scholarly journals Deep Learning Approach to DGA Classification for Effective Cyber Security

2021 ◽  
Vol 2 (4) ◽  
pp. 203-213
Author(s):  
Karunakaran P
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Cyber Security ◽  
Malicious Code ◽  
Domain Name ◽  
Ip Address ◽  
Knowledge Based ◽  
Proposed Model ◽  
Label Prediction ◽  
Text Features

In recent years, invaders are increasing rapidly in an internet world. Generally, in order to detect the anonymous attackers algorithm needs more number of features. Many algorithms fail in the efficiency of detection malicious code. Immediately this codes will not infect the system; it will attack server after communicate later. Our research focuses on analyzing the traffic of botnets for the domain name determination to the IP address of the server. This botnet creates the domain name differently. Many domains are generated by attackers and create the huge Domain Name System (DNS) traffic. In this research paper, uses both public and real time environments datasets to detect the text features as well as knowledge based feature extraction. The classifying of Domain Generation Algorithm (DGA) generated malicious domains randomly making the efficiency down in many algorithms which were used preprocessing without proper feature extraction. Effectively, our proposed algorithm is used to detect DGA which generates malicious domains randomly. This effective detection of our proposed algorithm performs with text based label prediction and additional features for extraction to improve the efficiency of the model. Our proposed model achieved 94.9% accuracy for DGA classification with help of additional feature extraction and knowledge based extraction in the deep learning architecture.

scholarly journals Effective DGA-Domain Detection and Classification with TextCNN and Additional Features

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1070
Author(s):  
Chanwoong Hwang ◽  
Hyosik Kim ◽  
Hooki Lee ◽  
Taejin Lee
Keyword(s):  
Security Policy ◽  
Domain Knowledge ◽  
Malicious Code ◽  
Ip Address ◽  
Advanced Persistent Threat ◽  
Knowledge Based ◽  
Proposed Model ◽  
Label Prediction ◽  
Additional Domain ◽  
And Control

Malicious codes, such as advanced persistent threat (APT) attacks, do not operate immediately after infecting the system, but after receiving commands from the attacker’s command and control (C&C) server. The system infected by the malicious code tries to communicate with the C&C server through the IP address or domain address of the C&C server. If the IP address or domain address is hard-coded inside the malicious code, it can analyze the malicious code to obtain the address and block access to the C&C server through security policy. In order to circumvent this address blocking technique, domain generation algorithms are included in the malware to dynamically generate domain addresses. The domain generation algorithm (DGA) generates domains randomly, so it is very difficult to identify and block malicious domains. Therefore, this paper effectively detects and classifies unknown DGA domains. We extract features that are effective for TextCNN-based label prediction, and add additional domain knowledge-based features to improve our model for detecting and classifying DGA-generated malicious domains. The proposed model achieved 99.19% accuracy for DGA classification and 88.77% accuracy for DGA class classification. We expect that the proposed model can be applied to effectively detect and block DGA-generated domains.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

scholarly journals Content Noise Detection Model Using Deep Learning in Web Forums

2020 ◽  
Vol 12 (12) ◽  
pp. 5074
Author(s):  
Jiyoung Woo ◽  
Jaeseok Yun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Detection Model ◽  
Proposed Model ◽  
Web Forum ◽  
Web Forums ◽  
Conventional Machine ◽  
Text Features ◽  
Deep Learning Model

Spam posts in web forum discussions cause user inconvenience and lower the value of the web forum as an open source of user opinion. In this regard, as the importance of a web post is evaluated in terms of the number of involved authors, noise distorts the analysis results by adding unnecessary data to the opinion analysis. Here, in this work, an automatic detection model for spam posts in web forums using both conventional machine learning and deep learning is proposed. To automatically differentiate between normal posts and spam, evaluators were asked to recognize spam posts in advance. To construct the machine learning-based model, text features from posted content using text mining techniques from the perspective of linguistics were extracted, and supervised learning was performed to distinguish content noise from normal posts. For the deep learning model, raw text including and excluding special characters was utilized. A comparison analysis on deep neural networks using the two different recurrent neural network (RNN) models of the simple RNN and long short-term memory (LSTM) network was also performed. Furthermore, the proposed model was applied to two web forums. The experimental results indicate that the deep learning model affords significant improvements over the accuracy of conventional machine learning associated with text features. The accuracy of the proposed model using LSTM reaches 98.56%, and the precision and recall of the noise class reach 99% and 99.53%, respectively.

scholarly journals Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3608
Author(s):  
Chiao-Sheng Wang ◽  
I-Hsi Kao ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Support Vector ◽  
One Dimensional ◽  
Proposed Model ◽  
Wide Range ◽  
Fault State

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

scholarly journals Data and knowledge-driven named entity recognition for cyber security

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Chen Gao ◽  
Xuan Zhang ◽  
Hui Liu
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Data Driven ◽  
Named Entity ◽  
Text Features ◽  
Complex Words ◽  
Deep Learning Model ◽  
Better Than

AbstractNamed Entity Recognition (NER) for cyber security aims to identify and classify cyber security terms from a large number of heterogeneous multisource cyber security texts. In the field of machine learning, deep neural networks automatically learn text features from a large number of datasets, but this data-driven method usually lacks the ability to deal with rare entities. Gasmi et al. proposed a deep learning method for named entity recognition in the field of cyber security, and achieved good results, reaching an F1 value of 82.8%. But it is difficult to accurately identify rare entities and complex words in the text.To cope with this challenge, this paper proposes a new model that combines data-driven deep learning methods with knowledge-driven dictionary methods to build dictionary features to assist in rare entity recognition. In addition, based on the data-driven deep learning model, an attention mechanism is adopted to enrich the local features of the text, better models the context, and improves the recognition effect of complex entities. Experimental results show that our method is better than the baseline model. Our model is more effective in identifying cyber security entities. The Precision, Recall and F1 value reached 90.19%, 86.60% and 88.36% respectively.

