 355-374
Author(s):  
Arthur Fournier ◽  
Franjieh El Khoury ◽  
Samuel Pierre
Keyword(s):  
Mobile Device ◽  
Private Information ◽  
Malware Detection ◽  
Computation Time ◽  
Stochastic Gradient Descent ◽  
Client Server ◽  
Detection Model ◽  
Proposed Model ◽  
Mobile Phone Security

The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for malware detection on Android devices to protect private and financial information, for the mobile applications of the ATISCOM project. This model is based on client/server architecture, to reduce the heavy computations on a mobile device by sending data from the mobile device to the server for remote processing (i.e., offloading) of the predictions. We then gradually optimized our proposed model for better classification of the newly installed applications on Android devices. We at first adopted Naive Bayes to build the model with 92.4486% accuracy, then the classification method that gave the best accuracy of 93.85% for stochastic gradient descent (SGD) with binary class (i.e., malware and benign), and finally the regression method with numerical values ranging from −100 to 100 to manage the uncertainty predictions. Therefore, our proposed model with random forest regression gives a good accuracy in terms of performance, with a good correlation coefficient, minimum computation time and the smallest number of errors for malware detection.

scholarly journals Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Yiming Jia ◽  
Xiaodong Tian ◽  
Zibang Zhang
Keyword(s):  
Transfer Learning ◽  
Adaptive Learning ◽  
Deep Level ◽  
Image Resolution ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Natural Image ◽  
Historical Period ◽  
Proposed Model

AbstractIt is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2444
Author(s):  
Mazhar Javed Awan ◽  
Osama Ahmed Masood ◽  
Mazin Abed Mohammed ◽  
Awais Yasin ◽  
Azlan Mohd Zain ◽  
...  
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Malware Detection ◽  
Detection Methods ◽  
Learning Framework ◽  
Malware Classification ◽  
Proposed Model ◽  
Communication Devices ◽  
Digital Assets

In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

scholarly journals An Ensemble-Based Malware Detection Model Using Minimum Feature Set

MENDEL ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Zelinka ◽  
Eslam Amer
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model ◽  
Learning Techniques ◽  
Proposed Model ◽  
Positive Rate ◽  
Minimum Number

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Seema Rani ◽  
Avadhesh Kumar ◽  
Naresh Kumar
Keyword(s):  
Question Answering ◽  
Neural Model ◽  
Manhattan Distance ◽  
Semantic Matching ◽  
English Only ◽  
Detection Model ◽  
Proposed Model ◽  
Reported Study ◽  
Social Media Platforms ◽  
Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 708
Author(s):  
Wenbo Liu ◽  
Fei Yan ◽  
Jiyong Zhang ◽  
Tao Deng
Keyword(s):  
Information Exchange ◽  
Information Transfer ◽  
Spatial Information ◽  
Contextual Information ◽  
Great Influence ◽  
Light Condition ◽  
Lane Detection ◽  
Detection Model ◽  
Spatial Features ◽  
Proposed Model

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model

2014 ◽  
Vol 643 ◽  
pp. 99-104
Author(s):  
Jin Yang ◽  
Yun Jie Li ◽  
Qin Li
Keyword(s):  
Situation Awareness ◽  
Artificial Immune Systems ◽  
Epidemic Spreading ◽  
State Spaces ◽  
Real Time Processing ◽  
Network Intrusion ◽  
Markov Chain Theory ◽  
Proposed Model ◽  
Network Risk

In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

A Systematized FMEA Practice for Magnet Relays

2010 ◽  
Vol 146-147 ◽  
pp. 757-769
Author(s):  
Ching Ming Cheng ◽  
Wen Fang Wu ◽  
Yao Hsu
Keyword(s):  
New Product Development ◽  
Failure Modes ◽  
New Product ◽  
Risk Priority Number ◽  
Detection Model ◽  
Regulation Function ◽  
Potential Failure ◽  
Weighting Rule ◽  
Rating Criterion

The Design Failure Modes and Effects Analysis (DFMEA) are generally applied to risk management of New Product Development (NPD) through standardization of potential failure modes and effect-ranking of rating criterion with failure modes. Typical 1 to 10 of effect-ranking are widely weighed the priority of classification, that framing effects and status quo senses might cause decision trap happening thus. The FMEA follows considerable indexes which are including Severity, Occurrence and Detection, and need be associated with difference between every two failures individually. However, we suspect that a more systematic construction of the analysis by which failure modes belong is necessary in order to make intellectual progress in this area. Two ways of such differentiation and construction are improvable effect-ranking and systematized indexes; here we resolve for attributes of failures with classification, maturity and experiance of indexes according to an existing rule. In Severity model, the larger differentiation is achieved by separating indexes to the classification of the Law & Regulation, Function and Cosmetic. Occurrence model has its characteristic a reliable ranking indexwhich assists decisionmakers to manage their venture. This is the model most closely associate with product maturity by grouping indexes to the new, extend and series product. Detection model offers a special perspective on cost; here the connections concerned with phase occasion of the review, verification and validation. Such differentiations will be proposed and mapped with the Life Cycle Profile (LCP) to systematize FMEA. Meanwhile, a more reasonable Risk Priority Number (RPN) with the new weighting rule will be worked out for effect-ranking and management system will be integrated systematiclly

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