scholarly journals DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

2021 ◽  
Author(s):  
Santhadevi D ◽  
B
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Internet Of Things ◽  
Cyber Security ◽  
Malware Detection ◽  
Manufacturing Industries ◽  
Apache Spark ◽  
Learning Framework ◽  
Proposed Model ◽  
Confidential Data

Abstract Internet of Things (IoT) technology has a dynamic atmosphere due to incorporating multiple smart peripherals, which provide autonomous homes, cities, manufacturing industries, medical domain, etc.; however, a threat by cyber security is still at constant risk, and it gets much attention in researches. Cyber issues in the IoT environment are usually coming due to intruder’s malware activity. This kind of malware affects the confidential data of users in the IoT environment. In this research, a novel framework is implemented with the association of an improved deep LSTM with Harris Hawk Optimization (DLSTM-HHO). This framework is highly improved by adopting an Apache Spark technique for pre-processing IoT dataset. An Apache Spark replaces the traditional data pre-processing, which provides more efficiency to this model for detecting the malware at the edge of the IoT environment. The implementation of this framework is done in the MATLAB2020a platform with Apache Spark. The proposed model provides better performance evaluation in terms of accuracy is at 98%, and the F1-Score at 98.5%.

Abstract 15042: Heart Rhythm Classification From an Optimal Lead Subset of the 12-lead Electrocardiogram by Deep Learning

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Changxin Lai ◽  
Shijie Zhou ◽  
Natalia Trayanova
Keyword(s):  
Decision Making ◽  
Feature Extraction ◽  
Deep Learning ◽  
Heart Rhythm ◽  
Significant Loss ◽  
Optimal Subset ◽  
Proposed Model ◽  
Validation Set ◽  
Ecg Leads ◽  
Electrocardiogram Ecg

Introduction: Deep learning (DL) has achieved promising performance on common heart rhythms classification using 12-lead electrocardiogram (ECG). However, two major concerns hinder the DL’s application - lack of interpretability and overfitting caused by using the full 12-lead ECG as input. Objective: We proposed a hybrid DL model with enhanced interpretability to detect 9 common types of heart rhythms from an optimal ECG lead subset, and to quantitively analyze the overfitting. Methods: We used a multicenter dataset of 6,877 annotated 12-lead ECG recordings. The proposed model (Fig. 1A) consists of a feature extraction and a decision-making. The feature extraction used 12 separate neural networks to extract features from each lead. The features were then fed into a random-forest classifier in the decision-making step to classify heart-rhythm types. The classifier was used to interpret the correlations between the heart rhythms and the ECG leads, to find an optimal subset of ECG leads, and to analyze whether using 12-lead ECG added unnecessary complexity to the model and undermined its generalizability. Results: The proposed model detected the correlations between the heart-rhythm types and the ECG leads (Fig. 1B), and identified an optimal ECG lead subset (leads II, aVR, V1, V4). The optimal subset was, in comparison with using 12-lead ECG, significantly better (F1 =0.776 vs. F1 = 0.767, P=0.02) on the validation set for classifying the 9 common heart rhythms. There was no statistical difference on the test set. No overfitting caused by 12-lead ECG was detected in this study. Conclusion: The hybrid DL model based on an optimal 4-lead ECG can interpret rhythm types without significant loss of accuracy in comparison with the 12-lead ECG.

Developing Brain Tumor Detection Model Using Deep Feature Extraction via Transfer Learning

2021 ◽  
pp. 119-137
Author(s):  
Adem Assfaw Mekonnen ◽  
Hussien Worku Seid ◽  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Transfer Learning ◽  
Intensity Variation ◽  
Machine Learning Algorithms ◽  
Proposed Model ◽  
Deep Feature Extraction

The timely prognosis of brain tumors is gambling a great role within the pretreatment of patients and keep the life of suffers. The manual classification of brain tumors is a difficult task for radiologists due to the intensity variation pixel information produced by the magnetic resonance machine and it is a very tedious task for a large number of images. A deep learning algorithm becomes a famous algorithm to conquer the problems traditional machine learning algorithms by automatically feature extraction from the input spaces and accurately detect the brain tumors. One of the most important features of deep learning is transferred a gain knowledge strategy to use small datasets. Transfer learning is explored by freezing layers and fine-tuning a pre-trained model to a recommended convolutional neural net model. The proposed model is trained using 4000 real magnetic resonance images datasets. The mean accuracy of the proposed model is found to be 98% for brain tumor classifications with mini-batch size 32 and a learning rate of 0.001.

scholarly journals A Research on Deep Learning Advance for Landslide Classification using Convolutional Neural Networks

2019 ◽  
Vol 8 (6S4) ◽  
pp. 903-906
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Proposed Model

Landslides can easily be tragic to human life and property. Increase in the rate of human settlement in the mountains has resulted in safety concerns. Landslides have caused economic loss between 1-2% of the GDP in many developing countries. In this study, we discuss a deep learning approach to detect landslides. Convolutional Neural Networks are used for feature extraction for our proposed model. As there was no source of an exact and precise data set for feature extraction, therefore, a new data set was built for testing the model. We have tested and compared this work with our proposed model and with other machine-learning algorithms such as Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors and Support Vector Machine. Our proposed deep learning model produces a classification accuracy of 96.90% outperforming the classical machine-learning algorithms.

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